• using R version 4.5.2 Patched (2026-01-31 r89382)
• using platform: aarch64-apple-darwin20
• R was compiled by     Apple clang version 16.0.0 (clang-1600.0.26.6)     GNU Fortran (GCC) 14.2.0
• running under: macOS Ventura 13.7.8
• using session charset: UTF-8
• checking for file ‘ackwards/DESCRIPTION’ ... OK
• this is package ‘ackwards’ version ‘0.1.1’
• package encoding: UTF-8
• checking package namespace information ... OK
• checking package dependencies ... OK
• checking if this is a source package ... OK
• checking if there is a namespace ... OK
• checking for executable files ... OK
• checking for hidden files and directories ... OK
• checking for portable file names ... OK
• checking for sufficient/correct file permissions ... OK
• checking whether package ‘ackwards’ can be installed ... [2s/2s] OK See the install log for details.
• checking installed package size ... OK
• checking package directory ... OK
• checking ‘build’ directory ... OK
• checking DESCRIPTION meta-information ... OK
• checking top-level files ... OK
• checking for left-over files ... OK
• checking index information ... OK
• checking package subdirectories ... OK
• checking code files for non-ASCII characters ... OK
• checking R files for syntax errors ... OK
• checking whether the package can be loaded ... [0s/0s] OK
• checking whether the package can be loaded with stated dependencies ... [0s/0s] OK
• checking whether the package can be unloaded cleanly ... [0s/0s] OK
• checking whether the namespace can be loaded with stated dependencies ... [0s/0s] OK
• checking whether the namespace can be unloaded cleanly ... [0s/0s] OK
• checking loading without being on the library search path ... [0s/0s] OK
• checking dependencies in R code ... OK
• checking S3 generic/method consistency ... OK
• checking replacement functions ... OK
• checking foreign function calls ... OK
• checking R code for possible problems ... [2s/2s] OK
• checking Rd files ... [0s/0s] OK
• checking Rd metadata ... OK
• checking Rd cross-references ... OK
• checking for missing documentation entries ... OK
• checking for code/documentation mismatches ... OK
• checking Rd \usage sections ... OK
• checking Rd contents ... OK
• checking for unstated dependencies in examples ... OK
• checking contents of ‘data’ directory ... OK
• checking data for non-ASCII characters ... [0s/0s] OK
• checking LazyData ... OK
• checking data for ASCII and uncompressed saves ... OK
• checking installed files from ‘inst/doc’ ... OK
• checking files in ‘vignettes’ ... OK
• checking examples ... [2s/2s] OK
• checking for unstated dependencies in ‘tests’ ... OK
• checking tests ... [111s/47s] ERROR   Running ‘testthat.R’ [111s/47s] Running the tests in ‘tests/testthat.R’ failed. Complete output:   > library(testthat)   > library(ackwards)   >   > test_check("ackwards")   Starting 2 test processes.   > test-suggest_k.R: i Running parallel analysis (5 iterations, PC + FA)...   > test-suggest_k.R: i PA-PC suggested 6 components -- above the evaluated ceiling (`k_max` = 3); reporting k <= 3.   > test-suggest_k.R: i Increase `k_max` to evaluate the full suggestion.   > test-suggest_k.R: i PA-FA suggested 6 factors -- above the evaluated ceiling (`k_max` = 3); reporting k <= 3.   > test-suggest_k.R: i Increase `k_max` to evaluate the full suggestion.   > test-suggest_k.R: v Running parallel analysis (5 iterations, PC + FA)... [90ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running MAP and VSS...   > test-suggest_k.R: CD: 364 rows with missing values removed (2436 complete cases used).   > test-suggest_k.R: v Running MAP and VSS... [54ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running Comparison Data (CD)...   > test-suggest_k.R: v Running Comparison Data (CD)... [3.1s]   > test-suggest_k.R:   > test-suggest_k.R: i Running parallel analysis (5 iterations, PC + FA)...   > test-suggest_k.R: v Running parallel analysis (5 iterations, PC + FA)... [50ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running MAP and VSS...   > test-suggest_k.R: CD: 54 rows with missing values removed (946 complete cases used).   > test-suggest_k.R: v Running MAP and VSS... [27ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running Comparison Data (CD)...   > test-suggest_k.R: v Running Comparison Data (CD)... [704ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running parallel analysis (5 iterations, PC + FA)...   > test-suggest_k.R: i PA-PC suggested 3 components -- above the evaluated ceiling (`k_max` = 2); reporting k <= 2.   > test-suggest_k.R: i Increase `k_max` to evaluate the full suggestion.   > test-suggest_k.R: i PA-FA suggested 4 factors -- above the evaluated ceiling (`k_max` = 2); reporting k <= 2.   > test-suggest_k.R: i Increase `k_max` to evaluate the full suggestion.   > test-suggest_k.R: v Running parallel analysis (5 iterations, PC + FA)... [52ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running MAP and VSS...   > test-suggest_k.R: CD: 86 rows with missing values removed (914 complete cases used).   > test-suggest_k.R: v Running MAP and VSS... [21ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running Comparison Data (CD)...   > test-suggest_k.R: v Running Comparison Data (CD)... [765ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)...   > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [37ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running MAP and VSS...   > test-suggest_k.R: CD: 54 rows with missing values removed (946 complete cases used).   > test-suggest_k.R: v Running MAP and VSS... [28ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running Comparison Data (CD)...   > test-suggest_k.R: v Running Comparison Data (CD)... [481ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)...   > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [28ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running MAP and VSS...   > test-suggest_k.R: CD: 54 rows with missing values removed (946 complete cases used).   > test-suggest_k.R: v Running MAP and VSS... [29ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running Comparison Data (CD)...   > test-suggest_k.R: v Running Comparison Data (CD)... [432ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running MAP and VSS...   > test-suggest_k.R: v Running MAP and VSS... [16ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running MAP and VSS...   > test-suggest_k.R: v Running MAP and VSS... [25ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running MAP and VSS...   > test-suggest_k.R: v Running MAP and VSS... [23ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running parallel analysis (5 iterations, PC + FA)...   > test-suggest_k.R: i PA-FA suggested 4 factors -- above the evaluated ceiling (`k_max` = 2); reporting k <= 2.   > test-suggest_k.R: i Increase `k_max` to evaluate the full suggestion.   > test-suggest_k.R: v Running parallel analysis (5 iterations, PC + FA)... [69ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)...   > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [41ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running MAP and VSS...   > test-suggest_k.R: CD: 41 rows with missing values removed (959 complete cases used).   > test-suggest_k.R: v Running MAP and VSS... [32ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running Comparison Data (CD)...   > test-suggest_k.R: v Running Comparison Data (CD)... [199ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)...   > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [35ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running MAP and VSS...   > test-suggest_k.R: i CD requires EFAtools (install to enable).   > test-suggest_k.R: v Running MAP and VSS... [29ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)...   > test-suggest_k.R: In smc, smcs < 0 were set to .0   > test-suggest_k.R: In smc, smcs < 0 were set to .0   > test-suggest_k.R: In smc, smcs < 0 were set to .0   > test-suggest_k.R: In factor.scores, the correlation matrix is singular, the pseudo inverse is used   > test-suggest_k.R: In smc, smcs < 0 were set to .0   > test-suggest_k.R: In smc, smcs < 0 were set to .0   > test-suggest_k.R: In smc, smcs < 0 were set to .0   > test-suggest_k.R: In smc, smcs < 0 were set to .0   > test-suggest_k.R: In factor.scores, the correlation matrix is singular, the pseudo inverse is used   > test-suggest_k.R: In smc, smcs < 0 were set to .0   > test-suggest_k.R: In smc, smcs < 0 were set to .0   > test-suggest_k.R: In factor.scores, the correlation matrix is singular, the pseudo inverse is used   > test-suggest_k.R: In factor.scores, the correlation matrix is singular, the pseudo inverse is used   > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [25ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running MAP and VSS...   > test-suggest_k.R: x Running MAP and VSS... [13ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)...   > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [29ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running MAP and VSS...   > test-suggest_k.R: CD: 64 rows with missing values removed (936 complete cases used).   > test-suggest_k.R: v Running MAP and VSS... [26ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running Comparison Data (CD)...   > test-suggest_k.R: v Running Comparison Data (CD)... [670ms]   > test-suggest_k.R:   > test-suggest_k.R:   > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------   > test-suggest_k.R: Variables: 8   > test-suggest_k.R: n: 1,000   > test-suggest_k.R: Basis: pearson   > test-suggest_k.R: Tested k: 1-4   > test-suggest_k.R:   > test-suggest_k.R: -- Criteria (k = 1-4) --   > test-suggest_k.R:   > test-suggest_k.R: k = 1: PA-PC v PA-FA v MAP 0.0427* VSS-1 0.6224 VSS-2 0.0000 CD v   > test-suggest_k.R: k = 2: PA-PC v PA-FA v MAP 0.0522 VSS-1 0.7305* VSS-2 0.7981 CD v   > test-suggest_k.R: k = 3: PA-PC - PA-FA v MAP 0.0971 VSS-1 0.6415 VSS-2 0.8407 CD v*   > test-suggest_k.R: k = 4: PA-PC - PA-FA - MAP 0.1577 VSS-1 0.6451 VSS-2 0.8510* CD -   > test-suggest_k.R:   > test-suggest_k.R: -- Recommendations --   > test-suggest_k.R:   > test-suggest_k.R: * PA-PC: k <= 2   > test-suggest_k.R: * PA-FA: k <= 3   > test-suggest_k.R: * MAP: k = 1   > test-suggest_k.R: * VSS-1: k = 2   > test-suggest_k.R: * VSS-2: k = 4   > test-suggest_k.R: * CD: k = 3   > test-suggest_k.R: Consensus range: k = 1-4   > test-suggest_k.R: --------------------------------------------------------------------------------   > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels   > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional.   > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,   > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range.   > test-suggest_k.R:   > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------   > test-suggest_k.R: Variables: 8   > test-suggest_k.R: n: 1,000   > test-suggest_k.R: Basis: pearson   > test-suggest_k.R: Tested k: 1-4   > test-suggest_k.R:   > test-suggest_k.R: -- Criteria (k = 1-4) --   > test-suggest_k.R:   > test-suggest_k.R: k = 1: PA-PC v PA-FA v MAP 0.0427* VSS-1 0.6224 VSS-2 0.0000 CD v   > test-suggest_k.R: k = 2: PA-PC v PA-FA v MAP 0.0522 VSS-1 0.7305* VSS-2 0.7981 CD v   > test-suggest_k.R: k = 3: PA-PC - PA-FA v MAP 0.0971 VSS-1 0.6415 VSS-2 0.8407 CD v*   > test-suggest_k.R: k = 4: PA-PC - PA-FA - MAP 0.1577 VSS-1 0.6451 VSS-2 0.8510* CD -   > test-suggest_k.R:   > test-suggest_k.R: -- Recommendations --   > test-suggest_k.R:   > test-suggest_k.R: * PA-PC: k <= 2   > test-suggest_k.R: * PA-FA: k <= 3   > test-suggest_k.R: * MAP: k = 1   > test-suggest_k.R: * VSS-1: k = 2   > test-suggest_k.R: * VSS-2: k = 4   > test-suggest_k.R: * CD: k = 3   > test-suggest_k.R: Consensus range: k = 1-4   > test-suggest_k.R: --------------------------------------------------------------------------------   > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels   > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional.   > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,   > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range.   > test-suggest_k.R:   > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------   > test-suggest_k.R: Variables: 8   > test-suggest_k.R: n: 1,000   > test-suggest_k.R: Basis: pearson   > test-suggest_k.R: Tested k: 1-4   > test-suggest_k.R:   > test-suggest_k.R: -- Criteria (k = 1-4) --   > test-suggest_k.R:   > test-suggest_k.R: k = 1: PA-PC v PA-FA - MAP 0.0427* VSS-1 0.6224 VSS-2 0.0000 CD v   > test-suggest_k.R: k = 2: PA-PC v PA-FA - MAP 0.0522 VSS-1 0.7305* VSS-2 0.7981 CD v   > test-suggest_k.R: k = 3: PA-PC - PA-FA - MAP 0.0971 VSS-1 0.6415 VSS-2 0.8407 CD v*   > test-suggest_k.R: k = 4: PA-PC - PA-FA - MAP 0.1577 VSS-1 0.6451 VSS-2 0.8510* CD -   > test-suggest_k.R:   > test-suggest_k.R: -- Recommendations --   > test-suggest_k.R:   > test-suggest_k.R: * PA-PC: k <= 2   > test-suggest_k.R: * PA-FA: undetermined (no FA factor exceeded random threshold)   > test-suggest_k.R: * MAP: k = 1   > test-suggest_k.R: * VSS-1: k = 2   > test-suggest_k.R: * VSS-2: k = 4   > test-suggest_k.R: * CD: k = 3   > test-suggest_k.R: Consensus range: k = 1-4   > test-suggest_k.R: --------------------------------------------------------------------------------   > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels   > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional.   > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,   > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range.   > test-suggest_k.R:   > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------   > test-suggest_k.R: Variables: 8   > test-suggest_k.R: n: 1,000   > test-suggest_k.R: Basis: pearson   > test-suggest_k.R: Tested k: 1-4   > test-suggest_k.R:   > test-suggest_k.R: -- Criteria (k = 1-4) --   > test-suggest_k.R:   > test-suggest_k.R: k = 1: PA-PC v PA-FA - MAP 0.0427* VSS-1 0.6224 VSS-2 0.0000 CD v   > test-suggest_k.R: k = 2: PA-PC v PA-FA - MAP 0.0522 VSS-1 0.7305* VSS-2 0.7981 CD v   > test-suggest_k.R: k = 3: PA-PC - PA-FA - MAP 0.0971 VSS-1 0.6415 VSS-2 0.8407 CD v*   > test-suggest_k.R: k = 4: PA-PC - PA-FA - MAP 0.1577 VSS-1 0.6451 VSS-2 0.8510* CD -   > test-suggest_k.R:   > test-suggest_k.R: -- Recommendations --   > test-suggest_k.R:   > test-suggest_k.R: * PA-PC: k <= 2   > test-suggest_k.R: * PA-FA: undetermined (no FA factor exceeded random threshold)   > test-suggest_k.R: * MAP: k = 1   > test-suggest_k.R: * VSS-1: k = 2   > test-suggest_k.R: * VSS-2: k = 4   > test-suggest_k.R: * CD: k = 3   > test-suggest_k.R: Consensus range: k = 1-4   > test-suggest_k.R: --------------------------------------------------------------------------------   > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels   > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional.   > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,   > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range.   > test-suggest_k.R: i Running parallel analysis (20 iterations, PC + FA)...   > test-suggest_k.R: v Running parallel analysis (20 iterations, PC + FA)... [133ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running MAP and VSS...   > test-suggest_k.R: CD: 125 rows with missing values removed (875 complete cases used).   > test-suggest_k.R: v Running MAP and VSS... [52ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running Comparison Data (CD)...   > test-esem.R:   > test-esem.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------   > test-esem.R: Engine: esem   > test-esem.R: Rotation: varimax   > test-esem.R: Basis: pearson   > test-esem.R: n: 200   > test-esem.R: k (max): 3   > test-esem.R:   > test-esem.R: -- Levels --   > test-esem.R:   > test-esem.R: v k = 1: 1 factor, 43.0% variance   > test-esem.R: v k = 2: 2 factors, 85.2% variance   > test-esem.R: v k = 3: 3 factors, 87.8% variance   > test-esem.R:   > test-esem.R: -- Edges --   > test-esem.R:   > test-esem.R: 3 of 8 edges have |r| >= 0.3   > test-esem.R: --------------------------------------------------------------------------------   > test-esem.R: Note: This is a series of linked solutions, not a fitted hierarchical model.   > test-esem.R: Cross-level edges are descriptive score correlations. Per-level fit indices   > test-esem.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --   > test-esem.R: they do not validate the edges or the hierarchy itself.   > test-suggest_k.R: v Running Comparison Data (CD)... [6.1s]   > test-suggest_k.R:   > test-suggest_k.R:   > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------   > test-suggest_k.R: Variables: 8   > test-suggest_k.R: n: 1,000   > test-suggest_k.R: Basis: pearson   > test-suggest_k.R: Tested k: 1-4   > test-suggest_k.R:   > test-suggest_k.R: -- Criteria (k = 1-4) --   > test-suggest_k.R:   > test-suggest_k.R: k = 1: PA-PC v PA-FA v MAP 0.0427* VSS-1 0.6224 VSS-2 0.0000   > test-suggest_k.R: k = 2: PA-PC v PA-FA v MAP 0.0522 VSS-1 0.7305* VSS-2 0.7981   > test-suggest_k.R: k = 3: PA-PC - PA-FA v MAP 0.0971 VSS-1 0.6415 VSS-2 0.8407   > test-suggest_k.R: k = 4: PA-PC - PA-FA - MAP 0.1577 VSS-1 0.6451 VSS-2 0.8510*   > test-suggest_k.R: + CD requires EFAtools (install to enable).   > test-suggest_k.R:   > test-suggest_k.R: -- Recommendations --   > test-suggest_k.R:   > test-suggest_k.R: * PA-PC: k <= 2   > test-suggest_k.R: * PA-FA: k <= 3   > test-suggest_k.R: * MAP: k = 1   > test-suggest_k.R: * VSS-1: k = 2   > test-suggest_k.R: * VSS-2: k = 4   > test-suggest_k.R: Consensus range: k = 1-4   > test-suggest_k.R: --------------------------------------------------------------------------------   > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels   > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional.   > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,   > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range.   > test-suggest_k.R:   > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------   > test-suggest_k.R: Variables: 8   > test-suggest_k.R: n: 1,000   > test-suggest_k.R: Basis: pearson   > test-suggest_k.R: Tested k: 1-4   > test-suggest_k.R:   > test-suggest_k.R: -- Criteria (k = 1-4) --   > test-suggest_k.R:   > test-suggest_k.R: k = 1: PA-PC v PA-FA v MAP 0.0427* VSS-1 0.6224 VSS-2 0.0000   > test-suggest_k.R: k = 2: PA-PC v PA-FA v MAP 0.0522 VSS-1 0.7305* VSS-2 0.7981   > test-suggest_k.R: k = 3: PA-PC - PA-FA v MAP 0.0971 VSS-1 0.6415 VSS-2 0.8407   > test-suggest_k.R: k = 4: PA-PC - PA-FA - MAP 0.1577 VSS-1 0.6451 VSS-2 0.8510*   > test-suggest_k.R: + CD requires EFAtools (install to enable).   > test-suggest_k.R:   > test-suggest_k.R: -- Recommendations --   > test-suggest_k.R:   > test-suggest_k.R: * PA-PC: k <= 2   > test-suggest_k.R: * PA-FA: k <= 3   > test-suggest_k.R: * MAP: k = 1   > test-suggest_k.R: * VSS-1: k = 2   > test-suggest_k.R: * VSS-2: k = 4   > test-suggest_k.R: Consensus range: k = 1-4   > test-suggest_k.R: --------------------------------------------------------------------------------   > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels   > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional.   > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,   > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range.   > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)...   > test-suggest_k.R: i PA-FA suggested 3 factors -- above the evaluated ceiling (`k_max` = 2); reporting k <= 2.   > test-suggest_k.R: i Increase `k_max` to evaluate the full suggestion.   > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [71ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running MAP and VSS...   > test-suggest_k.R: CD: 41 rows with missing values removed (959 complete cases used).   > test-suggest_k.R: v Running MAP and VSS... [34ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running Comparison Data (CD)...   > test-suggest_k.R: v Running Comparison Data (CD)... [199ms]   > test-suggest_k.R:   > test-suggest_k.R:   > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------   > test-suggest_k.R: Variables: 8   > test-suggest_k.R: n: 1,000   > test-suggest_k.R: Basis: pearson   > test-suggest_k.R: Tested k: 1-4   > test-suggest_k.R:   > test-suggest_k.R: -- Criteria (k = 1-4) --   > test-suggest_k.R:   > test-suggest_k.R: k = 1: PA-PC v PA-FA v MAP 0.0427* VSS-1 0.6224 VSS-2 0.0000 CD v*   > test-suggest_k.R: k = 2: PA-PC v PA-FA v MAP 0.0522 VSS-1 0.7305* VSS-2 0.7981 CD -   > test-suggest_k.R: k = 3: PA-PC - PA-FA v MAP 0.0971 VSS-1 0.6415 VSS-2 0.8407 CD -   > test-suggest_k.R: k = 4: PA-PC - PA-FA - MAP 0.1577 VSS-1 0.6451 VSS-2 0.8510* CD -   > test-suggest_k.R:   > test-suggest_k.R: -- Recommendations --   > test-suggest_k.R:   > test-suggest_k.R: * PA-PC: k <= 2   > test-suggest_k.R: * PA-FA: k <= 3   > test-suggest_k.R: * MAP: k = 1   > test-suggest_k.R: * VSS-1: k = 2   > test-suggest_k.R: * VSS-2: k = 4   > test-suggest_k.R: * CD: k = 1   > test-suggest_k.R: Consensus range: k = 1-4   > test-suggest_k.R: --------------------------------------------------------------------------------   > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels   > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional.   > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,   > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range.   > test-suggest_k.R:   > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------   > test-suggest_k.R: Variables: 8   > test-suggest_k.R: n: 1,000   > test-suggest_k.R: Basis: pearson   > test-suggest_k.R: Tested k: 1-4   > test-suggest_k.R:   > test-suggest_k.R: -- Criteria (k = 1-4) --   > test-suggest_k.R:   > test-suggest_k.R: k = 1: PA-PC v PA-FA v MAP 0.0427* VSS-1 0.6224 VSS-2 0.0000 CD v*   > test-suggest_k.R: k = 2: PA-PC v PA-FA v MAP 0.0522 VSS-1 0.7305* VSS-2 0.7981 CD -   > test-suggest_k.R: k = 3: PA-PC - PA-FA v MAP 0.0971 VSS-1 0.6415 VSS-2 0.8407 CD -   > test-suggest_k.R: k = 4: PA-PC - PA-FA - MAP 0.1577 VSS-1 0.6451 VSS-2 0.8510* CD -   > test-suggest_k.R:   > test-suggest_k.R: -- Recommendations --   > test-suggest_k.R:   > test-suggest_k.R: * PA-PC: k <= 2   > test-suggest_k.R: * PA-FA: k <= 3   > test-suggest_k.R: * MAP: k = 1   > test-suggest_k.R: * VSS-1: k = 2   > test-suggest_k.R: * VSS-2: k = 4   > test-suggest_k.R: * CD: k = 1   > test-suggest_k.R: Consensus range: k = 1-4   > test-suggest_k.R: --------------------------------------------------------------------------------   > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels   > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional.   > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,   > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range.   > test-suggest_k.R:   > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------   > test-suggest_k.R: Variables: 8   > test-suggest_k.R: n: 1,000   > test-suggest_k.R: Basis: pearson   > test-suggest_k.R: Tested k: 1-4   > test-suggest_k.R:   > test-suggest_k.R: -- Criteria (k = 1-4) --   > test-suggest_k.R:   > test-suggest_k.R: k = 1: PA-PC v PA-FA v MAP 0.0427 VSS-1 0.6224 VSS-2 0.0000   > test-suggest_k.R: k = 2: PA-PC v PA-FA v MAP 0.0522* VSS-1 0.7305* VSS-2 0.7981*   > test-suggest_k.R: k = 3: PA-PC - PA-FA v MAP 0.0971 VSS-1 0.6415 VSS-2 0.8407   > test-suggest_k.R: k = 4: PA-PC - PA-FA - MAP 0.1577 VSS-1 0.6451 VSS-2 0.8510   > test-suggest_k.R: + CD requires EFAtools (install to enable).   > test-suggest_k.R:   > test-suggest_k.R: -- Recommendations --   > test-suggest_k.R:   > test-suggest_k.R: * PA-PC: k <= 2   > test-suggest_k.R: * PA-FA: k <= 2   > test-suggest_k.R: * MAP: k = 2   > test-suggest_k.R: * VSS-1: k = 2   > test-suggest_k.R: * VSS-2: k = 2   > test-suggest_k.R: Consensus: k = 2   > test-suggest_k.R: --------------------------------------------------------------------------------   > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels   > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional.   > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,   > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range.   > test-suggest_k.R:   > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------   > test-suggest_k.R: Variables: 8   > test-suggest_k.R: n: 1,000   > test-suggest_k.R: Basis: pearson   > test-suggest_k.R: Tested k: 1-4   > test-suggest_k.R:   > test-suggest_k.R: -- Criteria (k = 1-4) --   > test-suggest_k.R:   > test-suggest_k.R: k = 1: PA-PC v PA-FA v MAP 0.0427 VSS-1 0.6224 VSS-2 0.0000   > test-suggest_k.R: k = 2: PA-PC v PA-FA v MAP 0.0522* VSS-1 0.7305* VSS-2 0.7981*   > test-suggest_k.R: k = 3: PA-PC - PA-FA v MAP 0.0971 VSS-1 0.6415 VSS-2 0.8407   > test-suggest_k.R: k = 4: PA-PC - PA-FA - MAP 0.1577 VSS-1 0.6451 VSS-2 0.8510   > test-suggest_k.R: + CD requires EFAtools (install to enable).   > test-suggest_k.R:   > test-suggest_k.R: -- Recommendations --   > test-suggest_k.R:   > test-suggest_k.R: * PA-PC: k <= 2   > test-suggest_k.R: * PA-FA: k <= 2   > test-suggest_k.R: * MAP: k = 2   > test-suggest_k.R: * VSS-1: k = 2   > test-suggest_k.R: * VSS-2: k = 2   > test-suggest_k.R: Consensus: k = 2   > test-suggest_k.R: --------------------------------------------------------------------------------   > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels   > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional.   > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,   > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range.   > test-suggest_k.R: i Running MAP and VSS...   > test-suggest_k.R: v Running MAP and VSS... [10ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running parallel analysis (5 iterations, PC + FA)...   > test-suggest_k.R: v Running parallel analysis (5 iterations, PC + FA)... [42ms]   > test-suggest_k.R:   > test-suggest_k.R: CD: 54 rows with missing values removed (946 complete cases used).   > test-suggest_k.R: i Running Comparison Data (CD)...   > test-suggest_k.R: v Running Comparison Data (CD)... [688ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running MAP and VSS...   > test-suggest_k.R: v Running MAP and VSS... [14ms]   > test-suggest_k.R:   > test-suggest_k.R:   > test-suggest_k.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------   > test-suggest_k.R: Variables: 8   > test-suggest_k.R: n: 1,000   > test-suggest_k.R: Basis: pearson   > test-suggest_k.R: Tested k: 1-4   > test-suggest_k.R:   > test-suggest_k.R: -- Criteria (k = 1-4) --   > test-suggest_k.R:   > test-suggest_k.R: k = 1: VSS-1 0.6224 VSS-2 0.0000   > test-suggest_k.R: k = 2: VSS-1 0.7305* VSS-2 0.7981   > test-suggest_k.R: k = 3: VSS-1 0.6415 VSS-2 0.8407   > test-suggest_k.R: k = 4: VSS-1 0.6451 VSS-2 0.8510*   > test-suggest_k.R:   > test-suggest_k.R: -- Recommendations --   > test-suggest_k.R:   > test-suggest_k.R: * VSS-1: k = 2   > test-suggest_k.R: * VSS-2: k = 4   > test-suggest_k.R: Consensus range: k = 2-4   > test-suggest_k.R: --------------------------------------------------------------------------------   > test-suggest_k.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels   > test-suggest_k.R: above the consensus to observe factor fragmentation is intentional.   > test-suggest_k.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,   > test-suggest_k.R: 2023). PA-FA and CD are more conservative. Use the range.   > test-suggest_k.R: i Running MAP and VSS...   > test-suggest_k.R: v Running MAP and VSS... [86ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)...   > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [50ms]   > test-suggest_k.R:   > test-suggest_k.R: i Running parallel analysis (3 iterations, PC + FA)...   > test-suggest_k.R: v Running parallel analysis (3 iterations, PC + FA)... [31ms]   > test-suggest_k.R:   > test-layout.R: i Redundancy pruning (direct criterion, |r| >= 0.9) flagged 3 nodes.   > test-layout.R: i Nodes are retained in the object; inspect with `x$prune$nodes` and `x$prune$chains`.   > test-esem.R:   > test-esem.R: *** caught segfault ***   > test-esem.R: address 0x110, cause 'invalid permissions'   > test-esem.R:   > test-esem.R: Traceback:   > test-esem.R: 1: lav_inspect_vcov(object, standardized = TRUE, type = type, ov_std = ov_std_user, free_only = FALSE, add_labels = FALSE, add_class = FALSE)   > test-esem.R: 2: doTryCatch(return(expr), name, parentenv, handler)   > test-esem.R: 3: tryCatchOne(expr, names, parentenv, handlers[[1L]])   > test-esem.R: 4: tryCatchList(expr, classes, parentenv, handlers)   > test-esem.R: 5: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})   > test-esem.R: 6: try(lav_inspect_vcov(object, standardized = TRUE, type = type, ov_std = ov_std_user, free_only = FALSE, add_labels = FALSE, add_class = FALSE))   > test-esem.R: 7: standardizedSolution(lavaan, remove_eq = FALSE, remove_ineq = FALSE, remove_def = FALSE)   > test-esem.R: 8: lav_step17_lavaan(lavmc = lavmc, timing = timing, lavoptions = lavoptions, lavpartable = lavpartable, lavdata = lavdata, lavsamplestats = lavsamplestats, lavmodel = lavmodel, lavcache = lavcache, lavfit = lavfit, lavboot = lavboot, lavoptim = lavoptim, lavimplied = lavimplied, lavloglik = lavloglik, lavvcov = lavvcov, lavtest = lavtest, lavh1 = lavh1, lavbaseline = lavbaseline, laveqs = laveqs, start_time0 = start_time0, lav_monte_carlo = lav_monte_carlo)   > test-esem.R: 9: lavaan::lavaan(model = c("efa(\"efa\")*f1 =~ x1 + x2 + x3 + x4 + x5 + x6", "efa(\"efa\")*f2 =~ x1 + x2 + x3 + x4 + x5 + x6", "efa(\"efa\")*f3 =~ x1 + x2 + x3 + x4 + x5 + x6"), data = list(x1 = c(5L, 3L, 4L, 4L, 3L, 4L, 4L, 3L, 5L, 2L, 4L, 5L, 3L, 3L, 3L, 4L, 2L, 1L, 1L, 5L, 3L, 1L, 3L, 5L, 5L, 3L, 3L, 1L, 4L, 2L, 4L, 4L, 4L, 3L, 5L, 1L, 1L, 2L, 1L, 3L, 4L, 2L, 3L, 3L, 1L, 3L, 1L, 5L, 3L, 4L, 4L, 2L, 5L, 4L, 4L, 3L, 3L, 3L, 1L, 4L, 2L, 4L, 4L, 5L, 3L, 5L, 4L, 5L, 3L, 4L, 2L, 3L, 4L, 1L, 3L, 3L, 3L, 4L, 1L, 1L, 5L, 3L, 4L, 3L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 2L, 4L, 5L, 1L, 1L, 1L, 1L, 3L, 4L, 5L, 5L, 1L, 5L, 1L, 3L, 2L, 2L, 3L, 3L, 3L, 4L, 2L, 2L, 1L, 2L, 3L, 5L, 1L, 3L, 1L, 2L, 3L, 2L, 3L, 3L, 3L, 1L, 1L, 3L, 4L, 3L, 3L, 4L, 5L, 2L, 3L, 4L, 3L, 3L, 3L, 3L, 2L, 2L, 1L, 5L, 2L, 2L, 4L, 1L, 2L, 1L, 5L, 3L, 1L, 1L, 3L, 3L, 3L, 5L, 3L, 1L, 3L, 3L, 4L, 5L, 1L, 3L, 3L, 5L, 3L, 3L, 1L, 5L, 5L, 3L, 1L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 4L, 2L, 2L, 3L, 3L, 4L, 1L, 2L, 4L, 2L, 3L, 5L, 3L, 1L, 4L, 5L, 5L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 5L, 4L, 3L, 3L, 3L, 3L, 5L, 2L, 3L, 2L, 3L, 2L, 1L, 3L, 3L, 3L, 1L, 2L, 5L, 3L, 5L, 1L, 3L, 1L, 2L, 5L, 3L, 4L, 1L, 3L, 4L, 4L, 5L, 1L, 5L, 4L, 2L, 5L, 2L, 1L, 3L, 1L, 3L, 5L, 1L, 3L, 3L, 2L, 2L, 4L, 5L, 3L, 1L, 3L, 5L, 5L, 3L, 1L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 3L, 2L, 5L, 4L, 3L, 2L, 3L, 3L, 3L, 5L, 5L, 2L, 1L, 4L, 2L, 5L, 2L, 5L, 2L, 3L, 3L, 4L, 4L, 2L), x2 = c(5L, 2L, 3L, 3L, 5L, 1L, 5L, 3L, 5L, 2L, 5L, 5L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 5L, 3L, 1L, 2L, 5L, 5L, 3L, 2L, 1L, 3L, 3L, 4L, 5L, 5L, 2L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 2L, 5L, 2L, 1L, 4L, 1L, 5L, 3L, 4L, 4L, 3L, 5L, 3L, 3L, 3L, 5L, 4L, 1L, 4L, 3L, 3L, 3L, 5L, 2L, 5L, 5L, 3L, 4L, 5L, 1L, 2L, 4L, 3L, 3L, 4L, 5L, 4L, 2L, 1L, 5L, 3L, 3L, 3L, 1L, 5L, 3L, 5L, 5L, 5L, 5L, 2L, 4L, 5L, 2L, 2L, 1L, 2L, 3L, 3L, 5L, 5L, 2L, 5L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 2L, 1L, 5L, 1L, 3L, 2L, 1L, 4L, 3L, 3L, 3L, 2L, 1L, 1L, 3L, 4L, 3L, 3L, 5L, 5L, 1L, 4L, 5L, 2L, 3L, 2L, 1L, 2L, 1L, 3L, 5L, 2L, 2L, 3L, 2L, 3L, 1L, 5L, 3L, 3L, 1L, 3L, 3L, 2L, 5L, 3L, 1L, 3L, 2L, 3L, 5L, 2L, 3L, 3L, 4L, 3L, 4L, 1L, 5L, 4L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 4L, 3L, 1L, 1L, 3L, 3L, 3L, 1L, 2L, 5L, 4L, 4L, 5L, 5L, 1L, 3L, 4L, 5L, 1L, 2L, 2L, 1L, 3L, 3L, 1L, 5L, 3L, 3L, 4L, 3L, 3L, 5L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 5L, 3L, 4L, 1L, 3L, 3L, 2L, 5L, 3L, 3L, 2L, 1L, 5L, 5L, 5L, 1L, 5L, 4L, 3L, 5L, 1L, 1L, 3L, 2L, 3L, 5L, 2L, 3L, 3L, 1L, 1L, 5L, 4L, 3L, 1L, 3L, 4L, 3L, 2L, 1L, 3L, 4L, 3L, 3L, 3L, 3L, 5L, 4L, 2L, 4L, 5L, 5L, 1L, 2L, 3L, 3L, 3L, 5L, 2L, 1L, 2L, 2L, 4L, 3L, 5L, 3L, 3L, 2L, 3L, 3L, 1L), x3 = c(5L, 2L, 3L, 4L, 4L, 3L, 4L, 4L, 5L, 3L, 4L, 5L, 1L, 3L, 3L, 4L, 4L, 1L, 1L, 5L, 3L, 1L, 3L, 5L, 4L, 2L, 3L, 1L, 4L, 3L, 3L, 4L, 5L, 3L, 3L, 1L, 2L, 2L, 1L, 3L, 3L, 2L, 4L, 2L, 1L, 4L, 3L, 5L, 2L, 4L, 3L, 2L, 5L, 3L, 3L, 3L, 5L, 2L, 1L, 3L, 3L, 3L, 5L, 5L, 1L, 4L, 3L, 5L, 4L, 5L, 2L, 3L, 3L, 1L, 1L, 4L, 4L, 5L, 2L, 2L, 5L, 3L, 3L, 3L, 1L, 4L, 4L, 2L, 5L, 4L, 5L, 2L, 4L, 5L, 1L, 2L, 1L, 2L, 3L, 4L, 4L, 4L, 2L, 5L, 2L, 3L, 3L, 3L, 3L, 2L, 4L, 3L, 3L, 1L, 1L, 2L, 1L, 5L, 2L, 3L, 1L, 2L, 2L, 2L, 3L, 3L, 2L, 1L, 1L, 4L, 5L, 1L, 3L, 5L, 5L, 1L, 3L, 5L, 3L, 3L, 3L, 2L, 1L, 3L, 3L, 4L, 1L, 2L, 5L, 1L, 3L, 1L, 5L, 3L, 3L, 1L, 3L, 2L, 2L, 5L, 2L, 3L, 4L, 2L, 3L, 5L, 1L, 3L, 3L, 3L, 3L, 4L, 1L, 5L, 4L, 3L, 1L, 4L, 4L, 3L, 4L, 2L, 3L, 4L, 3L, 1L, 3L, 3L, 1L, 1L, 3L, 3L, 4L, 1L, 2L, 4L, 3L, 4L, 5L, 2L, 1L, 3L, 5L, 5L, 1L, 2L, 1L, 1L, 3L, 2L, 1L, 5L, 3L, 3L, 3L, 3L, 3L, 5L, 3L, 1L, 4L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 1L, 5L, 2L, 4L, 2L, 3L, 3L, 3L, 5L, 3L, 4L, 1L, 3L, 5L, 5L, 4L, 3L, 5L, 5L, 3L, 4L, 2L, 1L, 3L, 2L, 3L, 5L, 1L, 1L, 3L, 1L, 3L, 3L, 4L, 3L, 1L, 3L, 4L, 5L, 3L, 1L, 3L, 4L, 3L, 3L, 3L, 3L, 5L, 3L, 1L, 5L, 5L, 5L, 1L, 2L, 3L, 3L, 4L, 5L, 1L, 1L, 3L, 3L, 4L, 3L, 5L, 3L, 3L, 3L, 4L, 3L, 1L), x4 = c(3L, 4L, 3L, 5L, 3L, 3L, 4L, 4L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 5L, 3L, 3L, 5L, 2L, 5L, 3L, 1L, 3L, 4L, 2L, 2L, 4L, 5L, 3L, 1L, 1L, 3L, 2L, 2L, 4L, 1L, 1L, 4L, 3L, 4L, 1L, 3L, 3L, 5L, 2L, 4L, 1L, 1L, 4L, 3L, 4L, 2L, 1L, 2L, 1L, 3L, 3L, 3L, 3L, 5L, 2L, 3L, 4L, 5L, 3L, 5L, 2L, 5L, 3L, 3L, 2L, 3L, 5L, 5L, 1L, 4L, 5L, 2L, 2L, 2L, 3L, 5L, 4L, 3L, 3L, 3L, 2L, 3L, 4L, 3L, 3L, 4L, 3L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 5L, 1L, 1L, 3L, 1L, 3L, 2L, 2L, 1L, 3L, 1L, 5L, 4L, 1L, 2L, 2L, 4L, 3L, 2L, 4L, 3L, 3L, 5L, 2L, 2L, 1L, 3L, 5L, 1L, 2L, 2L, 5L, 5L, 3L, 5L, 3L, 5L, 3L, 5L, 3L, 2L, 4L, 1L, 2L, 3L, 2L, 3L, 2L, 5L, 4L, 5L, 2L, 5L, 5L, 4L, 1L, 1L, 3L, 5L, 5L, 1L, 2L, 4L, 1L, 4L, 1L, 3L, 2L, 2L, 3L, 3L, 4L, 3L, 1L, 1L, 1L, 4L, 3L, 3L, 1L, 3L, 5L, 3L, 3L, 3L, 5L, 3L, 4L, 5L, 2L, 4L, 3L, 5L, 4L, 2L, 4L, 2L, 3L, 3L, 2L, 1L, 4L, 4L, 2L, 2L, 3L, 5L, 5L, 1L, 2L, 4L, 3L, 5L, 3L, 4L, 3L, 5L, 4L, 1L, 2L, 5L, 1L, 1L, 1L, 2L, 3L, 2L, 3L, 2L, 3L, 5L, 1L, 2L, 3L, 3L, 5L, 4L, 4L, 4L, 2L, 3L, 5L, 1L, 3L, 3L, 5L, 3L, 3L, 5L, 1L, 5L, 1L, 5L, 3L, 5L, 4L, 2L, 1L, 4L, 3L, 2L, 3L, 2L, 2L, 1L, 1L, 3L, 5L, 5L, 3L, 3L, 4L, 3L, 3L, 2L, 1L, 3L, 1L, 4L, 1L, 2L, 3L, 4L, 3L, 3L, 2L, 3L, 3L, 1L, 5L, 1L, 4L), x5 = c(3L, 4L, 3L, 3L, 1L, 3L, 4L, 5L, 3L, 3L, 3L, 2L, 2L, 3L, 4L, 3L, 2L, 3L, 1L, 5L, 3L, 3L, 3L, 3L, 5L, 2L, 4L, 4L, 2L, 3L, 4L, 3L, 3L, 3L, 5L, 2L, 1L, 1L, 1L, 2L, 4L, 4L, 3L, 2L, 3L, 3L, 5L, 1L, 3L, 5L, 3L, 2L, 4L, 2L, 3L, 5L, 2L, 5L, 3L, 1L, 3L, 2L, 3L, 3L, 3L, 1L, 5L, 1L, 3L, 3L, 5L, 4L, 5L, 1L, 5L, 3L, 3L, 1L, 3L, 5L, 5L, 1L, 4L, 5L, 3L, 4L, 1L, 4L, 5L, 4L, 3L, 3L, 5L, 2L, 3L, 4L, 3L, 3L, 2L, 5L, 5L, 3L, 3L, 4L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 2L, 5L, 4L, 1L, 3L, 1L, 4L, 3L, 2L, 4L, 3L, 3L, 5L, 2L, 2L, 1L, 2L, 5L, 1L, 3L, 1L, 5L, 4L, 3L, 5L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 1L, 5L, 4L, 5L, 2L, 5L, 5L, 3L, 1L, 1L, 3L, 5L, 4L, 1L, 2L, 3L, 1L, 5L, 1L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 1L, 1L, 1L, 4L, 3L, 3L, 1L, 1L, 4L, 3L, 3L, 3L, 5L, 2L, 3L, 5L, 1L, 3L, 3L, 5L, 4L, 2L, 2L, 1L, 3L, 1L, 1L, 1L, 5L, 3L, 3L, 2L, 3L, 5L, 5L, 1L, 3L, 5L, 3L, 5L, 3L, 3L, 4L, 5L, 5L, 2L, 3L, 4L, 1L, 1L, 1L, 2L, 5L, 3L, 3L, 2L, 3L, 5L, 2L, 2L, 2L, 3L, 5L, 3L, 5L, 3L, 2L, 3L, 5L, 1L,   > test-esem.R: 4L, 3L, 4L, 5L, 3L, 5L, 1L, 5L, 1L, 3L, 3L, 5L, 3L, 1L, 1L, 5L, 3L, 2L, 3L, 1L, 2L, 2L, 1L, 3L, 5L, 5L, 4L, 3L, 5L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 4L, 1L, 4L), x6 = c(3L, 5L, 3L, 3L, 3L, 3L, 4L, 5L, 1L, 3L, 3L, 1L, 1L, 2L, 3L, 2L, 2L, 3L, 1L, 4L, 3L, 3L, 3L, 3L, 5L, 2L, 5L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 4L, 3L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 1L, 5L, 4L, 5L, 1L, 4L, 5L, 5L, 1L, 3L, 3L, 3L, 5L, 3L, 4L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 5L, 1L, 3L, 5L, 5L, 3L, 5L, 1L, 5L, 2L, 4L, 1L, 3L, 5L, 5L, 2L, 3L, 5L, 3L, 1L, 3L, 4L, 5L, 5L, 3L, 1L, 3L, 1L, 3L, 5L, 3L, 3L, 2L, 5L, 5L, 2L, 3L, 3L, 3L, 1L, 4L, 5L, 5L, 1L, 2L, 2L, 1L, 3L, 3L, 2L, 1L, 3L, 1L, 5L, 5L, 1L, 4L, 3L, 4L, 4L, 2L, 5L, 3L, 3L, 5L, 2L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 5L, 5L, 3L, 5L, 1L, 4L, 3L, 3L, 3L, 2L, 4L, 1L, 3L, 3L, 2L, 3L, 3L, 5L, 4L, 5L, 2L, 3L, 5L, 3L, 1L, 1L, 3L, 5L, 3L, 1L, 2L, 3L, 1L, 5L, 1L, 3L, 3L, 2L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 5L, 3L, 3L, 2L, 2L, 4L, 3L, 3L, 3L, 5L, 2L, 5L, 4L, 1L, 3L, 4L, 5L, 5L, 3L, 3L, 1L, 3L, 1L, 2L, 1L, 5L, 5L, 2L, 1L, 3L, 5L, 5L, 1L, 2L, 5L, 3L, 5L, 3L, 5L, 4L, 5L, 5L, 1L, 2L, 4L, 1L, 1L, 1L, 4L, 5L, 3L, 3L, 4L, 2L, 5L, 2L, 3L, 2L, 3L, 5L, 3L, 5L, 5L, 3L, 3L, 4L, 1L, 3L, 3L, 5L, 5L, 3L, 5L, 1L, 5L, 1L, 3L, 3L, 5L, 3L, 1L, 1L, 5L, 3L, 3L, 3L, 2L, 3L, 1L, 1L, 3L, 5L, 5L, 3L, 3L, 4L, 3L, 3L, 1L, 1L, 5L, 1L, 5L, 1L, 1L, 3L, 3L, 3L, 4L, 2L, 2L, 3L, 3L, 5L, 1L, 5L)), sample_cov = NULL, sample_nobs = NULL, rotation = "varimax", rotation.args = list(), bounds = "pos.var", cmd = "sem", estimator = "WLSMV", ordered = c("x1", "x2", "x3", "x4", "x5", "x6"), missing = "available.cases", slot.sample.stats = new("lavSampleStats", var = list(c(1, 1, 1, 1, 1, 1)), cov = list(c(1, 0.847708645568633, 0.852266877770598, -0.045025067439856, 0.0187792727635132, -0.0528665611502654, 0.847708645568633, 1, 0.866098037290729, -0.00562422746118933, 0.0637607092511551, 0.00299593013986168, 0.852266877770598, 0.866098037290729, 1, -0.0468064624916715, 0.0271693531291436, -0.0307059937186903, -0.045025067439856, -0.00562422746118933, -0.0468064624916715, 1, 0.854835831983838, 0.8624962089988, 0.0187792727635132, 0.0637607092511551, 0.0271693531291436, 0.854835831983838, 1, 0.864362516433594, -0.0528665611502654, 0.00299593013986168, -0.0307059937186903, 0.8624962089988, 0.864362516433594, 1)), mean = list( c(0, 0, 0, 0, 0, 0)), th = list(c(-0.954165253146194, -0.467698799114508, 0.449136303439425, 1.00825234717068, -0.941074530352976, -0.439913165673234, 0.524400512708041, 0.853587957511572, -0.902734791643865, -0.458397807359904, 0.467698799114508, 0.994457883209753, -1.00825234717068, -0.394335532100823, 0.486423874628588, 0.967421566101701, -0.841621233572914, -0.412463129441405, 0.553384719555673, 0.915365087842814, -0.80642124701824, -0.458397807359904, 0.505321088513851, 0.760983928488951)), th.idx = list( c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L)), th.names = list(c("x1|t1", "x1|t2", "x1|t3", "x1|t4", "x2|t1", "x2|t2", "x2|t3", "x2|t4", "x3|t1", "x3|t2", "x3|t3", "x3|t4", "x4|t1", "x4|t2", "x4|t3", "x4|t4", "x5|t1", "x5|t2", "x5|t3", "x5|t4", "x6|t1", "x6|t2", "x6|t3", "x6|t4")), res.cov = list(NULL), res.var = list( NULL), res.th = list(NULL), res.th.nox = list(NULL), res.slopes = list(NULL), res.int = list(NULL), mean.x = list( NULL), cov.x = list(NULL), bifreq = list(NULL), group.w = list( 1), nobs = list(300L), ntotal = 300L, ngroups = 1L, x.idx = list(integer(0)), icov = list(NULL), cov.log.det = list( NULL), res.icov = list(NULL), res.cov.log.det = list( NULL), ridge = 0, WLS.obs = list(c(-0.954165253146194, -0.467698799114508, 0.449136303439425, 1.00825234717068, -0.941074530352976, -0.439913165673234, 0.524400512708041, 0.853587957511572, -0.902734791643865, -0.458397807359904, 0.467698799114508, 0.994457883209753, -1.00825234717068, -0.394335532100823, 0.486423874628588, 0.967421566101701, -0.841621233572914, -0.412463129441405, 0.553384719555673, 0.915365087842814, -0.80642124701824, -0.458397807359904, 0.505321088513851, 0.760983928488951, 0.847708645568633, 0.852266877770598, -0.045025067439856, 0.0187792727635132, -0.0528665611502654, 0.866098037290729, -0.00562422746118933, 0.0637607092511551, 0.00299593013986168, -0.0468064624916715, 0.0271693531291436, -0.0307059937186903, 0.854835831983838, 0.8624962089988, 0.864362516433594)), WLS.V = list(NULL), WLS.VD = list(c(0.45383482142755, 0.587709518619898, 0.591395244765584, 0.435865310988534, 0.458128368522494, 0.593178170698077, 0.575667399853721, 0.486139871182117, 0.470559146996906, 0.589572474383863, 0.587709518619898, 0.440480678601174, 0.435865310988534, 0.601506349600645, 0.583861633032411, 0.449463546185201, 0.489866730490175, 0.598291135869332, 0.569072720318582, 0.466488951106186, 0.500662923226088, 0.589572474383863, 0.579848639230085, 0.514201673558285, 7.44280585633078, 6.75326246420858, 0.68465337775283, 0.747098037875909, 0.711965105740159, 11.6264723451673, 0.71957812751013, 0.780476688725976, 0.738014115692331, 0.688027249018883, 0.764993477317059, 0.751303879951114, 8.27390801013265, 8.47099984869401, 10.1221593413523)), NACOV = list(c(2.20344484994446, 1.2774226485315, 0.608464081144384, 0.438580200934569, 1.24211958837833, 1.06100103747073, 0.579645270096896, 0.476730975188745, 1.17367332415033, 1.00896404529906, 0.601140069897175, 0.44176715543208, -0.219015644772081, -0.0242678714026954, -0.0379083822845332, 0.079081810080948, -0.0564610061408481, -0.120072924149705, 0.107363949033218, 0.109435686051998, -0.169568169571692, -0.238028958705595, -0.0135053916825915, -0.0269051436957956, 0.148317263761989, 0.15574465153135, -0.0301086318650708, 0.00933114409349929, -0.0308508864967571, 0.110777263526235, 0.00948677799058494, -0.00412089339286978, -0.10125465414037, 0.0785862105655183, 0.0555335781266458, -0.0638553156597295, -0.0280957630388753, -0.00123067312283201, -0.0342607132374605, 1.2774226485315, 1.70152085055262, 0.810471242285935, 0.584186727382364, 1.14089096616285, 1.11498605030749, 0.74527622661719, 0.635003375993402, 1.06755750714773, 1.03707982565867, 0.748585766358322, 0.588431735511437, -0.195764701197282, -0.057578351381687, -0.0504937836682346, -0.0373067097809645, 0.0599296137472571, -0.0417199784040522, 0.0501041505326083, 0.0809914034723241, -0.102201933410529, -0.130802860893886, -0.0148660064484897, 0.0449437782925636, 0.0741651928462058, 0.0490907248507851, -0.0176892046762685, 0.00394541784101921, -0.00847157050708947, 0.0757812257161068, -0.0661029519937833, -0.0252830371146104, -0.0476247917553443, -0.0264569477560322, 0.0323015886413371, -0.0169096775619646, -0.00652515275734159, -0.0245753162929485, -0.0110359863231057, 0.608464081144384, 0.810471242285935, 1.69091653822204, 1.21881067112823, 0.576674081941792, 0.774285253955362, 1.0792024722852, 1.05804201981214, 0.625589760454923, 0.815394330786237, 1.12783689455862, 1.15169768636051, 0.0136079556709071, 0.0243719568965317, -0.0184270459355523, -0.0493207998636407, 0.0858315089017964, 0.0837418857774159, -0.0113399097133595, 0.0479013123640107, 0.0827363125250564, -0.0337960621007554, -0.0540468233753761, 0.0344537680314544, -0.0835035702275452, -0.0786770150818312, 0.0106760261359684, -0.00123494659659294, 0.0122565252286696, -0.0592467719700587, -0.0412145589031354, -0.0176224865986117, -0.0380983101009663, -0.0402960408343714, -0.0522514103795643, -0.0445701055393561, 0.0398832411621319, 0.005478944302505, 0.0388495658350893, 0.438580200934569, 0.584186727382364, 1.21881067112823, 2.29428673213755, 0.441666799275109, 0.594893883378979, 1.19451162375741, 1.29095297173797, 0.450924370633182, 0.587735283838845, 1.16371239045051, 1.39753600268651, 0.0789137392518928, 0.0110390707298294, -0.146050804979038, -0.157518417294168, 0.0198461232749442, -0.0386675911959832, -0.106723079606281, 0.0285854186705204, -0.00626559185412115, -0.0310691387157628, -0.135030448940603, -0.116121963366209, -0.159334208125366, -0.139085742363334, 0.0271974707303992, -0.0071037068500013, 0.0263069574461733, -0.128992483494249, 0.107707346627344, 0.0946179973328222, 0.127452670358713, 0.110027033838194, 0.0789431409081486, 0.0904561706745593, 0.00997923231952932, -0.00851490425935088, 0.020452480807499, 1.24211958837833, 1.14089096616285, 0.576674081941792, 0.441666799275109, 2.18279431859916, 1.25161307106614, 0.58372464286605, 0.480086067447278, 1.29726260924579, 0.984812668193073, 0.605370716739469, 0.444876182620165, -0.170594704793659, -0.000939948520669462, -0.099353327882025, -0.121499556217292, 0.120824019421084, 0.0113621668615782, -0.0729739856394577, -0.0317318220007143, -0.0866730771247237, -0.174358692112653, -0.0760810863132528, -0.0604073968525699, 0.16194297708138, 0.120827352177701, -0.0919049501204477, -0.0267557831346448, -0.0881523522151011, 0.128137179839506, 0.000212298869746207, 0.0367164551098905, -0.00373340788228093, -0.000447770974788038, 0.0150078675242675, -0.0585860797001331, -0.0031712854387501, -0.0178213100814595, -0.00791788041762326, 1.06100103747073, 1.11498605030749, 0.774285253955362, 0.594893883378979, 1.25161307106614, 1.68583412100813, 0.786235732883992, 0.64664191532757, 1.13913953707982, 1.17862045059145, 0.81539146061268, 0.599216695337057, -0.134627822737602, -0.0578543768292243, -0.0254499315393859, -0.0184196260647881, 0.0394524588887727, -0.0165793698112683, 0.0190914135644343, 0.169073277814853, -0.057167462611575, -0.154022807307125, 0.0880784013810653, 0.0342100668274375, 0.0539361700938887, 0.0615567447500341, -0.0681981562446394, -0.0246495398767953, -0.0554549756281441, 0.0717566895580845, 5.75617068663093e-06,   > test-esem.R: 0.0204479578807549, 7.74832431628764e-05, 0.00318648418314011, 0.0406344952019763, 0.00971501942601362, -0.00345081806988481, -0.0155256161093073, 0.0078896468194146, 0.579645270096895, 0.74527622661719, 1.0792024722852, 1.19451162375741, 0.58372464286605, 0.786235732883992, 1.73711417435502, 1.42869471567676, 0.595959822290149, 0.670001492928105, 1.18493558332661, 1.16630442958261, 0.163795908274427, 0.109091022310938, -0.00540981015134475, -1.57046074786481e-16, 0.102731959227697, 0.146522255839915, 0.0252065688162364, 0.138836295431548, 0.0964684652932333, 0.0827491883696062, 0.120136315645039, -0.00321012859252077, -0.0858800421247186, -0.0451906419084792, -0.0765124055981449, -0.0495654183174262, -0.0848028980005815, -0.0833098166158672, -0.000535439721952539, -0.0120376783586505, 0.00713013186502567, 0.00519137622969441, 0.0317853983114957, 0.0120483492539913, 0.0335091835712613, -0.0158877338895445, 0.0458021905858184, 0.476730975188745, 0.635003375993402, 1.05804201981214, 1.29095297173797, 0.480086067447278, 0.64664191532757, 1.42869471567676, 2.05702115641813, 0.490148927129519, 0.571882388380539, 1.21484756042805, 1.46125569212401, 0.212680617707058, 0.145119741737691, -0.0165152518961889, -0.0882562149935091, 0.206218263545022, 0.166099245009795, -0.00386514223473117, 0.0174182527862748, 0.0997440147141751, 0.103034832027179, 0.0653127512676339, -0.0071150608221477, -0.134071027660114, -0.0976632797843361, -0.137740647801462, -0.129333918235308, -0.117770415220244, -0.155270199765802, 0.00279700652525927, -0.029357693070375, 0.00385735432182198, -0.0242062457633788, -0.0532930035886563, -0.00857750561667388, 0.0767430961264705, -0.00352146452370503, 0.0679798285981358, 1.17367332415033, 1.06755750714773, 0.625589760454923, 0.450924370633182, 1.29726260924579, 1.13913953707982, 0.595959822290149, 0.49014892712952, 2.1251313599618, 1.30132191861677, 0.618059609401317, 0.454201024362567, -0.241597776915651, -0.0362922311272902, -0.098028891044111, -0.142411540832355, 0.0448569291718996, -0.0239915790387723, -0.0385217266559336, 0.0430732015144227, -0.111115021635821, -0.132288435076582, -0.0047687613849894, -0.0301359480319713, 0.138783846835874, 0.13451304238041, -0.0723053721025387, 0.0235559639025693, -0.0229809041838941, 0.14579926213725, -0.0346675510650626, 0.0278921595163239, -0.0029584179876469, -0.0143270955539256, 0.0186565372953722, -0.0162965216843151, -0.00917673037167745, 0.00379043849646447, 0.00436030761914487, 1.00896404529906, 1.03707982565867, 0.815394330786237, 0.587735283838845, 0.984812668193073, 1.17862045059145, 0.670001492928105, 0.571882388380539, 1.30132191861677, 1.69614431380138, 0.805579524235362, 0.592006077646191, -0.0849566905132264, 0.0345326507924104, -0.042071892364949, -0.0309553186878741, 0.119344126776945, 0.0764956513097508, -0.0235887381762751, 0.0516400329150373, 0.0202881363683058, -0.0352304618528652, -0.0595609077281687, 0.0516911215418863, 0.0687933674969333, 0.0369020237224323, 0.00995076809763176, 0.064092949342599, 0.0369408306412495, 0.0799479942345352, 0.005592058134837, 0.0487302938257757, 0.0493669036835191, -0.0138630042221855, 0.00884439739842633, -0.00367260312735112, 0.0351601326522669, 0.00839475638588036, 0.0119231550404147, 0.601140069897175, 0.748585766358322, 1.12783689455862, 1.16371239045051, 0.605370716739469, 0.81539146061268, 1.18493558332661, 1.21484756042805, 0.618059609401317, 0.805579524235362, 1.70152085055262, 1.2504174379618, 0.118080295960265, 0.0828320142683918, -0.133598135955537, -0.186533548904822, 0.0732473056910928, 0.0671589896260352, -0.104565183720226, 0.0255762326754712, 0.0375171654291813, -0.0508677792365115, -0.0647733138112737, -0.0137328211449501, -0.0724591490523034, -0.0791267384446038, 0.00181309216736721, -0.0214221821010069, -0.0300017587548346, -0.0877579746528331, 0.10479954023783, 0.0704790601404688, 0.0725730814762955, 0.0132397293060142, 0.00392597583562099, 0.00574676565142401, 0.0545645893278382, 0.00351526032811938, 0.0482644269885699, 0.44176715543208, 0.588431735511437, 1.15169768636051, 1.39753600268651, 0.444876182620165, 0.599216695337057, 1.16630442958261, 1.46125569212401, 0.454201024362567, 0.592006077646191, 1.2504174379618, 2.27024713813936, 0.200952889275185, -0.0133620970402425, -0.0788523417871815, -0.219328304783088, 0.176164832903571, 0.0493539480970899, -0.116865540995329, 0.0187955189173209, 0.00380285492607957, -0.0183094663189546, -0.0671092644366832, -0.0330283840404653, -0.139306280370751, -0.13875441713431, 0.0668223880050227, -0.0448927653441914, 0.0318365551567394, -0.151806904210309, 0.148960920619109, 0.0609038763998929, 0.134305485249476, 0.031169280380637, -0.0100546599966053, 0.0170376579092655, 0.0337745421542386, -0.000211240075709474, 0.0322855829479996, -0.219015644772081, -0.195764701197282, 0.0136079556709071, 0.0789137392518928, -0.170594704793659, -0.134627822737602, 0.163795908274427, 0.212680617707058, -0.241597776915651, -0.0849566905132264, 0.118080295960265, 0.200952889275185, 2.29428673213755, 1.15558999503623, 0.577148887654197, 0.435492094872112, 1.27015188959644, 1.06222147579554, 0.553093374537492, 0.447838225838153, 1.35577768351094, 1.11397687818633, 0.570187169159342, 0.488208361335366, -0.0452073659278695, -0.0368039242195606, -0.0249465575779013, -0.157562953805574, -0.0510773841561005, -0.0144087480818, -0.000875250301410177, -0.102613201353469, 0.000573271171747722, -0.0360467188781403, -0.106138266021219, -0.00157974786814207, 0.155628564410312, 0.153818992474422, 0.166944494374591, -0.0242678714026954, -0.057578351381687, 0.0243719568965317, 0.0110390707298294, -0.000939948520669464, -0.0578543768292243, 0.109091022310938, 0.145119741737691, -0.0362922311272901, 0.0345326507924104, 0.0828320142683918, -0.0133620970402424, 1.15558999503623, 1.66249284095492, 0.830316892679672, 0.626521943879109, 0.974188874780071, 1.1002507213572, 0.742943487462754, 0.64428374039232, 1.10176290111632, 1.11577341589458, 0.743140939070639, 0.702362350920955, -0.0228496292950282, -0.00169230710749049, -0.0100558231103412, -0.00378787104048013, -0.0243505783648102, 0.0045802720538116, 0.0072931844882099, 0.0359904807552618, -0.0170014618557995, -0.0151073514301341, 0.0160490751802889, 0.00744705291894205, 0.0506965702279097, 0.0646531323927172, 0.0136332585073733, -0.0379083822845332, -0.0504937836682345, -0.0184270459355523, -0.146050804979038, -0.099353327882025, -0.0254499315393859, -0.00540981015134475, -0.0165152518961889, -0.098028891044111, -0.042071892364949, -0.133598135955537, -0.0788523417871815, 0.577148887654197, 0.830316892679671, 1.71273456487676, 1.29235692829569, 0.631548022402756, 0.768991594381702, 1.17427537880214, 1.1139472986559, 0.644167387922915, 0.76968868646083, 1.18316692085857, 1.25985049764384, -0.0497100849928933, 0.0114243324019316, 0.0145262770963222, 0.0972680602138176, 0.0637726699623928, 0.012559878300944, -0.00486631793652915, 0.0635513532398554, 0.0254927803908756, 0.00644936625016048, 0.0984423108227788, 0.0677157657287604, -0.0765907638253904, -0.07315026181056, -0.0918071222196781, 0.0790818100809479, -0.0373067097809646, -0.0493207998636407, -0.157518417294168, -0.121499556217292, -0.0184196260647881, -1.83037594978595e-16, -0.0882562149935091, -0.142411540832355, -0.0309553186878741, -0.186533548904822, -0.219328304783088, 0.435492094872112, 0.626521943879109, 1.29235692829569, 2.22487453874168, 0.476539376877921, 0.618983350337493, 1.14976137667066, 1.42361294424794, 0.486061415374205, 0.600533182544757, 1.05121838213894, 1.15403067874422, -0.0158659044150101, 0.0563346056670746, 0.0236919740312106, 0.103285956119985, -0.000130894536512416, 0.0205212058726693, -0.007320814773629, 0.0875644703300589, -0.00850467831901114, 0.0127667492155082, 0.17068413621789, 0.0662442892600502, -0.152316211916453, -0.145043021521224, -0.119582194867943, -0.056461006140848, 0.0599296137472572, 0.0858315089017964, 0.0198461232749443, 0.120824019421084, 0.0394524588887727, 0.102731959227697, 0.206218263545022, 0.0448569291718996, 0.119344126776945, 0.0732473056910928, 0.176164832903571, 1.27015188959644, 0.974188874780071, 0.631548022402756, 0.476539376877921, 2.04137153588563, 1.28678875419635, 0.605225158300069, 0.490049191698183, 1.2559069292527, 1.04757622393096, 0.623930127537152, 0.534224411962442, -0.00822479371015104, 0.00663271019471845, 0.019465008972702, 0.00885836454286383, 0.0422893759118978, 0.00923383989163264, 0.0428107979498749, 0.0357418739580545, 0.142453716502339, 0.0307344604466001, 0.0109412500742362, 0.0843708232885414, 0.134776830038491, 0.118952680023042, 0.141748124176178, -0.120072924149705, -0.0417199784040522, 0.0837418857774159, -0.0386675911959832, 0.0113621668615782, -0.0165793698112683, 0.146522255839915, 0.166099245009794, -0.0239915790387723, 0.0764956513097508, 0.0671589896260352, 0.0493539480970899, 1.06222147579554, 1.1002507213572, 0.768991594381703, 0.618983350337493, 1.28678875419635, 1.67142706961048, 0.786135027597283, 0.636531428934211, 1.09154115621558, 1.11501277174014, 0.758613312330241, 0.693911211524711, -0.00264628084991393, -0.020670859392103, 0.0206585540721674, 0.0076120270915056, 0.01977840827582, 0.00887062962888688, 0.0857746926208271, 0.0129649274588139, 0.0930336131142227, 0.0639141756310739, 0.00174847079953262, 0.0826773995441732, 0.0577716250034614, 0.0240332716358964, 0.062104419291224, 0.107363949033218, 0.0501041505326083, -0.0113399097133595, -0.106723079606281, -0.0729739856394577, 0.0190914135644343, 0.0252065688162364,   > test-esem.R: -0.00386514223473116, -0.0385217266559336, -0.0235887381762751, -0.104565183720226, -0.116865540995329, 0.553093374537492, 0.742943487462754, 1.17427537880214, 1.14976137667066, 0.605225158300069, 0.786135027597283, 1.75724466187761, 1.42283630209259, 0.549741259193713, 0.708475550052955, 1.22914973668607, 1.25763749578798, -0.0296908946602319, 0.0104200414391291, -0.0145720736487281, 0.0033195334097242, 0.0304989245705094, 0.0248134478800154, 0.019809047212115, -0.0278335902754632, 0.0775381432887526, 0.0170624923090307, -0.00828708360663368, 0.0575131343085677, -0.0862221610584902, -0.0960292154420592, -0.121941490751061, 0.109435686051998, 0.0809914034723241, 0.0479013123640107, 0.0285854186705205, -0.0317318220007143, 0.169073277814853, 0.138836295431548, 0.0174182527862748, 0.0430732015144227, 0.0516400329150374, 0.0255762326754712, 0.0187955189173209, 0.447838225838153, 0.64428374039232, 1.1139472986559, 1.42361294424794, 0.490049191698183, 0.636531428934211, 1.42283630209259, 2.14367349457838, 0.499841178456957, 0.617558201846951, 1.24599523308296, 1.40112741396321, -0.0394721249953512, 0.0370330313771152, -0.0431359339076604, -0.00738449211276925, -0.0854782307059588, 0.0247920992730687, -0.0973040204633906, -0.0424219899345994, -0.10773335775287, -0.0699475831262572, -0.0137548007419955, -0.0746340366111427, -0.14926687959738, -0.122855476743761, -0.153408764961069, -0.169568169571692, -0.102201933410529, 0.0827363125250564, -0.00626559185412116, -0.0866730771247237, -0.057167462611575, 0.0964684652932333, 0.0997440147141751, -0.111115021635821, 0.0202881363683058, 0.0375171654291814, 0.00380285492607959, 1.35577768351094, 1.10176290111632, 0.644167387922915, 0.486061415374205, 1.2559069292527, 1.09154115621558, 0.549741259193713, 0.499841178456957, 1.99735181817812, 1.37283056092203, 0.636397275021004, 0.544899092090007, -0.03708872669238, -0.00935414748115647, -0.00786735696938561, -0.0369559317351903, -0.0235867391754614, 0.00555327458708414, -0.0315362339684262, 0.00408552460269166, -0.000542481453965008, -0.0349182900330659, -0.0522477483772834, -0.0194906592088833, 0.0890398368213926, 0.131204656804499, 0.153319351221939, -0.238028958705595, -0.130802860893886, -0.0337960621007554, -0.0310691387157628, -0.174358692112653, -0.154022807307125, 0.0827491883696063, 0.103034832027179, -0.132288435076582, -0.0352304618528652, -0.0508677792365115, -0.0183094663189545, 1.11397687818633, 1.11577341589458, 0.76968868646083, 0.600533182544757, 1.04757622393096, 1.11501277174014, 0.708475550052955, 0.617558201846951, 1.37283056092203, 1.69614431380138, 0.786274468293161, 0.673227653107653, -0.0249870847892577, -0.0310623448791116, -0.0597662654253912, -0.0500516446493022, -0.0205554406398311, 0.00347675808841012, -0.0686655886007817, 0.00959998906957744, -0.00634275627248089, -0.0513443431595157, -0.00110780602245252, -0.0123907039927979, 0.0395883128666614, 0.0716961735842288, 0.0545351763997118, -0.0135053916825915, -0.0148660064484897, -0.0540468233753762, -0.135030448940603, -0.0760810863132528, 0.0880784013810652, 0.120136315645039, 0.0653127512676339, -0.00476876138498948, -0.0595609077281687, -0.0647733138112737, -0.0671092644366832, 0.570187169159342, 0.743140939070639, 1.18316692085857, 1.05121838213894, 0.623930127537152, 0.758613312330241, 1.22914973668607, 1.24599523308296, 0.636397275021004, 0.786274468293161, 1.72458799132095, 1.47663490650412, -0.0171591736580541, 0.0168446332834493, 0.0156862359188135, 0.033364333452295, 0.017604243021788, 0.0417778807218433, -0.0266860377378277, -0.00837804674737573, -0.00682932784787414, -0.0185360321639612, 0.00923449433845097, 0.0103019254283266, -0.0913713589057094, -0.0780111274536073, -0.103938786698107, -0.0269051436957957, 0.0449437782925636, 0.0344537680314544, -0.116121963366209, -0.0604073968525698, 0.0342100668274374, -0.00321012859252068, -0.0071150608221477, -0.0301359480319713, 0.0516911215418863, -0.0137328211449501, -0.0330283840404653, 0.488208361335366, 0.702362350920954, 1.25985049764384, 1.15403067874422, 0.534224411962442, 0.693911211524711, 1.25763749578798, 1.40112741396321, 0.544899092090007, 0.673227653107653, 1.47663490650412, 1.94476224295417, -0.0600572970215252, -0.0129462981571512, -0.0383334777746061, 0.0618573160951042, 0.0275218234991843, 0.0130731844584642, -0.0693311489782209, 0.0476073305366232, -0.00861177497025037, -0.104231047599455, -0.00601795258733618, 0.00995636294223921, -0.110540740333587, -0.122682130683992, -0.132968263756207, 0.148317263761989, 0.0741651928462058, -0.0835035702275452, -0.159334208125366, 0.161942977081381, 0.0539361700938887, -0.0858800421247186, -0.134071027660114, 0.138783846835874, 0.0687933674969333, -0.0724591490523034, -0.139306280370751, -0.0452073659278695, -0.0228496292950282, -0.0497100849928933, -0.0158659044150101, -0.00822479371015103, -0.00264628084991393, -0.0296908946602319, -0.0394721249953512, -0.03708872669238, -0.0249870847892577, -0.0171591736580541, -0.0600572970215252, 0.13435793157891, 0.0648999991313963, 0.0163257727891063, 0.0222001474813885, 0.00586415479170394, 0.0553201434932848, -0.00774333873252011, -0.00406552748573637, -0.00893407703646892, 0.00190480732538636, 0.00711445555663318, -0.00993507647126291, -0.00346764772535688, -0.00584038224322596, -0.0043281721243053, 0.15574465153135, 0.0490907248507852, -0.0786770150818312, -0.139085742363334, 0.120827352177701, 0.0615567447500341, -0.0451906419084792, -0.0976632797843361, 0.13451304238041, 0.0369020237224324, -0.0791267384446038, -0.13875441713431, -0.0368039242195606, -0.00169230710749049, 0.0114243324019316, 0.0563346056670746, 0.00663271019471846, -0.020670859392103, 0.0104200414391291, 0.0370330313771152, -0.00935414748115647, -0.0310623448791116, 0.0168446332834493, -0.0129462981571512, 0.0648999991313963, 0.148076578586997, 0.0102311998810714, 0.0360677109152718, 0.0157559499489283, 0.0471580164973155, -0.024660772824432, 0.00213812557707964, -0.0216906952994601, -0.0148339752411867, 0.018408323287891, -0.0189706498549756, -0.0118850547463466, -0.00878063137456458, -0.00695610027104775, -0.0301086318650708, -0.0176892046762685, 0.0106760261359684, 0.0271974707303992, -0.0919049501204477, -0.0681981562446394, -0.0765124055981449, -0.137740647801462, -0.0723053721025387, 0.00995076809763175, 0.00181309216736721, 0.0668223880050227, -0.0249465575779013, -0.0100558231103412, 0.0145262770963222, 0.0236919740312106, 0.0194650089727021, 0.0206585540721674, -0.0145720736487281, -0.0431359339076604, -0.00786735696938561, -0.0597662654253913, 0.0156862359188135, -0.0383334777746061, 0.0163257727891063, 0.0102311998810714, 1.46059310082162, 1.05092773064802, 1.14570089017992, -0.000204120425811351, 1.0924386835732, 0.800404470692711, 0.866078362448077, 1.06893041162047, 0.767172388010609, 0.812608776357063, 5.64931156303635e-06, 0.0102626772585488, 0.00490334272631464, 0.00933114409349929, 0.00394541784101922, -0.00123494659659295, -0.0071037068500013, -0.0267557831346448, -0.0246495398767953, -0.0495654183174262, -0.129333918235308, 0.0235559639025693, 0.064092949342599, -0.0214221821010069, -0.0448927653441914, -0.157562953805574, -0.00378787104048014, 0.0972680602138176, 0.103285956119985, 0.00885836454286384, 0.00761202709150559, 0.00331953340972421, -0.00738449211276925, -0.0369559317351903, -0.0500516446493022, 0.033364333452295, 0.0618573160951042, 0.0222001474813885, 0.0360677109152718, 1.05092773064801, 1.33851241644687, 1.0552643434031, 0.0164763439450202, 0.793710751295646, 1.05114166617281, 0.785239966039694, 0.790106915227831, 1.02177932037355, 0.772375497705721, -0.0457924736654755, -0.0294483813254788, -0.0249420219318624, -0.0308508864967572, -0.00847157050708947, 0.0122565252286696, 0.0263069574461733, -0.0881523522151011, -0.0554549756281442, -0.0848028980005815, -0.117770415220244, -0.0229809041838941, 0.0369408306412495, -0.0300017587548346, 0.0318365551567393, -0.0510773841561005, -0.0243505783648102, 0.0637726699623928, -0.000130894536512413, 0.0422893759118978, 0.01977840827582, 0.0304989245705094, -0.0854782307059588, -0.0235867391754614, -0.0205554406398311, 0.017604243021788, 0.0275218234991843, 0.00586415479170394, 0.0157559499489283, 1.14570089017992, 1.0552643434031, 1.40456321796895, 0.002375688741747, 0.854793847130113, 0.798909003312101, 1.08161905449057, 0.881489941991868, 0.812215657741097, 1.0699033568652, -0.0256936941471223, -0.0192954594118436, 0.00388272127596111, 0.110777263526235, 0.0757812257161068, -0.0592467719700587, -0.128992483494249, 0.128137179839506, 0.0717566895580845, -0.0833098166158672, -0.155270199765802, 0.14579926213725, 0.0799479942345352, -0.0877579746528331, -0.151806904210309, -0.0144087480818, 0.0045802720538116, 0.012559878300944, 0.0205212058726693, 0.00923383989163264, 0.00887062962888688, 0.0248134478800154, 0.0247920992730687, 0.00555327458708413, 0.00347675808841012, 0.0417778807218433, 0.0130731844584642, 0.0553201434932848, 0.0471580164973155, -0.000204120425811351, 0.0164763439450202, 0.002375688741747, 0.0860106118444142, -0.019900141742653, -0.00853341765654292, -0.0214039745610184, 0.00329365658923978, 0.018798081814193, 0.00581948595103035, -0.00488309654925988, 0.00560377528198599, -0.0120229152759623, 0.00948677799058492, -0.0661029519937833, -0.0412145589031353, 0.107707346627344, 0.000212298869746286, 5.75617068664727e-06, -0.000535439721952491, 0.00279700652525929, -0.0346675510650626, 0.005592058134837, 0.10479954023783, 0.148960920619109, -0.000875250301410178, 0.0072931844882099, -0.00486631793652915, -0.00732081477362901, 0.0428107979498749, 0.0857746926208272, 0.019809047212115, -0.0973040204633905, -0.0315362339684262, -0.0686655886007816, -0.0266860377378277, -0.0693311489782208, -0.00774333873252011, -0.024660772824432, 1.0924386835732, 0.793710751295646, 0.854793847130113, -0.019900141742653, 1.38970316324119,   > test-esem.R: 0.981893200602164, 1.08597201729475, 1.11220858116664, 0.765698559147951, 0.82894666600568, 0.0142986764803992, 0.010614318885733, 0.0160737255995745, -0.0041208933928698, -0.0252830371146104, -0.0176224865986117, 0.0946179973328222, 0.0367164551098905, 0.0204479578807549, -0.0120376783586505, -0.029357693070375, 0.0278921595163239, 0.0487302938257758, 0.0704790601404688, 0.0609038763998929, -0.102613201353469, 0.0359904807552618, 0.0635513532398554, 0.0875644703300589, 0.0357418739580544, 0.0129649274588139, -0.0278335902754632, -0.0424219899345994, 0.00408552460269165, 0.00959998906957744, -0.00837804674737573, 0.0476073305366232, -0.00406552748573637, 0.00213812557707964, 0.800404470692711, 1.05114166617281, 0.798909003312101, -0.00853341765654293, 0.981893200602164, 1.28126824855252, 0.953252779725438, 0.79984192501233, 1.00662688239373, 0.729381828929068, -0.0329019063108192, -0.0136956271030428, -0.0107407756452412, -0.10125465414037, -0.0476247917553443, -0.0380983101009663, 0.127452670358713, -0.00373340788228093, 7.74832431628925e-05, 0.00713013186502569, 0.00385735432182198, -0.00295841798764691, 0.0493669036835192, 0.0725730814762955, 0.134305485249476, 0.000573271171747709, -0.0170014618557995, 0.0254927803908756, -0.00850467831901116, 0.142453716502339, 0.0930336131142227, 0.0775381432887526, -0.10773335775287, -0.000542481453965015, -0.0063427562724809, -0.00682932784787413, -0.00861177497025037, -0.00893407703646892, -0.0216906952994601, 0.866078362448077, 0.785239966039694, 1.08161905449057, -0.0214039745610184, 1.08597201729475, 0.953252779725438, 1.35498763334885, 0.910480398190892, 0.75340726253597, 1.0564303582682, 0.00579222433398088, -0.00895211036399707, 0.0266774382853429, 0.0785862105655182, -0.0264569477560322, -0.0402960408343714, 0.110027033838194, -0.000447770974788044, 0.00318648418314012, 0.00519137622969441, -0.0242062457633788, -0.0143270955539256, -0.0138630042221855, 0.0132397293060142, 0.031169280380637, -0.0360467188781403, -0.0151073514301341, 0.0064493662501605, 0.0127667492155082, 0.0307344604466001, 0.0639141756310739, 0.0170624923090307, -0.0699475831262572, -0.0349182900330659, -0.0513443431595157, -0.0185360321639612, -0.104231047599455, 0.00190480732538636, -0.0148339752411867, 1.06893041162047, 0.790106915227831, 0.881489941991868, 0.00329365658923978, 1.11220858116664, 0.79984192501233, 0.910480398190892, 1.45343080732047, 1.05062470048119, 1.10783285444028, 0.00482548296748707, 0.0192021029400786, 0.0155516790039065, 0.0555335781266458, 0.0323015886413371, -0.0522514103795643, 0.0789431409081487, 0.0150078675242675, 0.0406344952019763, 0.0317853983114957, -0.0532930035886563, 0.0186565372953721, 0.00884439739842633, 0.00392597583562098, -0.0100546599966053, -0.106138266021218, 0.0160490751802889, 0.0984423108227788, 0.17068413621789, 0.0109412500742362, 0.00174847079953263, -0.00828708360663369, -0.0137548007419955, -0.0522477483772834, -0.00110780602245253, 0.00923449433845097, -0.00601795258733618, 0.00711445555663318, 0.018408323287891, 0.76717238801061, 1.02177932037355, 0.812215657741097, 0.018798081814193, 0.765698559147951, 1.00662688239373, 0.75340726253597, 1.05062470048119, 1.30720068817729, 1.01913616621123, -0.0536652115645841, -0.019213861470815, -0.0172908302087426, -0.0638553156597295, -0.0169096775619646, -0.0445701055393561, 0.0904561706745593, -0.0585860797001331, 0.00971501942601363, 0.0120483492539913, -0.00857750561667388, -0.0162965216843151, -0.00367260312735112, 0.00574676565142401, 0.0170376579092655, -0.00157974786814208, 0.00744705291894205, 0.0677157657287604, 0.0662442892600502, 0.0843708232885414, 0.0826773995441732, 0.0575131343085677, -0.0746340366111427, -0.0194906592088833, -0.0123907039927979, 0.0103019254283266, 0.00995636294223919, -0.00993507647126291, -0.0189706498549756, 0.812608776357063, 0.772375497705721, 1.0699033568652, 0.00581948595103035, 0.82894666600568, 0.729381828929068, 1.0564303582682, 1.10783285444028, 1.01913616621123, 1.33101934741115, -0.0160332825788463, -0.0194673047264479, 0.0076134333416248, -0.0280957630388753, -0.00652515275734159, 0.0398832411621319, 0.00997923231952932, -0.00317128543875011, -0.00345081806988481, 0.0335091835712613, 0.0767430961264705, -0.00917673037167745, 0.0351601326522669, 0.0545645893278382, 0.0337745421542386, 0.155628564410312, 0.0506965702279097, -0.0765907638253904, -0.152316211916453, 0.134776830038491, 0.0577716250034614, -0.0862221610584902, -0.14926687959738, 0.0890398368213926, 0.0395883128666614, -0.0913713589057094, -0.110540740333588, -0.00346764772535688, -0.0118850547463466, 5.64931156303779e-06, -0.0457924736654755, -0.0256936941471223, -0.00488309654925988, 0.0142986764803992, -0.0329019063108192, 0.00579222433398088, 0.00482548296748707, -0.0536652115645841, -0.0160332825788463, 0.120861870687388, 0.0659352781421284, 0.0637822024211485, -0.00123067312283201, -0.0245753162929485, 0.005478944302505, -0.00851490425935088, -0.0178213100814595, -0.0155256161093073, -0.0158877338895445, -0.00352146452370504, 0.00379043849646447, 0.00839475638588036, 0.00351526032811938, -0.00021124007570947, 0.153818992474422, 0.0646531323927172, -0.07315026181056, -0.145043021521224, 0.118952680023042, 0.0240332716358964, -0.0960292154420592, -0.122855476743761, 0.131204656804499, 0.0716961735842288, -0.0780111274536073, -0.122682130683992, -0.00584038224322596, -0.00878063137456458, 0.0102626772585488, -0.0294483813254788, -0.0192954594118436, 0.00560377528198599, 0.010614318885733, -0.0136956271030428, -0.00895211036399707, 0.0192021029400786, -0.019213861470815, -0.0194673047264479, 0.0659352781421284, 0.118049819131347, 0.0482278273076368, -0.0342607132374605, -0.0110359863231057, 0.0388495658350893, 0.020452480807499, -0.00791788041762326, 0.0078896468194146, 0.0458021905858184, 0.0679798285981358, 0.00436030761914487, 0.0119231550404147, 0.0482644269885699, 0.0322855829479996, 0.166944494374591, 0.0136332585073733, -0.0918071222196781, -0.119582194867943, 0.141748124176178, 0.0621044192912241, -0.121941490751061, -0.153408764961069, 0.153319351221939, 0.0545351763997117, -0.103938786698107, -0.132968263756207, -0.00432817212430529, -0.00695610027104775, 0.00490334272631464, -0.0249420219318624, 0.00388272127596111, -0.0120229152759623, 0.0160737255995745, -0.0107407756452412, 0.0266774382853429, 0.0155516790039065, -0.0172908302087426, 0.0076134333416248, 0.0637822024211485, 0.0482278273076368, 0.0987931493939915)), NACOV.user = FALSE, missing.flag = FALSE, missing = list(NULL), missing.h1 = list( NULL), YLp = list(NULL), zero.cell.tables = list( c("x1", "x1", "x2", "x4", "x4", "x5", "x2", "x3", "x3", "x5", "x6", "x6"))), model.type = "sem")   > test-esem.R: 10: eval(sc, parent.frame())   > test-esem.R: 11: eval(sc, parent.frame())   > test-esem.R: 12: sem(model = c("efa(\"efa\")*f1 =~ x1 + x2 + x3 + x4 + x5 + x6", "efa(\"efa\")*f2 =~ x1 + x2 + x3 + x4 + x5 + x6", "efa(\"efa\")*f3 =~ x1 + x2 + x3 + x4 + x5 + x6"), data = list(x1 = c(5L, 3L, 4L, 4L, 3L, 4L, 4L, 3L, 5L, 2L, 4L, 5L, 3L, 3L, 3L, 4L, 2L, 1L, 1L, 5L, 3L, 1L, 3L, 5L, 5L, 3L, 3L, 1L, 4L, 2L, 4L, 4L, 4L, 3L, 5L, 1L, 1L, 2L, 1L, 3L, 4L, 2L, 3L, 3L, 1L, 3L, 1L, 5L, 3L, 4L, 4L, 2L, 5L, 4L, 4L, 3L, 3L, 3L, 1L, 4L, 2L, 4L, 4L, 5L, 3L, 5L, 4L, 5L, 3L, 4L, 2L, 3L, 4L, 1L,   > test-esem.R: 3L, 3L, 3L, 4L, 1L, 1L, 5L, 3L, 4L, 3L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 2L, 4L, 5L, 1L, 1L, 1L, 1L, 3L, 4L, 5L, 5L, 1L, 5L, 1L, 3L, 2L, 2L, 3L, 3L, 3L, 4L, 2L, 2L, 1L, 2L, 3L, 5L, 1L, 3L, 1L, 2L, 3L, 2L, 3L, 3L, 3L, 1L, 1L, 3L, 4L, 3L, 3L, 4L, 5L, 2L, 3L, 4L, 3L, 3L, 3L, 3L, 2L, 2L, 1L, 5L, 2L, 2L, 4L, 1L, 2L, 1L, 5L, 3L, 1L, 1L, 3L, 3L, 3L, 5L, 3L, 1L, 3L, 3L, 4L, 5L, 1L, 3L, 3L, 5L, 3L, 3L, 1L, 5L, 5L, 3L, 1L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 4L, 2L, 2L, 3L, 3L, 4L, 1L, 2L, 4L, 2L, 3L, 5L, 3L, 1L, 4L, 5L, 5L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 5L, 4L, 3L, 3L, 3L, 3L, 5L, 2L, 3L, 2L, 3L, 2L, 1L, 3L, 3L, 3L, 1L, 2L, 5L, 3L, 5L, 1L, 3L, 1L, 2L, 5L, 3L, 4L, 1L, 3L, 4L, 4L, 5L, 1L, 5L, 4L, 2L, 5L, 2L, 1L, 3L, 1L, 3L, 5L, 1L, 3L, 3L, 2L, 2L, 4L, 5L, 3L, 1L, 3L, 5L, 5L, 3L, 1L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 3L, 2L, 5L, 4L, 3L, 2L, 3L, 3L, 3L, 5L, 5L, 2L, 1L, 4L, 2L, 5L, 2L, 5L, 2L, 3L, 3L, 4L, 4L, 2L), x2 = c(5L, 2L, 3L, 3L, 5L, 1L, 5L, 3L, 5L, 2L, 5L, 5L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 5L, 3L, 1L, 2L, 5L, 5L, 3L, 2L, 1L, 3L, 3L, 4L, 5L, 5L, 2L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 2L, 5L, 2L, 1L, 4L, 1L, 5L, 3L, 4L, 4L, 3L, 5L, 3L, 3L, 3L, 5L, 4L, 1L, 4L, 3L, 3L, 3L, 5L, 2L, 5L, 5L, 3L, 4L, 5L, 1L, 2L, 4L, 3L, 3L, 4L, 5L, 4L, 2L, 1L, 5L, 3L, 3L, 3L, 1L, 5L, 3L, 5L, 5L, 5L, 5L, 2L, 4L, 5L, 2L, 2L, 1L, 2L, 3L, 3L, 5L, 5L, 2L, 5L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 2L, 1L, 5L, 1L, 3L, 2L, 1L, 4L, 3L, 3L, 3L, 2L, 1L, 1L, 3L, 4L, 3L, 3L, 5L, 5L, 1L, 4L, 5L, 2L, 3L, 2L, 1L, 2L, 1L, 3L, 5L, 2L, 2L, 3L, 2L, 3L, 1L, 5L, 3L, 3L, 1L, 3L, 3L, 2L, 5L, 3L, 1L, 3L, 2L, 3L, 5L, 2L, 3L, 3L, 4L, 3L, 4L, 1L, 5L, 4L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 4L, 3L, 1L, 1L, 3L, 3L, 3L, 1L, 2L, 5L, 4L, 4L, 5L, 5L, 1L, 3L, 4L, 5L, 1L, 2L, 2L, 1L, 3L, 3L, 1L, 5L, 3L, 3L, 4L, 3L, 3L, 5L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 5L, 3L, 4L, 1L, 3L, 3L, 2L, 5L, 3L, 3L, 2L, 1L, 5L, 5L, 5L, 1L, 5L, 4L, 3L, 5L, 1L, 1L, 3L, 2L, 3L, 5L, 2L, 3L, 3L, 1L, 1L, 5L, 4L, 3L, 1L, 3L, 4L, 3L, 2L, 1L, 3L, 4L, 3L, 3L, 3L, 3L, 5L, 4L, 2L, 4L, 5L, 5L, 1L, 2L, 3L, 3L, 3L, 5L, 2L, 1L, 2L, 2L, 4L, 3L, 5L, 3L, 3L, 2L, 3L, 3L, 1L), x3 = c(5L, 2L, 3L, 4L, 4L, 3L, 4L, 4L, 5L, 3L, 4L, 5L, 1L, 3L, 3L, 4L, 4L, 1L, 1L, 5L, 3L, 1L, 3L, 5L, 4L, 2L, 3L, 1L, 4L, 3L, 3L, 4L, 5L, 3L, 3L, 1L, 2L, 2L, 1L, 3L, 3L, 2L, 4L, 2L, 1L, 4L, 3L, 5L, 2L, 4L, 3L, 2L, 5L, 3L, 3L, 3L, 5L, 2L, 1L, 3L, 3L, 3L, 5L, 5L, 1L, 4L, 3L, 5L, 4L, 5L, 2L, 3L, 3L, 1L, 1L, 4L, 4L, 5L, 2L, 2L, 5L, 3L, 3L, 3L, 1L, 4L, 4L, 2L, 5L, 4L, 5L, 2L, 4L, 5L, 1L, 2L, 1L, 2L, 3L, 4L, 4L, 4L, 2L, 5L, 2L, 3L, 3L, 3L, 3L, 2L, 4L, 3L, 3L, 1L, 1L, 2L, 1L, 5L, 2L, 3L, 1L, 2L, 2L, 2L, 3L, 3L, 2L, 1L, 1L, 4L, 5L, 1L, 3L, 5L, 5L, 1L, 3L, 5L, 3L, 3L, 3L, 2L, 1L, 3L, 3L, 4L, 1L, 2L, 5L, 1L, 3L, 1L, 5L, 3L, 3L, 1L, 3L, 2L, 2L, 5L, 2L, 3L, 4L, 2L, 3L, 5L, 1L, 3L, 3L, 3L, 3L, 4L, 1L, 5L, 4L, 3L, 1L, 4L, 4L, 3L, 4L, 2L, 3L, 4L, 3L, 1L, 3L, 3L, 1L, 1L, 3L, 3L, 4L, 1L, 2L, 4L, 3L, 4L, 5L, 2L, 1L, 3L, 5L, 5L, 1L, 2L, 1L, 1L, 3L, 2L, 1L, 5L, 3L, 3L, 3L, 3L, 3L, 5L, 3L, 1L, 4L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 1L, 5L, 2L, 4L, 2L, 3L, 3L, 3L, 5L, 3L, 4L, 1L, 3L, 5L, 5L, 4L, 3L, 5L, 5L, 3L, 4L, 2L, 1L, 3L, 2L, 3L, 5L, 1L, 1L, 3L, 1L, 3L, 3L, 4L, 3L, 1L, 3L, 4L, 5L, 3L, 1L, 3L, 4L, 3L, 3L, 3L, 3L, 5L, 3L, 1L, 5L, 5L, 5L, 1L, 2L, 3L, 3L, 4L, 5L, 1L, 1L, 3L, 3L, 4L, 3L, 5L, 3L, 3L, 3L, 4L, 3L, 1L), x4 = c(3L, 4L, 3L, 5L, 3L, 3L, 4L, 4L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 5L, 3L, 3L, 5L, 2L, 5L, 3L, 1L, 3L, 4L, 2L, 2L, 4L, 5L, 3L, 1L, 1L, 3L, 2L, 2L, 4L, 1L, 1L, 4L, 3L, 4L, 1L, 3L, 3L, 5L, 2L, 4L, 1L, 1L, 4L, 3L, 4L, 2L, 1L, 2L, 1L, 3L, 3L, 3L, 3L, 5L, 2L, 3L, 4L, 5L, 3L, 5L, 2L, 5L, 3L, 3L, 2L, 3L, 5L, 5L, 1L, 4L, 5L, 2L, 2L, 2L, 3L, 5L, 4L, 3L, 3L, 3L, 2L, 3L, 4L, 3L, 3L, 4L, 3L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 5L, 1L, 1L, 3L, 1L, 3L, 2L, 2L, 1L, 3L, 1L, 5L, 4L, 1L, 2L, 2L, 4L, 3L, 2L, 4L, 3L, 3L, 5L, 2L, 2L, 1L, 3L, 5L, 1L, 2L, 2L, 5L, 5L, 3L, 5L, 3L, 5L, 3L, 5L, 3L, 2L, 4L, 1L, 2L, 3L, 2L, 3L, 2L, 5L, 4L, 5L, 2L, 5L, 5L, 4L, 1L, 1L, 3L, 5L, 5L, 1L, 2L, 4L, 1L, 4L, 1L, 3L, 2L, 2L, 3L, 3L, 4L, 3L, 1L, 1L, 1L, 4L, 3L, 3L, 1L, 3L, 5L, 3L, 3L, 3L, 5L, 3L, 4L, 5L, 2L, 4L, 3L, 5L, 4L, 2L, 4L, 2L, 3L, 3L, 2L, 1L, 4L, 4L, 2L, 2L, 3L, 5L, 5L, 1L, 2L, 4L, 3L, 5L, 3L, 4L, 3L, 5L, 4L, 1L, 2L, 5L, 1L, 1L, 1L, 2L, 3L, 2L, 3L, 2L, 3L, 5L, 1L, 2L, 3L, 3L, 5L, 4L, 4L, 4L, 2L, 3L, 5L, 1L, 3L, 3L, 5L, 3L, 3L, 5L, 1L, 5L, 1L, 5L, 3L, 5L, 4L, 2L, 1L, 4L, 3L, 2L, 3L, 2L, 2L, 1L, 1L, 3L, 5L, 5L, 3L, 3L, 4L, 3L, 3L, 2L, 1L, 3L, 1L, 4L, 1L, 2L, 3L, 4L, 3L, 3L, 2L, 3L, 3L, 1L, 5L, 1L, 4L), x5 = c(3L, 4L, 3L, 3L, 1L, 3L, 4L, 5L, 3L, 3L, 3L, 2L, 2L, 3L, 4L, 3L, 2L, 3L, 1L, 5L, 3L, 3L, 3L, 3L, 5L, 2L, 4L, 4L, 2L, 3L, 4L, 3L, 3L, 3L, 5L, 2L, 1L, 1L, 1L, 2L, 4L, 4L, 3L, 2L, 3L, 3L, 5L, 1L, 3L, 5L, 3L, 2L, 4L, 2L, 3L, 5L, 2L, 5L, 3L, 1L, 3L, 2L, 3L, 3L, 3L, 1L, 5L, 1L, 3L, 3L, 5L, 4L, 5L, 1L, 5L, 3L, 3L, 1L, 3L, 5L, 5L, 1L, 4L, 5L, 3L, 4L, 1L, 4L, 5L, 4L, 3L, 3L, 5L, 2L, 3L, 4L, 3L, 3L, 2L, 5L, 5L, 3L, 3L, 4L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 2L, 5L, 4L, 1L, 3L, 1L, 4L, 3L, 2L, 4L, 3L, 3L, 5L, 2L, 2L, 1L, 2L, 5L, 1L, 3L, 1L, 5L, 4L, 3L, 5L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 1L, 5L, 4L, 5L, 2L, 5L, 5L, 3L, 1L, 1L, 3L, 5L, 4L, 1L, 2L, 3L, 1L, 5L, 1L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 1L, 1L, 1L, 4L, 3L, 3L, 1L, 1L, 4L, 3L, 3L, 3L, 5L, 2L, 3L, 5L, 1L, 3L, 3L, 5L, 4L, 2L, 2L, 1L, 3L, 1L, 1L, 1L, 5L, 3L, 3L, 2L, 3L, 5L, 5L, 1L, 3L, 5L, 3L, 5L, 3L, 3L, 4L, 5L, 5L, 2L, 3L, 4L, 1L, 1L, 1L, 2L, 5L, 3L, 3L, 2L, 3L, 5L, 2L, 2L, 2L, 3L, 5L, 3L, 5L, 3L, 2L, 3L, 5L, 1L, 4L, 3L, 4L, 5L, 3L, 5L, 1L, 5L, 1L, 3L, 3L, 5L, 3L, 1L, 1L, 5L, 3L, 2L, 3L, 1L, 2L, 2L, 1L, 3L, 5L, 5L, 4L, 3L, 5L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 4L, 1L, 4L), x6 = c(3L, 5L, 3L, 3L, 3L, 3L, 4L, 5L, 1L, 3L, 3L, 1L, 1L, 2L, 3L, 2L, 2L, 3L, 1L, 4L, 3L, 3L, 3L, 3L, 5L, 2L, 5L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 4L, 3L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 1L, 5L, 4L, 5L, 1L, 4L, 5L, 5L, 1L, 3L, 3L, 3L, 5L, 3L, 4L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 5L, 1L, 3L, 5L, 5L, 3L, 5L, 1L, 5L, 2L, 4L, 1L, 3L, 5L, 5L, 2L, 3L, 5L, 3L, 1L, 3L, 4L, 5L, 5L, 3L, 1L, 3L, 1L, 3L, 5L, 3L, 3L, 2L, 5L, 5L, 2L, 3L, 3L, 3L, 1L, 4L, 5L, 5L, 1L, 2L, 2L, 1L, 3L, 3L, 2L, 1L, 3L, 1L, 5L, 5L, 1L, 4L, 3L, 4L, 4L, 2L, 5L, 3L, 3L, 5L, 2L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 5L, 5L, 3L, 5L, 1L, 4L, 3L, 3L, 3L, 2L, 4L, 1L, 3L, 3L, 2L, 3L, 3L, 5L, 4L, 5L, 2L, 3L, 5L, 3L, 1L, 1L, 3L, 5L, 3L, 1L, 2L, 3L, 1L, 5L, 1L, 3L, 3L, 2L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 5L, 3L, 3L, 2L, 2L, 4L, 3L, 3L, 3L, 5L, 2L, 5L, 4L, 1L, 3L, 4L, 5L, 5L, 3L, 3L, 1L, 3L, 1L, 2L, 1L, 5L, 5L, 2L, 1L, 3L, 5L, 5L, 1L, 2L, 5L, 3L, 5L, 3L, 5L, 4L, 5L, 5L, 1L, 2L, 4L, 1L, 1L, 1L, 4L, 5L, 3L, 3L, 4L, 2L, 5L, 2L, 3L, 2L, 3L, 5L, 3L, 5L, 5L, 3L, 3L, 4L, 1L, 3L, 3L, 5L, 5L, 3L, 5L, 1L, 5L, 1L, 3L, 3L, 5L, 3L, 1L, 1L, 5L, 3L, 3L, 3L, 2L, 3L, 1L, 1L, 3L, 5L, 5L, 3L, 3L, 4L, 3L, 3L, 1L, 1L, 5L, 1L, 5L, 1L, 1L, 3L, 3L, 3L, 4L, 2L, 2L, 3L, 3L, 5L, 1L, 5L)), sample_cov = NULL, sample_nobs = NULL, rotation = "varimax", rotation.args = list(), bounds = "pos.var", cmd = "efa", estimator = "WLSMV", ordered = c("x1", "x2", "x3", "x4", "x5", "x6"), missing = "available.cases", slot.sample.stats = new("lavSampleStats", var = list(c(1, 1, 1, 1, 1, 1)), cov = list(c(1, 0.847708645568633, 0.852266877770598, -0.045025067439856, 0.0187792727635132, -0.0528665611502654, 0.847708645568633, 1, 0.866098037290729, -0.00562422746118933, 0.0637607092511551, 0.00299593013986168, 0.852266877770598, 0.866098037290729, 1, -0.0468064624916715, 0.0271693531291436, -0.0307059937186903, -0.045025067439856, -0.00562422746118933, -0.0468064624916715, 1, 0.854835831983838, 0.8624962089988, 0.0187792727635132, 0.0637607092511551, 0.0271693531291436, 0.854835831983838, 1, 0.864362516433594, -0.0528665611502654, 0.00299593013986168, -0.0307059937186903, 0.8624962089988, 0.864362516433594, 1)), mean = list( c(0, 0, 0, 0, 0, 0)), th = list(c(-0.954165253146194, -0.467698799114508, 0.449136303439425, 1.00825234717068, -0.941074530352976, -0.439913165673234, 0.524400512708041, 0.853587957511572, -0.902734791643865, -0.458397807359904, 0.467698799114508, 0.994457883209753, -1.00825234717068, -0.394335532100823, 0.486423874628588, 0.967421566101701, -0.841621233572914, -0.412463129441405, 0.553384719555673, 0.915365087842814, -0.80642124701824, -0.458397807359904, 0.505321088513851, 0.760983928488951)), th.idx = list( c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L)), th.names = list(c("x1|t1", "x1|t2", "x1|t3", "x1|t4", "x2|t1", "x2|t2", "x2|t3", "x2|t4", "x3|t1", "x3|t2", "x3|t3", "x3|t4", "x4|t1", "x4|t2", "x4|t3", "x4|t4", "x5|t1", "x5|t2", "x5|t3", "x5|t4", "x6|t1", "x6|t2", "x6|t3", "x6|t4")), res.cov = list(NULL), res.var = list( NULL), res.th = list(NULL), res.th.nox = list(NULL), res.slopes = list(NULL), res.int = list(NULL), mean.x = list( NULL), cov.x = list(NULL), bifreq = list(NULL), group.w = list( 1), nobs = list(300L), ntotal = 300L, ngroups = 1L, x.idx = list(integer(0)), icov = list(NULL), cov.log.det = list( NULL), res.icov = list(NULL), res.cov.log.det = list( NULL), ridge = 0, WLS.obs = list(c(-0.954165253146194, -0.467698799114508, 0.449136303439425, 1.00825234717068, -0.941074530352976, -0.439913165673234, 0.524400512708041, 0.853587957511572, -0.902734791643865, -0.458397807359904, 0.467698799114508, 0.994457883209753, -1.00825234717068, -0.394335532100823, 0.486423874628588, 0.967421566101701, -0.841621233572914, -0.412463129441405, 0.553384719555673, 0.915365087842814, -0.80642124701824, -0.458397807359904, 0.505321088513851, 0.760983928488951, 0.847708645568633, 0.852266877770598, -0.045025067439856, 0.0187792727635132, -0.0528665611502654, 0.866098037290729, -0.00562422746118933, 0.0637607092511551, 0.00299593013986168, -0.0468064624916715, 0.0271693531291436, -0.0307059937186903, 0.854835831983838, 0.8624962089988, 0.864362516433594)), WLS.V = list(NULL), WLS.VD = list(c(0.45383482142755, 0.587709518619898, 0.591395244765584, 0.435865310988534, 0.458128368522494,   > test-esem.R: 0.593178170698077, 0.575667399853721, 0.486139871182117, 0.470559146996906, 0.589572474383863, 0.587709518619898, 0.440480678601174, 0.435865310988534, 0.601506349600645, 0.583861633032411, 0.449463546185201, 0.489866730490175, 0.598291135869332, 0.569072720318582, 0.466488951106186, 0.500662923226088, 0.589572474383863, 0.579848639230085, 0.514201673558285, 7.44280585633078, 6.75326246420858, 0.68465337775283, 0.747098037875909, 0.711965105740159, 11.6264723451673, 0.71957812751013, 0.780476688725976, 0.738014115692331, 0.688027249018883, 0.764993477317059, 0.751303879951114, 8.27390801013265, 8.47099984869401, 10.1221593413523)), NACOV = list(c(2.20344484994446, 1.2774226485315, 0.608464081144384, 0.438580200934569, 1.24211958837833, 1.06100103747073, 0.579645270096896, 0.476730975188745, 1.17367332415033, 1.00896404529906, 0.601140069897175, 0.44176715543208, -0.219015644772081, -0.0242678714026954, -0.0379083822845332, 0.079081810080948, -0.0564610061408481, -0.120072924149705, 0.107363949033218, 0.109435686051998, -0.169568169571692, -0.238028958705595, -0.0135053916825915, -0.0269051436957956, 0.148317263761989, 0.15574465153135, -0.0301086318650708, 0.00933114409349929, -0.0308508864967571, 0.110777263526235, 0.00948677799058494, -0.00412089339286978, -0.10125465414037, 0.0785862105655183, 0.0555335781266458, -0.0638553156597295, -0.0280957630388753, -0.00123067312283201, -0.0342607132374605, 1.2774226485315, 1.70152085055262, 0.810471242285935, 0.584186727382364, 1.14089096616285, 1.11498605030749, 0.74527622661719, 0.635003375993402, 1.06755750714773, 1.03707982565867, 0.748585766358322, 0.588431735511437, -0.195764701197282, -0.057578351381687, -0.0504937836682346, -0.0373067097809645, 0.0599296137472571, -0.0417199784040522, 0.0501041505326083, 0.0809914034723241, -0.102201933410529, -0.130802860893886, -0.0148660064484897, 0.0449437782925636, 0.0741651928462058, 0.0490907248507851, -0.0176892046762685, 0.00394541784101921, -0.00847157050708947, 0.0757812257161068, -0.0661029519937833, -0.0252830371146104, -0.0476247917553443, -0.0264569477560322, 0.0323015886413371, -0.0169096775619646, -0.00652515275734159, -0.0245753162929485, -0.0110359863231057, 0.608464081144384, 0.810471242285935, 1.69091653822204, 1.21881067112823, 0.576674081941792, 0.774285253955362, 1.0792024722852, 1.05804201981214, 0.625589760454923, 0.815394330786237, 1.12783689455862, 1.15169768636051, 0.0136079556709071, 0.0243719568965317, -0.0184270459355523, -0.0493207998636407, 0.0858315089017964, 0.0837418857774159, -0.0113399097133595, 0.0479013123640107, 0.0827363125250564, -0.0337960621007554, -0.0540468233753761, 0.0344537680314544, -0.0835035702275452, -0.0786770150818312, 0.0106760261359684, -0.00123494659659294, 0.0122565252286696, -0.0592467719700587, -0.0412145589031354, -0.0176224865986117, -0.0380983101009663, -0.0402960408343714, -0.0522514103795643, -0.0445701055393561, 0.0398832411621319, 0.005478944302505, 0.0388495658350893, 0.438580200934569, 0.584186727382364, 1.21881067112823, 2.29428673213755, 0.441666799275109, 0.594893883378979, 1.19451162375741, 1.29095297173797, 0.450924370633182, 0.587735283838845, 1.16371239045051, 1.39753600268651, 0.0789137392518928, 0.0110390707298294, -0.146050804979038, -0.157518417294168, 0.0198461232749442, -0.0386675911959832, -0.106723079606281, 0.0285854186705204, -0.00626559185412115, -0.0310691387157628, -0.135030448940603, -0.116121963366209, -0.159334208125366, -0.139085742363334, 0.0271974707303992, -0.0071037068500013, 0.0263069574461733, -0.128992483494249, 0.107707346627344, 0.0946179973328222, 0.127452670358713, 0.110027033838194, 0.0789431409081486, 0.0904561706745593, 0.00997923231952932, -0.00851490425935088, 0.020452480807499, 1.24211958837833, 1.14089096616285, 0.576674081941792, 0.441666799275109, 2.18279431859916, 1.25161307106614, 0.58372464286605, 0.480086067447278, 1.29726260924579, 0.984812668193073, 0.605370716739469, 0.444876182620165, -0.170594704793659, -0.000939948520669462, -0.099353327882025, -0.121499556217292, 0.120824019421084, 0.0113621668615782, -0.0729739856394577, -0.0317318220007143, -0.0866730771247237, -0.174358692112653, -0.0760810863132528, -0.0604073968525699, 0.16194297708138, 0.120827352177701, -0.0919049501204477, -0.0267557831346448, -0.0881523522151011, 0.128137179839506, 0.000212298869746207, 0.0367164551098905, -0.00373340788228093, -0.000447770974788038, 0.0150078675242675, -0.0585860797001331, -0.0031712854387501, -0.0178213100814595, -0.00791788041762326, 1.06100103747073, 1.11498605030749, 0.774285253955362, 0.594893883378979, 1.25161307106614, 1.68583412100813, 0.786235732883992, 0.64664191532757, 1.13913953707982, 1.17862045059145, 0.81539146061268, 0.599216695337057, -0.134627822737602, -0.0578543768292243, -0.0254499315393859, -0.0184196260647881, 0.0394524588887727, -0.0165793698112683, 0.0190914135644343, 0.169073277814853, -0.057167462611575, -0.154022807307125, 0.0880784013810653, 0.0342100668274375, 0.0539361700938887, 0.0615567447500341, -0.0681981562446394, -0.0246495398767953, -0.0554549756281441, 0.0717566895580845, 5.75617068663093e-06, 0.0204479578807549, 7.74832431628764e-05, 0.00318648418314011, 0.0406344952019763, 0.00971501942601362, -0.00345081806988481, -0.0155256161093073, 0.0078896468194146, 0.579645270096895, 0.74527622661719, 1.0792024722852, 1.19451162375741, 0.58372464286605, 0.786235732883992, 1.73711417435502, 1.42869471567676, 0.595959822290149, 0.670001492928105, 1.18493558332661, 1.16630442958261, 0.163795908274427, 0.109091022310938, -0.00540981015134475, -1.57046074786481e-16, 0.102731959227697, 0.146522255839915, 0.0252065688162364, 0.138836295431548, 0.0964684652932333, 0.0827491883696062, 0.120136315645039, -0.00321012859252077, -0.0858800421247186, -0.0451906419084792, -0.0765124055981449, -0.0495654183174262, -0.0848028980005815, -0.0833098166158672, -0.000535439721952539, -0.0120376783586505, 0.00713013186502567, 0.00519137622969441, 0.0317853983114957, 0.0120483492539913, 0.0335091835712613, -0.0158877338895445, 0.0458021905858184, 0.476730975188745, 0.635003375993402, 1.05804201981214, 1.29095297173797, 0.480086067447278, 0.64664191532757, 1.42869471567676, 2.05702115641813, 0.490148927129519, 0.571882388380539, 1.21484756042805, 1.46125569212401, 0.212680617707058, 0.145119741737691, -0.0165152518961889, -0.0882562149935091, 0.206218263545022, 0.166099245009795, -0.00386514223473117, 0.0174182527862748, 0.0997440147141751, 0.103034832027179, 0.0653127512676339, -0.0071150608221477, -0.134071027660114, -0.0976632797843361, -0.137740647801462, -0.129333918235308, -0.117770415220244, -0.155270199765802, 0.00279700652525927, -0.029357693070375, 0.00385735432182198, -0.0242062457633788, -0.0532930035886563, -0.00857750561667388, 0.0767430961264705, -0.00352146452370503, 0.0679798285981358, 1.17367332415033, 1.06755750714773, 0.625589760454923, 0.450924370633182, 1.29726260924579, 1.13913953707982, 0.595959822290149, 0.49014892712952, 2.1251313599618, 1.30132191861677, 0.618059609401317, 0.454201024362567, -0.241597776915651, -0.0362922311272902, -0.098028891044111, -0.142411540832355, 0.0448569291718996, -0.0239915790387723, -0.0385217266559336, 0.0430732015144227, -0.111115021635821, -0.132288435076582, -0.0047687613849894, -0.0301359480319713, 0.138783846835874, 0.13451304238041, -0.0723053721025387, 0.0235559639025693, -0.0229809041838941, 0.14579926213725, -0.0346675510650626, 0.0278921595163239, -0.0029584179876469, -0.0143270955539256, 0.0186565372953722, -0.0162965216843151, -0.00917673037167745, 0.00379043849646447, 0.00436030761914487, 1.00896404529906, 1.03707982565867, 0.815394330786237, 0.587735283838845, 0.984812668193073, 1.17862045059145, 0.670001492928105, 0.571882388380539, 1.30132191861677, 1.69614431380138, 0.805579524235362, 0.592006077646191, -0.0849566905132264, 0.0345326507924104, -0.042071892364949, -0.0309553186878741, 0.119344126776945, 0.0764956513097508, -0.0235887381762751, 0.0516400329150373, 0.0202881363683058, -0.0352304618528652, -0.0595609077281687, 0.0516911215418863, 0.0687933674969333, 0.0369020237224323, 0.00995076809763176, 0.064092949342599, 0.0369408306412495, 0.0799479942345352, 0.005592058134837, 0.0487302938257757, 0.0493669036835191, -0.0138630042221855, 0.00884439739842633, -0.00367260312735112, 0.0351601326522669, 0.00839475638588036, 0.0119231550404147, 0.601140069897175, 0.748585766358322, 1.12783689455862, 1.16371239045051, 0.605370716739469, 0.81539146061268, 1.18493558332661, 1.21484756042805, 0.618059609401317, 0.805579524235362, 1.70152085055262, 1.2504174379618, 0.118080295960265, 0.0828320142683918, -0.133598135955537, -0.186533548904822, 0.0732473056910928, 0.0671589896260352, -0.104565183720226, 0.0255762326754712, 0.0375171654291813, -0.0508677792365115, -0.0647733138112737, -0.0137328211449501, -0.0724591490523034, -0.0791267384446038, 0.00181309216736721, -0.0214221821010069, -0.0300017587548346, -0.0877579746528331, 0.10479954023783, 0.0704790601404688, 0.0725730814762955, 0.0132397293060142, 0.00392597583562099, 0.00574676565142401, 0.0545645893278382, 0.00351526032811938, 0.0482644269885699, 0.44176715543208, 0.588431735511437, 1.15169768636051, 1.39753600268651, 0.444876182620165, 0.599216695337057, 1.16630442958261, 1.46125569212401, 0.454201024362567, 0.592006077646191, 1.2504174379618, 2.27024713813936, 0.200952889275185, -0.0133620970402425, -0.0788523417871815, -0.219328304783088, 0.176164832903571, 0.0493539480970899, -0.116865540995329, 0.0187955189173209, 0.00380285492607957, -0.0183094663189546, -0.0671092644366832, -0.0330283840404653, -0.139306280370751, -0.13875441713431, 0.0668223880050227, -0.0448927653441914, 0.0318365551567394, -0.151806904210309, 0.148960920619109, 0.0609038763998929, 0.134305485249476, 0.031169280380637, -0.0100546599966053, 0.0170376579092655, 0.0337745421542386, -0.000211240075709474, 0.0322855829479996, -0.219015644772081, -0.195764701197282, 0.0136079556709071, 0.0789137392518928, -0.170594704793659, -0.134627822737602, 0.163795908274427,   > test-esem.R: 0.212680617707058, -0.241597776915651, -0.0849566905132264, 0.118080295960265, 0.200952889275185, 2.29428673213755, 1.15558999503623, 0.577148887654197, 0.435492094872112, 1.27015188959644, 1.06222147579554, 0.553093374537492, 0.447838225838153, 1.35577768351094, 1.11397687818633, 0.570187169159342, 0.488208361335366, -0.0452073659278695, -0.0368039242195606, -0.0249465575779013, -0.157562953805574, -0.0510773841561005, -0.0144087480818, -0.000875250301410177, -0.102613201353469, 0.000573271171747722, -0.0360467188781403, -0.106138266021219, -0.00157974786814207, 0.155628564410312, 0.153818992474422, 0.166944494374591, -0.0242678714026954, -0.057578351381687, 0.0243719568965317, 0.0110390707298294, -0.000939948520669464, -0.0578543768292243, 0.109091022310938, 0.145119741737691, -0.0362922311272901, 0.0345326507924104, 0.0828320142683918, -0.0133620970402424, 1.15558999503623, 1.66249284095492, 0.830316892679672, 0.626521943879109, 0.974188874780071, 1.1002507213572, 0.742943487462754, 0.64428374039232, 1.10176290111632, 1.11577341589458, 0.743140939070639, 0.702362350920955, -0.0228496292950282, -0.00169230710749049, -0.0100558231103412, -0.00378787104048013, -0.0243505783648102, 0.0045802720538116, 0.0072931844882099, 0.0359904807552618, -0.0170014618557995, -0.0151073514301341, 0.0160490751802889, 0.00744705291894205, 0.0506965702279097, 0.0646531323927172, 0.0136332585073733, -0.0379083822845332, -0.0504937836682345, -0.0184270459355523, -0.146050804979038, -0.099353327882025, -0.0254499315393859, -0.00540981015134475, -0.0165152518961889, -0.098028891044111, -0.042071892364949, -0.133598135955537, -0.0788523417871815, 0.577148887654197, 0.830316892679671, 1.71273456487676, 1.29235692829569, 0.631548022402756, 0.768991594381702, 1.17427537880214, 1.1139472986559, 0.644167387922915, 0.76968868646083, 1.18316692085857, 1.25985049764384, -0.0497100849928933, 0.0114243324019316, 0.0145262770963222, 0.0972680602138176, 0.0637726699623928, 0.012559878300944, -0.00486631793652915, 0.0635513532398554, 0.0254927803908756, 0.00644936625016048, 0.0984423108227788, 0.0677157657287604, -0.0765907638253904, -0.07315026181056, -0.0918071222196781, 0.0790818100809479, -0.0373067097809646, -0.0493207998636407, -0.157518417294168, -0.121499556217292, -0.0184196260647881, -1.83037594978595e-16, -0.0882562149935091, -0.142411540832355, -0.0309553186878741, -0.186533548904822, -0.219328304783088, 0.435492094872112, 0.626521943879109, 1.29235692829569, 2.22487453874168, 0.476539376877921, 0.618983350337493, 1.14976137667066, 1.42361294424794, 0.486061415374205, 0.600533182544757, 1.05121838213894, 1.15403067874422, -0.0158659044150101, 0.0563346056670746, 0.0236919740312106, 0.103285956119985, -0.000130894536512416, 0.0205212058726693, -0.007320814773629, 0.0875644703300589, -0.00850467831901114, 0.0127667492155082, 0.17068413621789, 0.0662442892600502, -0.152316211916453, -0.145043021521224, -0.119582194867943, -0.056461006140848, 0.0599296137472572, 0.0858315089017964, 0.0198461232749443, 0.120824019421084, 0.0394524588887727, 0.102731959227697, 0.206218263545022, 0.0448569291718996, 0.119344126776945, 0.0732473056910928, 0.176164832903571, 1.27015188959644, 0.974188874780071, 0.631548022402756, 0.476539376877921, 2.04137153588563, 1.28678875419635, 0.605225158300069, 0.490049191698183, 1.2559069292527, 1.04757622393096, 0.623930127537152, 0.534224411962442, -0.00822479371015104, 0.00663271019471845, 0.019465008972702, 0.00885836454286383, 0.0422893759118978, 0.00923383989163264, 0.0428107979498749, 0.0357418739580545, 0.142453716502339, 0.0307344604466001, 0.0109412500742362, 0.0843708232885414, 0.134776830038491, 0.118952680023042, 0.141748124176178, -0.120072924149705, -0.0417199784040522, 0.0837418857774159, -0.0386675911959832, 0.0113621668615782, -0.0165793698112683, 0.146522255839915, 0.166099245009794, -0.0239915790387723, 0.0764956513097508, 0.0671589896260352, 0.0493539480970899, 1.06222147579554, 1.1002507213572, 0.768991594381703, 0.618983350337493, 1.28678875419635, 1.67142706961048, 0.786135027597283, 0.636531428934211, 1.09154115621558, 1.11501277174014, 0.758613312330241, 0.693911211524711, -0.00264628084991393, -0.020670859392103, 0.0206585540721674, 0.0076120270915056, 0.01977840827582, 0.00887062962888688, 0.0857746926208271, 0.0129649274588139, 0.0930336131142227, 0.0639141756310739, 0.00174847079953262, 0.0826773995441732, 0.0577716250034614, 0.0240332716358964, 0.062104419291224, 0.107363949033218, 0.0501041505326083, -0.0113399097133595, -0.106723079606281, -0.0729739856394577, 0.0190914135644343, 0.0252065688162364, -0.00386514223473116, -0.0385217266559336, -0.0235887381762751, -0.104565183720226, -0.116865540995329, 0.553093374537492, 0.742943487462754, 1.17427537880214, 1.14976137667066, 0.605225158300069, 0.786135027597283, 1.75724466187761, 1.42283630209259, 0.549741259193713, 0.708475550052955, 1.22914973668607, 1.25763749578798, -0.0296908946602319, 0.0104200414391291, -0.0145720736487281, 0.0033195334097242, 0.0304989245705094, 0.0248134478800154, 0.019809047212115, -0.0278335902754632, 0.0775381432887526, 0.0170624923090307, -0.00828708360663368, 0.0575131343085677, -0.0862221610584902, -0.0960292154420592, -0.121941490751061, 0.109435686051998, 0.0809914034723241, 0.0479013123640107, 0.0285854186705205, -0.0317318220007143, 0.169073277814853, 0.138836295431548, 0.0174182527862748, 0.0430732015144227, 0.0516400329150374, 0.0255762326754712, 0.0187955189173209, 0.447838225838153, 0.64428374039232, 1.1139472986559, 1.42361294424794, 0.490049191698183, 0.636531428934211, 1.42283630209259, 2.14367349457838, 0.499841178456957, 0.617558201846951, 1.24599523308296, 1.40112741396321, -0.0394721249953512, 0.0370330313771152, -0.0431359339076604, -0.00738449211276925, -0.0854782307059588, 0.0247920992730687, -0.0973040204633906, -0.0424219899345994, -0.10773335775287, -0.0699475831262572, -0.0137548007419955, -0.0746340366111427, -0.14926687959738, -0.122855476743761, -0.153408764961069, -0.169568169571692, -0.102201933410529, 0.0827363125250564, -0.00626559185412116, -0.0866730771247237, -0.057167462611575, 0.0964684652932333, 0.0997440147141751, -0.111115021635821, 0.0202881363683058, 0.0375171654291814, 0.00380285492607959, 1.35577768351094, 1.10176290111632, 0.644167387922915, 0.486061415374205, 1.2559069292527, 1.09154115621558, 0.549741259193713, 0.499841178456957, 1.99735181817812, 1.37283056092203, 0.636397275021004, 0.544899092090007, -0.03708872669238, -0.00935414748115647, -0.00786735696938561, -0.0369559317351903, -0.0235867391754614, 0.00555327458708414, -0.0315362339684262, 0.00408552460269166, -0.000542481453965008, -0.0349182900330659, -0.0522477483772834, -0.0194906592088833, 0.0890398368213926, 0.131204656804499, 0.153319351221939, -0.238028958705595, -0.130802860893886, -0.0337960621007554, -0.0310691387157628, -0.174358692112653, -0.154022807307125, 0.0827491883696063, 0.103034832027179, -0.132288435076582, -0.0352304618528652, -0.0508677792365115, -0.0183094663189545, 1.11397687818633, 1.11577341589458, 0.76968868646083, 0.600533182544757, 1.04757622393096, 1.11501277174014, 0.708475550052955, 0.617558201846951, 1.37283056092203, 1.69614431380138, 0.786274468293161, 0.673227653107653, -0.0249870847892577, -0.0310623448791116, -0.0597662654253912, -0.0500516446493022, -0.0205554406398311, 0.00347675808841012, -0.0686655886007817, 0.00959998906957744, -0.00634275627248089, -0.0513443431595157, -0.00110780602245252, -0.0123907039927979, 0.0395883128666614, 0.0716961735842288, 0.0545351763997118, -0.0135053916825915, -0.0148660064484897, -0.0540468233753762, -0.135030448940603, -0.0760810863132528, 0.0880784013810652, 0.120136315645039, 0.0653127512676339, -0.00476876138498948, -0.0595609077281687, -0.0647733138112737, -0.0671092644366832, 0.570187169159342, 0.743140939070639, 1.18316692085857, 1.05121838213894, 0.623930127537152, 0.758613312330241, 1.22914973668607, 1.24599523308296, 0.636397275021004, 0.786274468293161, 1.72458799132095, 1.47663490650412, -0.0171591736580541, 0.0168446332834493, 0.0156862359188135, 0.033364333452295, 0.017604243021788, 0.0417778807218433, -0.0266860377378277, -0.00837804674737573, -0.00682932784787414, -0.0185360321639612, 0.00923449433845097, 0.0103019254283266, -0.0913713589057094, -0.0780111274536073, -0.103938786698107, -0.0269051436957957, 0.0449437782925636, 0.0344537680314544, -0.116121963366209,   > test-esem.R: -0.0604073968525698, 0.0342100668274374, -0.00321012859252068, -0.0071150608221477, -0.0301359480319713, 0.0516911215418863, -0.0137328211449501, -0.0330283840404653, 0.488208361335366, 0.702362350920954, 1.25985049764384, 1.15403067874422, 0.534224411962442, 0.693911211524711, 1.25763749578798, 1.40112741396321, 0.544899092090007, 0.673227653107653, 1.47663490650412, 1.94476224295417, -0.0600572970215252, -0.0129462981571512, -0.0383334777746061, 0.0618573160951042, 0.0275218234991843, 0.0130731844584642, -0.0693311489782209, 0.0476073305366232, -0.00861177497025037, -0.104231047599455, -0.00601795258733618, 0.00995636294223921, -0.110540740333587, -0.122682130683992, -0.132968263756207, 0.148317263761989, 0.0741651928462058, -0.0835035702275452, -0.159334208125366, 0.161942977081381, 0.0539361700938887, -0.0858800421247186, -0.134071027660114, 0.138783846835874, 0.0687933674969333, -0.0724591490523034, -0.139306280370751, -0.0452073659278695, -0.0228496292950282, -0.0497100849928933, -0.0158659044150101, -0.00822479371015103, -0.00264628084991393, -0.0296908946602319, -0.0394721249953512, -0.03708872669238, -0.0249870847892577, -0.0171591736580541, -0.0600572970215252, 0.13435793157891, 0.0648999991313963, 0.0163257727891063, 0.0222001474813885, 0.00586415479170394, 0.0553201434932848, -0.00774333873252011, -0.00406552748573637, -0.00893407703646892, 0.00190480732538636, 0.00711445555663318, -0.00993507647126291, -0.00346764772535688, -0.00584038224322596, -0.0043281721243053, 0.15574465153135, 0.0490907248507852, -0.0786770150818312, -0.139085742363334, 0.120827352177701, 0.0615567447500341, -0.0451906419084792, -0.0976632797843361, 0.13451304238041, 0.0369020237224324, -0.0791267384446038, -0.13875441713431, -0.0368039242195606, -0.00169230710749049, 0.0114243324019316, 0.0563346056670746, 0.00663271019471846, -0.020670859392103, 0.0104200414391291, 0.0370330313771152, -0.00935414748115647, -0.0310623448791116, 0.0168446332834493, -0.0129462981571512, 0.0648999991313963, 0.148076578586997, 0.0102311998810714, 0.0360677109152718, 0.0157559499489283, 0.0471580164973155, -0.024660772824432, 0.00213812557707964, -0.0216906952994601, -0.0148339752411867, 0.018408323287891, -0.0189706498549756, -0.0118850547463466, -0.00878063137456458, -0.00695610027104775, -0.0301086318650708, -0.0176892046762685, 0.0106760261359684, 0.0271974707303992, -0.0919049501204477, -0.0681981562446394, -0.0765124055981449, -0.137740647801462, -0.0723053721025387, 0.00995076809763175, 0.00181309216736721, 0.0668223880050227, -0.0249465575779013, -0.0100558231103412, 0.0145262770963222, 0.0236919740312106, 0.0194650089727021, 0.0206585540721674, -0.0145720736487281, -0.0431359339076604, -0.00786735696938561, -0.0597662654253913, 0.0156862359188135, -0.0383334777746061, 0.0163257727891063, 0.0102311998810714, 1.46059310082162, 1.05092773064802, 1.14570089017992, -0.000204120425811351, 1.0924386835732, 0.800404470692711, 0.866078362448077, 1.06893041162047, 0.767172388010609, 0.812608776357063, 5.64931156303635e-06, 0.0102626772585488, 0.00490334272631464, 0.00933114409349929, 0.00394541784101922, -0.00123494659659295, -0.0071037068500013, -0.0267557831346448, -0.0246495398767953, -0.0495654183174262, -0.129333918235308, 0.0235559639025693, 0.064092949342599, -0.0214221821010069, -0.0448927653441914, -0.157562953805574, -0.00378787104048014, 0.0972680602138176, 0.103285956119985, 0.00885836454286384, 0.00761202709150559, 0.00331953340972421, -0.00738449211276925, -0.0369559317351903, -0.0500516446493022, 0.033364333452295, 0.0618573160951042, 0.0222001474813885, 0.0360677109152718, 1.05092773064801, 1.33851241644687, 1.0552643434031, 0.0164763439450202, 0.793710751295646, 1.05114166617281, 0.785239966039694, 0.790106915227831, 1.02177932037355, 0.772375497705721, -0.0457924736654755, -0.0294483813254788, -0.0249420219318624, -0.0308508864967572, -0.00847157050708947, 0.0122565252286696, 0.0263069574461733, -0.0881523522151011, -0.0554549756281442, -0.0848028980005815, -0.117770415220244, -0.0229809041838941, 0.0369408306412495, -0.0300017587548346, 0.0318365551567393, -0.0510773841561005, -0.0243505783648102, 0.0637726699623928, -0.000130894536512413, 0.0422893759118978, 0.01977840827582, 0.0304989245705094, -0.0854782307059588, -0.0235867391754614, -0.0205554406398311, 0.017604243021788, 0.0275218234991843, 0.00586415479170394, 0.0157559499489283, 1.14570089017992, 1.0552643434031, 1.40456321796895, 0.002375688741747, 0.854793847130113, 0.798909003312101, 1.08161905449057, 0.881489941991868, 0.812215657741097, 1.0699033568652, -0.0256936941471223, -0.0192954594118436, 0.00388272127596111, 0.110777263526235, 0.0757812257161068, -0.0592467719700587, -0.128992483494249, 0.128137179839506, 0.0717566895580845, -0.0833098166158672, -0.155270199765802, 0.14579926213725, 0.0799479942345352, -0.0877579746528331, -0.151806904210309, -0.0144087480818, 0.0045802720538116, 0.012559878300944, 0.0205212058726693, 0.00923383989163264, 0.00887062962888688, 0.0248134478800154, 0.0247920992730687, 0.00555327458708413, 0.00347675808841012, 0.0417778807218433, 0.0130731844584642, 0.0553201434932848, 0.0471580164973155, -0.000204120425811351, 0.0164763439450202, 0.002375688741747, 0.0860106118444142, -0.019900141742653, -0.00853341765654292, -0.0214039745610184, 0.00329365658923978, 0.018798081814193, 0.00581948595103035, -0.00488309654925988, 0.00560377528198599, -0.0120229152759623, 0.00948677799058492, -0.0661029519937833, -0.0412145589031353, 0.107707346627344, 0.000212298869746286, 5.75617068664727e-06, -0.000535439721952491, 0.00279700652525929, -0.0346675510650626, 0.005592058134837, 0.10479954023783, 0.148960920619109, -0.000875250301410178, 0.0072931844882099, -0.00486631793652915, -0.00732081477362901, 0.0428107979498749, 0.0857746926208272, 0.019809047212115, -0.0973040204633905, -0.0315362339684262, -0.0686655886007816, -0.0266860377378277, -0.0693311489782208, -0.00774333873252011, -0.024660772824432, 1.0924386835732, 0.793710751295646, 0.854793847130113, -0.019900141742653, 1.38970316324119, 0.981893200602164, 1.08597201729475, 1.11220858116664, 0.765698559147951, 0.82894666600568, 0.0142986764803992, 0.010614318885733, 0.0160737255995745, -0.0041208933928698, -0.0252830371146104, -0.0176224865986117, 0.0946179973328222, 0.0367164551098905, 0.0204479578807549, -0.0120376783586505, -0.029357693070375, 0.0278921595163239, 0.0487302938257758, 0.0704790601404688, 0.0609038763998929, -0.102613201353469, 0.0359904807552618, 0.0635513532398554, 0.0875644703300589, 0.0357418739580544, 0.0129649274588139, -0.0278335902754632, -0.0424219899345994, 0.00408552460269165, 0.00959998906957744, -0.00837804674737573, 0.0476073305366232, -0.00406552748573637, 0.00213812557707964, 0.800404470692711, 1.05114166617281, 0.798909003312101, -0.00853341765654293, 0.981893200602164, 1.28126824855252, 0.953252779725438, 0.79984192501233, 1.00662688239373, 0.729381828929068, -0.0329019063108192, -0.0136956271030428, -0.0107407756452412, -0.10125465414037, -0.0476247917553443, -0.0380983101009663, 0.127452670358713, -0.00373340788228093, 7.74832431628925e-05, 0.00713013186502569, 0.00385735432182198, -0.00295841798764691, 0.0493669036835192, 0.0725730814762955, 0.134305485249476, 0.000573271171747709, -0.0170014618557995, 0.0254927803908756, -0.00850467831901116, 0.142453716502339, 0.0930336131142227, 0.0775381432887526, -0.10773335775287, -0.000542481453965015, -0.0063427562724809, -0.00682932784787413, -0.00861177497025037, -0.00893407703646892, -0.0216906952994601, 0.866078362448077, 0.785239966039694, 1.08161905449057, -0.0214039745610184, 1.08597201729475, 0.953252779725438, 1.35498763334885, 0.910480398190892, 0.75340726253597, 1.0564303582682, 0.00579222433398088, -0.00895211036399707, 0.0266774382853429, 0.0785862105655182, -0.0264569477560322, -0.0402960408343714, 0.110027033838194, -0.000447770974788044, 0.00318648418314012, 0.00519137622969441, -0.0242062457633788, -0.0143270955539256, -0.0138630042221855, 0.0132397293060142, 0.031169280380637, -0.0360467188781403, -0.0151073514301341, 0.0064493662501605, 0.0127667492155082, 0.0307344604466001, 0.0639141756310739, 0.0170624923090307, -0.0699475831262572, -0.0349182900330659, -0.0513443431595157, -0.0185360321639612, -0.104231047599455, 0.00190480732538636, -0.0148339752411867, 1.06893041162047, 0.790106915227831, 0.881489941991868, 0.00329365658923978, 1.11220858116664, 0.79984192501233, 0.910480398190892, 1.45343080732047, 1.05062470048119, 1.10783285444028, 0.00482548296748707, 0.0192021029400786, 0.0155516790039065, 0.0555335781266458, 0.0323015886413371, -0.0522514103795643, 0.0789431409081487, 0.0150078675242675, 0.0406344952019763, 0.0317853983114957, -0.0532930035886563, 0.0186565372953721, 0.00884439739842633, 0.00392597583562098, -0.0100546599966053, -0.106138266021218, 0.0160490751802889, 0.0984423108227788, 0.17068413621789,   > test-esem.R: 0.0109412500742362, 0.00174847079953263, -0.00828708360663369, -0.0137548007419955, -0.0522477483772834, -0.00110780602245253, 0.00923449433845097, -0.00601795258733618, 0.00711445555663318, 0.018408323287891, 0.76717238801061, 1.02177932037355, 0.812215657741097, 0.018798081814193, 0.765698559147951, 1.00662688239373, 0.75340726253597, 1.05062470048119, 1.30720068817729, 1.01913616621123, -0.0536652115645841, -0.019213861470815, -0.0172908302087426, -0.0638553156597295, -0.0169096775619646, -0.0445701055393561, 0.0904561706745593, -0.0585860797001331, 0.00971501942601363, 0.0120483492539913, -0.00857750561667388, -0.0162965216843151, -0.00367260312735112, 0.00574676565142401, 0.0170376579092655, -0.00157974786814208, 0.00744705291894205, 0.0677157657287604, 0.0662442892600502, 0.0843708232885414, 0.0826773995441732, 0.0575131343085677, -0.0746340366111427, -0.0194906592088833, -0.0123907039927979, 0.0103019254283266, 0.00995636294223919, -0.00993507647126291, -0.0189706498549756, 0.812608776357063, 0.772375497705721, 1.0699033568652, 0.00581948595103035, 0.82894666600568, 0.729381828929068, 1.0564303582682, 1.10783285444028, 1.01913616621123, 1.33101934741115, -0.0160332825788463, -0.0194673047264479, 0.0076134333416248, -0.0280957630388753, -0.00652515275734159, 0.0398832411621319, 0.00997923231952932, -0.00317128543875011, -0.00345081806988481, 0.0335091835712613, 0.0767430961264705, -0.00917673037167745, 0.0351601326522669, 0.0545645893278382, 0.0337745421542386, 0.155628564410312, 0.0506965702279097, -0.0765907638253904, -0.152316211916453, 0.134776830038491, 0.0577716250034614, -0.0862221610584902, -0.14926687959738, 0.0890398368213926, 0.0395883128666614, -0.0913713589057094, -0.110540740333588, -0.00346764772535688, -0.0118850547463466, 5.64931156303779e-06, -0.0457924736654755, -0.0256936941471223, -0.00488309654925988, 0.0142986764803992, -0.0329019063108192, 0.00579222433398088, 0.00482548296748707, -0.0536652115645841, -0.0160332825788463, 0.120861870687388, 0.0659352781421284, 0.0637822024211485, -0.00123067312283201, -0.0245753162929485, 0.005478944302505, -0.00851490425935088, -0.0178213100814595, -0.0155256161093073, -0.0158877338895445, -0.00352146452370504, 0.00379043849646447, 0.00839475638588036, 0.00351526032811938, -0.00021124007570947, 0.153818992474422, 0.0646531323927172, -0.07315026181056, -0.145043021521224, 0.118952680023042, 0.0240332716358964, -0.0960292154420592, -0.122855476743761, 0.131204656804499, 0.0716961735842288, -0.0780111274536073, -0.122682130683992, -0.00584038224322596, -0.00878063137456458, 0.0102626772585488, -0.0294483813254788, -0.0192954594118436, 0.00560377528198599, 0.010614318885733, -0.0136956271030428, -0.00895211036399707, 0.0192021029400786, -0.019213861470815, -0.0194673047264479, 0.0659352781421284, 0.118049819131347, 0.0482278273076368, -0.0342607132374605, -0.0110359863231057, 0.0388495658350893, 0.020452480807499, -0.00791788041762326, 0.0078896468194146, 0.0458021905858184, 0.0679798285981358, 0.00436030761914487, 0.0119231550404147, 0.0482644269885699, 0.0322855829479996, 0.166944494374591, 0.0136332585073733, -0.0918071222196781, -0.119582194867943, 0.141748124176178, 0.0621044192912241, -0.121941490751061, -0.153408764961069, 0.153319351221939, 0.0545351763997117, -0.103938786698107, -0.132968263756207, -0.00432817212430529, -0.00695610027104775, 0.00490334272631464, -0.0249420219318624, 0.00388272127596111, -0.0120229152759623, 0.0160737255995745, -0.0107407756452412, 0.0266774382853429, 0.0155516790039065, -0.0172908302087426, 0.0076134333416248, 0.0637822024211485, 0.0482278273076368, 0.0987931493939915)), NACOV.user = FALSE, missing.flag = FALSE, missing = list(NULL), missing.h1 = list( NULL), YLp = list(NULL), zero.cell.tables = list( c("x1", "x1", "x2", "x4", "x4", "x5", "x2", "x3", "x3", "x5", "x6", "x6"))))   > test-esem.R: 13: do.call("sem", args = c(list(model = model_syntax, data = data, sample_cov = sample_cov, sample_nobs = sample_nobs, rotation = rotation, rotation.args = rotation_args, bounds = bounds, cmd = "efa"), dotdotdot))   > test-esem.R: 14: (function (data = NULL, nfactors = 1L, sample_cov = NULL, sample_nobs = NULL, rotation = "geomin", rotation_args = list(), ov_names = NULL, bounds = "pos.var", ..., output = "efa") { dotdotdot <- list(...) lav_adapt_func(environment(), dotdotdot, TRUE) if (!missing(rotation_args)) { lav_deprecated_args("rotation", "rotation_args") } if (is.list(rotation)) { rotation_args <- modifyList(list(), rotation) rotation_args <- lav_snake_case(rotation_args) rotation <- rotation[[1L]] } twolevel_flag <- !is.null(dotdotdot$cluster) group_flag <- !is.null(dotdotdot$group) sampling_weights_flag <- !is.null(dotdotdot$sampling.weights) if (!is.null(data) && !inherits(data, "lavMoments")) { data <- as.data.frame(data) } if (!is.null(data) && inherits(data, "lavMoments")) { if ("sample.cov" %in% names(data)) { ov_names <- rownames(data$sample.cov) if (is.null(ov_names)) { ov_names <- colnames(data$sample.cov) } } else { lav_msg_stop(gettext("When data= is of class lavMoments, it must contain sample.cov")) } } else if (!is.null(data) && inherits(data, "data.frame")) { if (length(ov_names) > 0L) { names_1 <- ov_names if (twolevel_flag) { names_1 <- c(names_1, dotdotdot$cluster) } if (group_flag) { names_1 <- c(names_1, dotdotdot$group) } if (sampling_weights_flag) { names_1 <- c(names_1, dotdotdot$sampling.weights) } data <- data[, names_1, drop = FALSE] } else { ov_names <- names(data) if (twolevel_flag) { if (!all(dotdotdot$cluster %in% ov_names)) { lav_msg_stop(gettextf("cluster variable(s) %s not found in data.", lav_msg_view(dotdotdot$cluster[!dotdotdot$cluster %in% ov_names]))) } ov_names <- setdiff(ov_names, dotdotdot$cluster) } if (group_flag) { if (!all(dotdotdot$group %in% ov_names)) { lav_msg_stop(gettextf("group variable(s) %s not found in data.", lav_msg_view(dotdotdot$group[!dotdotdot$group %in% ov_names]))) } ov_names <- setdiff(ov_names, dotdotdot$group) } if (sampling_weights_flag) { if (!all(dotdotdot$sampling.weights %in% ov_names)) { lav_msg_stop(gettextf("sampling.weights variable(s) %s not found in data.", lav_msg_view(dotdotdot$sampling.weights[!dotdotdot$sampling.weights %in% ov_names]))) } ov_names <- setdiff(ov_names, dotdotdot$sampling.weights) } } } else if (!is.null(sample_cov)) { ov_names <- rownames(sample_cov) if (is.null(ov_names)) { ov_names <- colnames(sample_cov) } } if (length(ov_names) == 0L) { lav_msg_stop(gettext("could not extract variable names from data or sample_cov")) } nlevels <- if (twolevel_flag) 2L else 1L if (group_flag && !is.null(data) && inherits(data, "data.frame")) { ngroups <- length(unique(data[[dotdotdot$group]])) } else if (!is.null(sample_cov) && is.list(sample_cov) && !inherits(sample_cov, "lavMoments")) { ngroups <- length(sample_cov) } else { ngroups <- 1L }   > test-esem.R: nblocks <- ngroups * nlevels if (is.list(nfactors)) { nfactors_list <- lapply(nfactors, function(x) { x <- as.integer(x) if (length(x) == 1L) { x <- rep.int(x, nblocks) } if (length(x) != nblocks) { lav_msg_stop(gettextf("each element of nfactors must have length 1 or\n the number of blocks (= %1$s); found length %2$s.", nblocks, length(x))) } x }) } else { nfactors_list <- lapply(as.integer(nfactors), function(k) { rep.int(k, nblocks) }) } nfactors_labels <- vapply(nfactors_list, function(x) { if (length(unique(x)) == 1L) { paste0("nf", x[1L]) } else { paste0("nf", paste(x, collapse = "_")) } }, character(1L)) all_nfactors <- unlist(nfactors_list) if (any(all_nfactors < 1L)) { lav_msg_stop(gettext("nfactors must be greater than zero.")) } else { nvar <- length(ov_names) p_star <- nvar * (nvar + 1)/2 nfac_max <- 0L for (nfac in seq_len(nvar)) { npar <- nfac * nvar + nfac * (nfac + 1L)/2 + nvar - nfac^2 if (npar > p_star) { nfac_max <- nfac - 1L break } } if (any(all_nfactors > nfac_max)) { lav_msg_stop(gettextf("when nvar = %1$s the maximum number of factors\n is %2$s", nvar, nfac_max)) } } output <- tolower(output) if (!output %in% c("lavaan", "efa")) { lav_msg_stop(gettext("output= must be either \"lavaan\" or \"efa\"")) } if (output == "lavaan" && length(nfactors_list) > 1L) { lav_msg_stop(gettext("when output = \"lavaan\", nfactors must be a\n single (integer) number.")) } nfits <- length(nfactors_list) out <- vector("list", length = nfits) for (f in seq_len(nfits)) { model_syntax <- lav_syntax_efa(ov_names = ov_names, nfactors = nfactors_list[[f]], nlevels = nlevels, ngroups = ngroups) fit <- do.call("sem", args = c(list(model = model_syntax, data = data, sample_cov = sample_cov, sample_nobs = sample_nobs, rotation = rotation, rotation.args = rotation_args, bounds = bounds, cmd = "efa"), dotdotdot)) if (output == "efa") { fit@Options$model.type <- "efa" } out[[f]] <- fit } if (nfits == 1L && output == "lavaan") { out <- out[[1]] } else { names(out) <- nfactors_labels out$loadings <- lav_efa_get_loadings(out) class(out) <- c("efaList", "list") } out})(data = list(x1 = c(5L, 3L, 4L, 4L, 3L, 4L, 4L, 3L, 5L, 2L, 4L, 5L, 3L, 3L, 3L, 4L, 2L, 1L, 1L, 5L, 3L, 1L, 3L, 5L, 5L, 3L, 3L, 1L, 4L, 2L, 4L, 4L, 4L, 3L, 5L, 1L, 1L, 2L, 1L, 3L, 4L, 2L, 3L, 3L, 1L, 3L, 1L, 5L, 3L, 4L, 4L, 2L, 5L, 4L, 4L, 3L, 3L, 3L, 1L, 4L, 2L, 4L, 4L, 5L, 3L, 5L, 4L, 5L, 3L, 4L, 2L, 3L, 4L, 1L, 3L, 3L, 3L, 4L, 1L, 1L, 5L, 3L, 4L, 3L, 2L, 3L, 3L, 3L, 4L, 4L, 5L, 2L, 4L, 5L, 1L, 1L, 1L, 1L, 3L, 4L, 5L, 5L, 1L, 5L, 1L, 3L, 2L, 2L, 3L, 3L, 3L, 4L, 2L, 2L, 1L, 2L, 3L, 5L, 1L, 3L, 1L, 2L, 3L, 2L, 3L, 3L, 3L, 1L, 1L, 3L, 4L, 3L, 3L, 4L, 5L, 2L, 3L, 4L, 3L, 3L, 3L, 3L, 2L, 2L, 1L, 5L, 2L, 2L, 4L, 1L, 2L, 1L, 5L, 3L, 1L, 1L, 3L, 3L, 3L, 5L, 3L, 1L, 3L, 3L, 4L, 5L, 1L, 3L, 3L, 5L, 3L, 3L, 1L, 5L, 5L, 3L, 1L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 4L, 2L, 2L, 3L, 3L, 4L, 1L, 2L, 4L, 2L, 3L, 5L, 3L, 1L, 4L, 5L, 5L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 5L, 4L, 3L, 3L, 3L, 3L, 5L, 2L, 3L, 2L, 3L, 2L, 1L, 3L, 3L, 3L, 1L, 2L, 5L, 3L, 5L, 1L, 3L, 1L, 2L, 5L, 3L, 4L, 1L, 3L, 4L, 4L, 5L, 1L, 5L, 4L, 2L, 5L, 2L, 1L, 3L, 1L, 3L, 5L, 1L, 3L, 3L, 2L, 2L, 4L, 5L, 3L, 1L, 3L, 5L, 5L, 3L, 1L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 3L, 2L, 5L, 4L, 3L, 2L, 3L, 3L, 3L, 5L, 5L, 2L, 1L, 4L, 2L, 5L, 2L, 5L, 2L, 3L, 3L, 4L, 4L, 2L), x2 = c(5L, 2L, 3L, 3L, 5L, 1L, 5L, 3L, 5L, 2L, 5L, 5L, 2L, 3L, 3L, 3L, 3L, 1L, 1L, 5L, 3L, 1L, 2L, 5L, 5L, 3L, 2L, 1L, 3L, 3L, 4L, 5L, 5L, 2L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 2L, 5L, 2L, 1L, 4L, 1L, 5L, 3L, 4L, 4L, 3L, 5L, 3L, 3L, 3L, 5L, 4L, 1L, 4L, 3L, 3L, 3L, 5L, 2L, 5L, 5L, 3L, 4L, 5L, 1L, 2L, 4L, 3L, 3L, 4L, 5L, 4L, 2L, 1L, 5L, 3L, 3L, 3L, 1L, 5L, 3L, 5L, 5L, 5L, 5L, 2L, 4L, 5L, 2L, 2L, 1L, 2L, 3L, 3L, 5L, 5L, 2L, 5L, 1L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 2L, 1L, 5L, 1L, 3L, 2L, 1L, 4L, 3L, 3L, 3L, 2L, 1L, 1L, 3L, 4L, 3L, 3L, 5L, 5L, 1L, 4L, 5L, 2L, 3L, 2L, 1L, 2L, 1L, 3L, 5L, 2L, 2L, 3L, 2L, 3L, 1L, 5L, 3L, 3L, 1L, 3L, 3L, 2L, 5L, 3L, 1L, 3L, 2L, 3L, 5L, 2L, 3L, 3L, 4L, 3L, 4L, 1L, 5L, 4L, 3L, 1L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 3L, 1L, 4L, 3L, 1L, 1L, 3L, 3L, 3L, 1L, 2L, 5L, 4L, 4L, 5L, 5L, 1L, 3L, 4L, 5L, 1L, 2L, 2L, 1L, 3L, 3L, 1L, 5L, 3L, 3L, 4L, 3L, 3L, 5L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 5L, 3L, 4L, 1L, 3L, 3L, 2L, 5L, 3L, 3L, 2L, 1L, 5L, 5L, 5L, 1L, 5L, 4L, 3L, 5L, 1L, 1L, 3L, 2L, 3L, 5L, 2L, 3L, 3L, 1L, 1L, 5L, 4L, 3L, 1L, 3L, 4L, 3L, 2L, 1L, 3L, 4L, 3L, 3L, 3L, 3L, 5L, 4L, 2L, 4L, 5L, 5L, 1L, 2L, 3L, 3L, 3L, 5L, 2L, 1L, 2L, 2L, 4L, 3L, 5L, 3L, 3L, 2L, 3L, 3L, 1L), x3 = c(5L, 2L, 3L, 4L, 4L, 3L, 4L, 4L, 5L, 3L, 4L, 5L, 1L, 3L, 3L, 4L, 4L, 1L, 1L, 5L, 3L, 1L, 3L, 5L, 4L, 2L, 3L, 1L, 4L, 3L, 3L, 4L, 5L, 3L, 3L, 1L, 2L, 2L, 1L, 3L, 3L, 2L, 4L, 2L, 1L, 4L, 3L, 5L, 2L, 4L, 3L, 2L, 5L, 3L, 3L, 3L, 5L, 2L, 1L, 3L, 3L, 3L, 5L, 5L, 1L, 4L, 3L, 5L, 4L, 5L, 2L, 3L, 3L, 1L, 1L, 4L, 4L, 5L, 2L, 2L, 5L, 3L, 3L, 3L, 1L, 4L, 4L, 2L, 5L, 4L, 5L, 2L, 4L, 5L, 1L, 2L, 1L, 2L, 3L, 4L, 4L, 4L, 2L, 5L, 2L, 3L, 3L, 3L, 3L, 2L, 4L, 3L, 3L, 1L, 1L, 2L, 1L, 5L, 2L, 3L, 1L, 2L, 2L, 2L, 3L, 3L, 2L, 1L, 1L, 4L, 5L, 1L, 3L, 5L, 5L, 1L, 3L, 5L, 3L, 3L, 3L, 2L, 1L, 3L, 3L, 4L, 1L, 2L, 5L, 1L, 3L, 1L, 5L, 3L, 3L, 1L, 3L, 2L, 2L, 5L, 2L, 3L, 4L, 2L, 3L, 5L, 1L, 3L, 3L, 3L, 3L, 4L, 1L, 5L, 4L, 3L, 1L, 4L, 4L, 3L, 4L, 2L, 3L, 4L, 3L, 1L, 3L, 3L, 1L, 1L, 3L, 3L, 4L, 1L, 2L, 4L, 3L, 4L, 5L, 2L, 1L, 3L, 5L, 5L, 1L, 2L, 1L, 1L, 3L, 2L, 1L, 5L, 3L, 3L, 3L, 3L, 3L, 5L, 3L, 1L, 4L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 1L, 5L, 2L, 4L, 2L, 3L, 3L, 3L, 5L, 3L, 4L, 1L, 3L, 5L, 5L, 4L, 3L, 5L, 5L, 3L, 4L, 2L, 1L, 3L, 2L, 3L, 5L, 1L, 1L, 3L, 1L, 3L, 3L, 4L, 3L, 1L, 3L, 4L, 5L, 3L, 1L, 3L, 4L, 3L, 3L, 3L, 3L, 5L, 3L, 1L, 5L, 5L, 5L, 1L, 2L, 3L, 3L, 4L, 5L, 1L, 1L, 3L, 3L, 4L, 3L, 5L, 3L, 3L, 3L, 4L, 3L, 1L), x4 = c(3L, 4L, 3L, 5L, 3L, 3L, 4L, 4L, 2L, 2L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 2L, 5L, 3L, 3L, 5L, 2L, 5L, 3L, 1L, 3L, 4L, 2L, 2L, 4L, 5L, 3L, 1L, 1L, 3L, 2L, 2L, 4L, 1L, 1L, 4L, 3L, 4L, 1L, 3L, 3L, 5L, 2L, 4L, 1L, 1L, 4L, 3L, 4L, 2L, 1L, 2L, 1L, 3L, 3L, 3L, 3L, 5L, 2L, 3L, 4L, 5L, 3L, 5L, 2L, 5L, 3L, 3L, 2L, 3L, 5L, 5L, 1L, 4L, 5L, 2L, 2L, 2L, 3L, 5L, 4L, 3L, 3L, 3L, 2L, 3L, 4L, 3L, 3L, 4L, 3L, 5L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 5L, 1L, 1L, 3L, 1L, 3L, 2L, 2L, 1L, 3L, 1L, 5L, 4L, 1L, 2L, 2L, 4L, 3L, 2L, 4L, 3L, 3L, 5L, 2L, 2L, 1L, 3L, 5L, 1L, 2L, 2L, 5L, 5L, 3L, 5L, 3L, 5L, 3L, 5L, 3L, 2L, 4L, 1L, 2L, 3L, 2L, 3L, 2L, 5L, 4L, 5L, 2L, 5L, 5L, 4L, 1L, 1L, 3L, 5L, 5L, 1L, 2L, 4L, 1L, 4L, 1L, 3L, 2L, 2L, 3L, 3L, 4L, 3L, 1L, 1L, 1L, 4L, 3L, 3L, 1L, 3L, 5L, 3L, 3L, 3L, 5L, 3L, 4L, 5L, 2L, 4L, 3L, 5L, 4L, 2L, 4L, 2L, 3L, 3L, 2L, 1L, 4L, 4L, 2L, 2L, 3L, 5L, 5L, 1L, 2L, 4L, 3L, 5L, 3L, 4L, 3L, 5L, 4L, 1L, 2L, 5L, 1L, 1L, 1L, 2L, 3L, 2L, 3L, 2L, 3L, 5L, 1L, 2L, 3L, 3L, 5L, 4L, 4L, 4L, 2L, 3L, 5L, 1L, 3L, 3L, 5L, 3L, 3L, 5L, 1L, 5L, 1L, 5L, 3L, 5L, 4L, 2L, 1L, 4L, 3L, 2L, 3L, 2L, 2L, 1L, 1L, 3L, 5L, 5L, 3L, 3L, 4L, 3L, 3L, 2L, 1L, 3L, 1L, 4L, 1L, 2L, 3L, 4L, 3L, 3L, 2L, 3L, 3L, 1L, 5L, 1L, 4L), x5 = c(3L, 4L, 3L, 3L, 1L, 3L, 4L, 5L, 3L, 3L, 3L, 2L, 2L, 3L, 4L, 3L, 2L, 3L, 1L, 5L, 3L, 3L, 3L, 3L, 5L, 2L, 4L, 4L, 2L, 3L, 4L, 3L, 3L, 3L, 5L, 2L, 1L, 1L, 1L, 2L, 4L, 4L, 3L, 2L, 3L, 3L, 5L, 1L, 3L, 5L, 3L, 2L, 4L, 2L, 3L, 5L, 2L, 5L, 3L, 1L, 3L, 2L, 3L, 3L, 3L, 1L, 5L, 1L, 3L, 3L, 5L, 4L, 5L, 1L, 5L, 3L, 3L, 1L, 3L, 5L, 5L, 1L, 4L, 5L, 3L, 4L, 1L, 4L, 5L, 4L, 3L, 3L, 5L, 2L, 3L, 4L, 3L, 3L, 2L, 5L, 5L, 3L, 3L, 4L, 1L, 2L, 3L, 4L, 5L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 2L, 5L, 4L, 1L, 3L, 1L, 4L, 3L, 2L, 4L, 3L, 3L, 5L, 2L, 2L, 1L, 2L, 5L, 1L, 3L, 1L, 5L, 4L, 3L, 5L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 2L, 3L, 1L, 5L, 4L, 5L, 2L, 5L, 5L, 3L, 1L, 1L, 3L, 5L, 4L, 1L, 2L, 3L, 1L, 5L, 1L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 1L, 1L, 1L, 4L, 3L, 3L, 1L, 1L, 4L, 3L, 3L, 3L, 5L, 2L, 3L, 5L, 1L, 3L, 3L, 5L, 4L, 2L, 2L, 1L, 3L, 1L, 1L, 1L, 5L, 3L, 3L, 2L, 3L, 5L, 5L, 1L, 3L, 5L, 3L, 5L, 3L, 3L, 4L, 5L, 5L, 2L, 3L, 4L, 1L, 1L, 1L, 2L, 5L, 3L, 3L, 2L, 3L, 5L, 2L, 2L, 2L, 3L, 5L, 3L, 5L, 3L, 2L, 3L, 5L, 1L, 4L, 3L, 4L, 5L, 3L, 5L, 1L, 5L, 1L, 3L, 3L, 5L, 3L, 1L, 1L, 5L, 3L, 2L, 3L, 1L, 2L, 2L, 1L, 3L, 5L, 5L, 4L, 3L, 5L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 4L, 1L, 4L), x6 = c(3L, 5L, 3L, 3L, 3L, 3L, 4L, 5L, 1L, 3L, 3L, 1L, 1L, 2L, 3L, 2L, 2L, 3L, 1L, 4L, 3L, 3L, 3L, 3L, 5L, 2L, 5L, 3L, 2L, 3L, 3L, 3L, 1L, 3L, 4L, 3L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 1L, 5L, 4L, 5L, 1L, 4L, 5L, 5L, 1L, 3L, 3L, 3L, 5L, 3L, 4L, 3L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 5L, 1L, 3L, 5L, 5L, 3L, 5L, 1L, 5L, 2L, 4L, 1L, 3L, 5L, 5L, 2L, 3L, 5L, 3L, 1L, 3L, 4L, 5L, 5L, 3L, 1L, 3L, 1L, 3L, 5L, 3L, 3L, 2L, 5L, 5L, 2L, 3L, 3L, 3L, 1L, 4L, 5L, 5L, 1L, 2L, 2L, 1L, 3L, 3L, 2L, 1L, 3L, 1L, 5L, 5L, 1L, 4L, 3L, 4L, 4L, 2L, 5L, 3L, 3L, 5L, 2L, 1L, 1L, 3L, 3L, 1L, 3L, 1L, 5L, 5L, 3L, 5L, 1L, 4L, 3L, 3L, 3L, 2L, 4L, 1L, 3L, 3L, 2L, 3L, 3L, 5L, 4L, 5L, 2L, 3L, 5L, 3L, 1L, 1L, 3L, 5L, 3L, 1L, 2L, 3L, 1L, 5L, 1L, 3L, 3L, 2L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 5L, 3L, 3L, 2L, 2L, 4L, 3L, 3L, 3L, 5L, 2L, 5L, 4L, 1L, 3L, 4L, 5L, 5L, 3L, 3L, 1L, 3L, 1L, 2L, 1L, 5L, 5L, 2L, 1L, 3L, 5L, 5L, 1L, 2L, 5L, 3L, 5L, 3L, 5L, 4L, 5L, 5L, 1L, 2L, 4L, 1L, 1L, 1L, 4L, 5L, 3L, 3L, 4L, 2L, 5L, 2L, 3L, 2L, 3L, 5L, 3L, 5L, 5L, 3L, 3L, 4L, 1L, 3L, 3L, 5L, 5L, 3L, 5L, 1L, 5L, 1L, 3L, 3L, 5L, 3L, 1L, 1L, 5L, 3L, 3L, 3L, 2L, 3L, 1L, 1L, 3L, 5L, 5L, 3L, 3L, 4L, 3L, 3L, 1L, 1L, 5L, 1L, 5L, 1L, 1L, 3L, 3L, 3L, 4L, 2L, 2L, 3L, 3L, 5L, 1L, 5L)), nfactors = 3L, rotation = "varimax", estimator = "WLSMV", ordered = c("x1", "x2", "x3", "x4", "x5", "x6"), missing = "available.cases", slot_sample_stats = new("lavSampleStats", var = list(c(1, 1, 1, 1, 1, 1)), cov = list(c(1, 0.847708645568633, 0.852266877770598, -0.045025067439856, 0.0187792727635132, -0.0528665611502654, 0.847708645568633, 1, 0.866098037290729, -0.00562422746118933, 0.0637607092511551, 0.00299593013986168,   > test-esem.R: 0.852266877770598, 0.866098037290729, 1, -0.0468064624916715, 0.0271693531291436, -0.0307059937186903, -0.045025067439856, -0.00562422746118933, -0.0468064624916715, 1, 0.854835831983838, 0.8624962089988, 0.0187792727635132, 0.0637607092511551, 0.0271693531291436, 0.854835831983838, 1, 0.864362516433594, -0.0528665611502654, 0.00299593013986168, -0.0307059937186903, 0.8624962089988, 0.864362516433594, 1)), mean = list( c(0, 0, 0, 0, 0, 0)), th = list(c(-0.954165253146194, -0.467698799114508, 0.449136303439425, 1.00825234717068, -0.941074530352976, -0.439913165673234, 0.524400512708041, 0.853587957511572, -0.902734791643865, -0.458397807359904, 0.467698799114508, 0.994457883209753, -1.00825234717068, -0.394335532100823, 0.486423874628588, 0.967421566101701, -0.841621233572914, -0.412463129441405, 0.553384719555673, 0.915365087842814, -0.80642124701824, -0.458397807359904, 0.505321088513851, 0.760983928488951)), th.idx = list( c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L)), th.names = list(c("x1|t1", "x1|t2", "x1|t3", "x1|t4", "x2|t1", "x2|t2", "x2|t3", "x2|t4", "x3|t1", "x3|t2", "x3|t3", "x3|t4", "x4|t1", "x4|t2", "x4|t3", "x4|t4", "x5|t1", "x5|t2", "x5|t3", "x5|t4", "x6|t1", "x6|t2", "x6|t3", "x6|t4")), res.cov = list(NULL), res.var = list( NULL), res.th = list(NULL), res.th.nox = list(NULL), res.slopes = list(NULL), res.int = list(NULL), mean.x = list( NULL), cov.x = list(NULL), bifreq = list(NULL), group.w = list( 1), nobs = list(300L), ntotal = 300L, ngroups = 1L, x.idx = list(integer(0)), icov = list(NULL), cov.log.det = list( NULL), res.icov = list(NULL), res.cov.log.det = list( NULL), ridge = 0, WLS.obs = list(c(-0.954165253146194, -0.467698799114508, 0.449136303439425, 1.00825234717068, -0.941074530352976, -0.439913165673234, 0.524400512708041, 0.853587957511572, -0.902734791643865, -0.458397807359904, 0.467698799114508, 0.994457883209753, -1.00825234717068, -0.394335532100823, 0.486423874628588, 0.967421566101701, -0.841621233572914, -0.412463129441405, 0.553384719555673, 0.915365087842814, -0.80642124701824, -0.458397807359904, 0.505321088513851, 0.760983928488951, 0.847708645568633, 0.852266877770598, -0.045025067439856, 0.0187792727635132, -0.0528665611502654, 0.866098037290729, -0.00562422746118933, 0.0637607092511551, 0.00299593013986168, -0.0468064624916715, 0.0271693531291436, -0.0307059937186903, 0.854835831983838, 0.8624962089988, 0.864362516433594)), WLS.V = list(NULL), WLS.VD = list(c(0.45383482142755, 0.587709518619898, 0.591395244765584, 0.435865310988534, 0.458128368522494, 0.593178170698077, 0.575667399853721, 0.486139871182117, 0.470559146996906, 0.589572474383863, 0.587709518619898, 0.440480678601174, 0.435865310988534, 0.601506349600645, 0.583861633032411, 0.449463546185201, 0.489866730490175, 0.598291135869332, 0.569072720318582, 0.466488951106186, 0.500662923226088, 0.589572474383863, 0.579848639230085, 0.514201673558285, 7.44280585633078, 6.75326246420858, 0.68465337775283, 0.747098037875909, 0.711965105740159, 11.6264723451673, 0.71957812751013, 0.780476688725976, 0.738014115692331, 0.688027249018883, 0.764993477317059, 0.751303879951114, 8.27390801013265, 8.47099984869401, 10.1221593413523)), NACOV = list(c(2.20344484994446, 1.2774226485315, 0.608464081144384, 0.438580200934569, 1.24211958837833, 1.06100103747073, 0.579645270096896, 0.476730975188745, 1.17367332415033, 1.00896404529906, 0.601140069897175, 0.44176715543208, -0.219015644772081, -0.0242678714026954, -0.0379083822845332, 0.079081810080948, -0.0564610061408481, -0.120072924149705, 0.107363949033218, 0.109435686051998, -0.169568169571692, -0.238028958705595, -0.0135053916825915, -0.0269051436957956, 0.148317263761989, 0.15574465153135, -0.0301086318650708, 0.00933114409349929, -0.0308508864967571, 0.110777263526235, 0.00948677799058494, -0.00412089339286978, -0.10125465414037, 0.0785862105655183, 0.0555335781266458, -0.0638553156597295, -0.0280957630388753, -0.00123067312283201, -0.0342607132374605, 1.2774226485315, 1.70152085055262, 0.810471242285935, 0.584186727382364, 1.14089096616285, 1.11498605030749, 0.74527622661719, 0.635003375993402, 1.06755750714773, 1.03707982565867, 0.748585766358322, 0.588431735511437, -0.195764701197282, -0.057578351381687, -0.0504937836682346, -0.0373067097809645, 0.0599296137472571, -0.0417199784040522, 0.0501041505326083, 0.0809914034723241, -0.102201933410529, -0.130802860893886, -0.0148660064484897, 0.0449437782925636, 0.0741651928462058, 0.0490907248507851, -0.0176892046762685, 0.00394541784101921, -0.00847157050708947, 0.0757812257161068, -0.0661029519937833, -0.0252830371146104, -0.0476247917553443, -0.0264569477560322, 0.0323015886413371, -0.0169096775619646, -0.00652515275734159, -0.0245753162929485, -0.0110359863231057, 0.608464081144384, 0.810471242285935, 1.69091653822204, 1.21881067112823, 0.576674081941792, 0.774285253955362, 1.0792024722852, 1.05804201981214, 0.625589760454923, 0.815394330786237, 1.12783689455862, 1.15169768636051, 0.0136079556709071, 0.0243719568965317, -0.0184270459355523, -0.0493207998636407, 0.0858315089017964, 0.0837418857774159, -0.0113399097133595, 0.0479013123640107, 0.0827363125250564, -0.0337960621007554, -0.0540468233753761, 0.0344537680314544, -0.0835035702275452, -0.0786770150818312, 0.0106760261359684, -0.00123494659659294, 0.0122565252286696, -0.0592467719700587, -0.0412145589031354, -0.0176224865986117, -0.0380983101009663, -0.0402960408343714, -0.0522514103795643, -0.0445701055393561, 0.0398832411621319, 0.005478944302505, 0.0388495658350893, 0.438580200934569, 0.584186727382364, 1.21881067112823, 2.29428673213755, 0.441666799275109, 0.594893883378979, 1.19451162375741, 1.29095297173797, 0.450924370633182, 0.587735283838845, 1.16371239045051, 1.39753600268651, 0.0789137392518928, 0.0110390707298294, -0.146050804979038, -0.157518417294168, 0.0198461232749442, -0.0386675911959832, -0.106723079606281, 0.0285854186705204, -0.00626559185412115, -0.0310691387157628, -0.135030448940603, -0.116121963366209, -0.159334208125366, -0.139085742363334, 0.0271974707303992, -0.0071037068500013, 0.0263069574461733, -0.128992483494249, 0.107707346627344, 0.0946179973328222, 0.127452670358713, 0.110027033838194, 0.0789431409081486, 0.0904561706745593, 0.00997923231952932, -0.00851490425935088, 0.020452480807499, 1.24211958837833, 1.14089096616285, 0.576674081941792, 0.441666799275109, 2.18279431859916, 1.25161307106614, 0.58372464286605, 0.480086067447278, 1.29726260924579, 0.984812668193073, 0.605370716739469, 0.444876182620165, -0.170594704793659, -0.000939948520669462, -0.099353327882025, -0.121499556217292, 0.120824019421084, 0.0113621668615782, -0.0729739856394577, -0.0317318220007143, -0.0866730771247237, -0.174358692112653, -0.0760810863132528, -0.0604073968525699, 0.16194297708138, 0.120827352177701, -0.0919049501204477, -0.0267557831346448, -0.0881523522151011, 0.128137179839506, 0.000212298869746207, 0.0367164551098905, -0.00373340788228093, -0.000447770974788038, 0.0150078675242675, -0.0585860797001331, -0.0031712854387501, -0.0178213100814595, -0.00791788041762326, 1.06100103747073, 1.11498605030749, 0.774285253955362, 0.594893883378979, 1.25161307106614, 1.68583412100813, 0.786235732883992, 0.64664191532757, 1.13913953707982, 1.17862045059145, 0.81539146061268, 0.599216695337057, -0.134627822737602, -0.0578543768292243, -0.0254499315393859, -0.0184196260647881, 0.0394524588887727, -0.0165793698112683, 0.0190914135644343, 0.169073277814853, -0.057167462611575, -0.154022807307125, 0.0880784013810653, 0.0342100668274375, 0.0539361700938887, 0.0615567447500341, -0.0681981562446394, -0.0246495398767953, -0.0554549756281441, 0.0717566895580845, 5.75617068663093e-06, 0.0204479578807549, 7.74832431628764e-05, 0.00318648418314011, 0.0406344952019763, 0.00971501942601362, -0.00345081806988481, -0.0155256161093073, 0.0078896468194146, 0.579645270096895, 0.74527622661719, 1.0792024722852, 1.19451162375741, 0.58372464286605, 0.786235732883992, 1.73711417435502, 1.42869471567676, 0.595959822290149, 0.670001492928105, 1.18493558332661, 1.16630442958261, 0.163795908274427, 0.109091022310938, -0.00540981015134475, -1.57046074786481e-16, 0.102731959227697, 0.146522255839915, 0.0252065688162364, 0.138836295431548, 0.0964684652932333, 0.0827491883696062, 0.120136315645039, -0.00321012859252077, -0.0858800421247186, -0.0451906419084792, -0.0765124055981449, -0.0495654183174262, -0.0848028980005815, -0.0833098166158672, -0.000535439721952539, -0.0120376783586505, 0.00713013186502567, 0.00519137622969441, 0.0317853983114957, 0.0120483492539913, 0.0335091835712613, -0.0158877338895445, 0.0458021905858184, 0.476730975188745, 0.635003375993402, 1.05804201981214, 1.29095297173797, 0.480086067447278, 0.64664191532757, 1.42869471567676, 2.05702115641813, 0.490148927129519, 0.571882388380539, 1.21484756042805, 1.46125569212401, 0.212680617707058, 0.145119741737691, -0.0165152518961889, -0.0882562149935091, 0.206218263545022, 0.166099245009795, -0.00386514223473117, 0.0174182527862748, 0.0997440147141751, 0.103034832027179, 0.0653127512676339, -0.0071150608221477, -0.134071027660114, -0.0976632797843361, -0.137740647801462, -0.129333918235308, -0.117770415220244, -0.155270199765802, 0.00279700652525927, -0.029357693070375, 0.00385735432182198, -0.0242062457633788, -0.0532930035886563, -0.00857750561667388, 0.0767430961264705, -0.00352146452370503, 0.0679798285981358, 1.17367332415033, 1.06755750714773, 0.625589760454923, 0.450924370633182, 1.29726260924579, 1.13913953707982, 0.595959822290149, 0.49014892712952, 2.1251313599618, 1.30132191861677, 0.618059609401317, 0.454201024362567, -0.241597776915651, -0.0362922311272902, -0.098028891044111, -0.142411540832355, 0.0448569291718996, -0.0239915790387723, -0.0385217266559336, 0.0430732015144227, -0.111115021635821, -0.132288435076582, -0.0047687613849894, -0.0301359480319713, 0.138783846835874, 0.13451304238041, -0.0723053721025387, 0.0235559639025693, -0.0229809041838941, 0.14579926213725, -0.0346675510650626,   > test-esem.R: 0.0278921595163239, -0.0029584179876469, -0.0143270955539256, 0.0186565372953722, -0.0162965216843151, -0.00917673037167745, 0.00379043849646447, 0.00436030761914487, 1.00896404529906, 1.03707982565867, 0.815394330786237, 0.587735283838845, 0.984812668193073, 1.17862045059145, 0.670001492928105, 0.571882388380539, 1.30132191861677, 1.69614431380138, 0.805579524235362, 0.592006077646191, -0.0849566905132264, 0.0345326507924104, -0.042071892364949, -0.0309553186878741, 0.119344126776945, 0.0764956513097508, -0.0235887381762751, 0.0516400329150373, 0.0202881363683058, -0.0352304618528652, -0.0595609077281687, 0.0516911215418863, 0.0687933674969333, 0.0369020237224323, 0.00995076809763176, 0.064092949342599, 0.0369408306412495, 0.0799479942345352, 0.005592058134837, 0.0487302938257757, 0.0493669036835191, -0.0138630042221855, 0.00884439739842633, -0.00367260312735112, 0.0351601326522669, 0.00839475638588036, 0.0119231550404147, 0.601140069897175, 0.748585766358322, 1.12783689455862, 1.16371239045051, 0.605370716739469, 0.81539146061268, 1.18493558332661, 1.21484756042805, 0.618059609401317, 0.805579524235362, 1.70152085055262, 1.2504174379618, 0.118080295960265, 0.0828320142683918, -0.133598135955537, -0.186533548904822, 0.0732473056910928, 0.0671589896260352, -0.104565183720226, 0.0255762326754712, 0.0375171654291813, -0.0508677792365115, -0.0647733138112737, -0.0137328211449501, -0.0724591490523034, -0.0791267384446038, 0.00181309216736721, -0.0214221821010069, -0.0300017587548346, -0.0877579746528331, 0.10479954023783, 0.0704790601404688, 0.0725730814762955, 0.0132397293060142, 0.00392597583562099, 0.00574676565142401, 0.0545645893278382, 0.00351526032811938, 0.0482644269885699, 0.44176715543208, 0.588431735511437, 1.15169768636051, 1.39753600268651, 0.444876182620165, 0.599216695337057, 1.16630442958261, 1.46125569212401, 0.454201024362567, 0.592006077646191, 1.2504174379618, 2.27024713813936, 0.200952889275185, -0.0133620970402425, -0.0788523417871815, -0.219328304783088, 0.176164832903571, 0.0493539480970899, -0.116865540995329, 0.0187955189173209, 0.00380285492607957, -0.0183094663189546, -0.0671092644366832, -0.0330283840404653, -0.139306280370751, -0.13875441713431, 0.0668223880050227, -0.0448927653441914, 0.0318365551567394, -0.151806904210309, 0.148960920619109, 0.0609038763998929, 0.134305485249476, 0.031169280380637, -0.0100546599966053, 0.0170376579092655, 0.0337745421542386, -0.000211240075709474, 0.0322855829479996, -0.219015644772081, -0.195764701197282, 0.0136079556709071, 0.0789137392518928, -0.170594704793659, -0.134627822737602, 0.163795908274427, 0.212680617707058, -0.241597776915651, -0.0849566905132264, 0.118080295960265, 0.200952889275185, 2.29428673213755, 1.15558999503623, 0.577148887654197, 0.435492094872112, 1.27015188959644, 1.06222147579554, 0.553093374537492, 0.447838225838153, 1.35577768351094, 1.11397687818633, 0.570187169159342, 0.488208361335366, -0.0452073659278695, -0.0368039242195606, -0.0249465575779013, -0.157562953805574, -0.0510773841561005, -0.0144087480818, -0.000875250301410177, -0.102613201353469, 0.000573271171747722, -0.0360467188781403, -0.106138266021219, -0.00157974786814207, 0.155628564410312, 0.153818992474422, 0.166944494374591, -0.0242678714026954, -0.057578351381687, 0.0243719568965317, 0.0110390707298294, -0.000939948520669464, -0.0578543768292243, 0.109091022310938, 0.145119741737691, -0.0362922311272901, 0.0345326507924104, 0.0828320142683918, -0.0133620970402424, 1.15558999503623, 1.66249284095492, 0.830316892679672, 0.626521943879109, 0.974188874780071, 1.1002507213572, 0.742943487462754, 0.64428374039232, 1.10176290111632, 1.11577341589458, 0.743140939070639, 0.702362350920955, -0.0228496292950282, -0.00169230710749049, -0.0100558231103412, -0.00378787104048013, -0.0243505783648102, 0.0045802720538116, 0.0072931844882099, 0.0359904807552618, -0.0170014618557995, -0.0151073514301341, 0.0160490751802889, 0.00744705291894205, 0.0506965702279097, 0.0646531323927172, 0.0136332585073733, -0.0379083822845332, -0.0504937836682345, -0.0184270459355523, -0.146050804979038, -0.099353327882025, -0.0254499315393859, -0.00540981015134475, -0.0165152518961889, -0.098028891044111, -0.042071892364949, -0.133598135955537, -0.0788523417871815, 0.577148887654197, 0.830316892679671, 1.71273456487676, 1.29235692829569, 0.631548022402756, 0.768991594381702, 1.17427537880214, 1.1139472986559, 0.644167387922915, 0.76968868646083, 1.18316692085857, 1.25985049764384, -0.0497100849928933, 0.0114243324019316, 0.0145262770963222, 0.0972680602138176, 0.0637726699623928, 0.012559878300944, -0.00486631793652915, 0.0635513532398554, 0.0254927803908756, 0.00644936625016048, 0.0984423108227788, 0.0677157657287604, -0.0765907638253904, -0.07315026181056, -0.0918071222196781, 0.0790818100809479, -0.0373067097809646, -0.0493207998636407, -0.157518417294168, -0.121499556217292, -0.0184196260647881, -1.83037594978595e-16, -0.0882562149935091, -0.142411540832355, -0.0309553186878741, -0.186533548904822, -0.219328304783088, 0.435492094872112, 0.626521943879109, 1.29235692829569, 2.22487453874168, 0.476539376877921, 0.618983350337493, 1.14976137667066, 1.42361294424794, 0.486061415374205, 0.600533182544757, 1.05121838213894, 1.15403067874422, -0.0158659044150101, 0.0563346056670746, 0.0236919740312106, 0.103285956119985, -0.000130894536512416, 0.0205212058726693, -0.007320814773629, 0.0875644703300589, -0.00850467831901114, 0.0127667492155082, 0.17068413621789, 0.0662442892600502, -0.152316211916453, -0.145043021521224, -0.119582194867943, -0.056461006140848, 0.0599296137472572, 0.0858315089017964, 0.0198461232749443, 0.120824019421084, 0.0394524588887727, 0.102731959227697, 0.206218263545022, 0.0448569291718996, 0.119344126776945, 0.0732473056910928, 0.176164832903571, 1.27015188959644, 0.974188874780071, 0.631548022402756, 0.476539376877921, 2.04137153588563, 1.28678875419635, 0.605225158300069, 0.490049191698183, 1.2559069292527, 1.04757622393096, 0.623930127537152, 0.534224411962442, -0.00822479371015104, 0.00663271019471845, 0.019465008972702, 0.00885836454286383, 0.0422893759118978, 0.00923383989163264, 0.0428107979498749, 0.0357418739580545, 0.142453716502339, 0.0307344604466001, 0.0109412500742362, 0.0843708232885414, 0.134776830038491, 0.118952680023042, 0.141748124176178, -0.120072924149705, -0.0417199784040522, 0.0837418857774159, -0.0386675911959832, 0.0113621668615782, -0.0165793698112683, 0.146522255839915, 0.166099245009794, -0.0239915790387723, 0.0764956513097508, 0.0671589896260352, 0.0493539480970899, 1.06222147579554, 1.1002507213572, 0.768991594381703, 0.618983350337493, 1.28678875419635, 1.67142706961048, 0.786135027597283, 0.636531428934211, 1.09154115621558, 1.11501277174014, 0.758613312330241, 0.693911211524711, -0.00264628084991393, -0.020670859392103, 0.0206585540721674, 0.0076120270915056, 0.01977840827582, 0.00887062962888688, 0.0857746926208271, 0.0129649274588139, 0.0930336131142227, 0.0639141756310739, 0.00174847079953262, 0.0826773995441732, 0.0577716250034614, 0.0240332716358964, 0.062104419291224, 0.107363949033218, 0.0501041505326083, -0.0113399097133595, -0.106723079606281, -0.0729739856394577, 0.0190914135644343, 0.0252065688162364, -0.00386514223473116, -0.0385217266559336, -0.0235887381762751, -0.104565183720226, -0.116865540995329, 0.553093374537492, 0.742943487462754, 1.17427537880214, 1.14976137667066, 0.605225158300069, 0.786135027597283, 1.75724466187761, 1.42283630209259, 0.549741259193713, 0.708475550052955, 1.22914973668607, 1.25763749578798, -0.0296908946602319, 0.0104200414391291, -0.0145720736487281, 0.0033195334097242, 0.0304989245705094, 0.0248134478800154, 0.019809047212115, -0.0278335902754632, 0.0775381432887526, 0.0170624923090307, -0.00828708360663368, 0.0575131343085677, -0.0862221610584902, -0.0960292154420592, -0.121941490751061, 0.109435686051998, 0.0809914034723241, 0.0479013123640107, 0.0285854186705205, -0.0317318220007143, 0.169073277814853, 0.138836295431548, 0.0174182527862748, 0.0430732015144227, 0.0516400329150374, 0.0255762326754712, 0.0187955189173209, 0.447838225838153, 0.64428374039232, 1.1139472986559, 1.42361294424794, 0.490049191698183, 0.636531428934211, 1.42283630209259, 2.14367349457838, 0.499841178456957, 0.617558201846951, 1.24599523308296, 1.40112741396321, -0.0394721249953512, 0.0370330313771152, -0.0431359339076604, -0.00738449211276925, -0.0854782307059588, 0.0247920992730687, -0.0973040204633906, -0.0424219899345994, -0.10773335775287, -0.0699475831262572, -0.0137548007419955, -0.0746340366111427, -0.14926687959738, -0.122855476743761, -0.153408764961069, -0.169568169571692, -0.102201933410529, 0.0827363125250564, -0.00626559185412116, -0.0866730771247237, -0.057167462611575, 0.0964684652932333, 0.0997440147141751, -0.111115021635821, 0.0202881363683058, 0.0375171654291814, 0.00380285492607959, 1.35577768351094, 1.10176290111632, 0.644167387922915, 0.486061415374205, 1.2559069292527, 1.09154115621558, 0.549741259193713, 0.499841178456957, 1.99735181817812, 1.37283056092203, 0.636397275021004, 0.544899092090007, -0.03708872669238, -0.00935414748115647, -0.00786735696938561, -0.0369559317351903, -0.0235867391754614, 0.00555327458708414, -0.0315362339684262, 0.00408552460269166, -0.000542481453965008, -0.0349182900330659, -0.0522477483772834, -0.0194906592088833, 0.0890398368213926, 0.131204656804499, 0.153319351221939, -0.238028958705595, -0.130802860893886, -0.0337960621007554, -0.0310691387157628, -0.174358692112653, -0.154022807307125, 0.0827491883696063, 0.103034832027179, -0.132288435076582, -0.0352304618528652, -0.0508677792365115, -0.0183094663189545, 1.11397687818633, 1.11577341589458, 0.76968868646083, 0.600533182544757, 1.04757622393096, 1.11501277174014, 0.708475550052955,   > test-esem.R: 0.617558201846951, 1.37283056092203, 1.69614431380138, 0.786274468293161, 0.673227653107653, -0.0249870847892577, -0.0310623448791116, -0.0597662654253912, -0.0500516446493022, -0.0205554406398311, 0.00347675808841012, -0.0686655886007817, 0.00959998906957744, -0.00634275627248089, -0.0513443431595157, -0.00110780602245252, -0.0123907039927979, 0.0395883128666614, 0.0716961735842288, 0.0545351763997118, -0.0135053916825915, -0.0148660064484897, -0.0540468233753762, -0.135030448940603, -0.0760810863132528, 0.0880784013810652, 0.120136315645039, 0.0653127512676339, -0.00476876138498948, -0.0595609077281687, -0.0647733138112737, -0.0671092644366832, 0.570187169159342, 0.743140939070639, 1.18316692085857, 1.05121838213894, 0.623930127537152, 0.758613312330241, 1.22914973668607, 1.24599523308296, 0.636397275021004, 0.786274468293161, 1.72458799132095, 1.47663490650412, -0.0171591736580541, 0.0168446332834493, 0.0156862359188135, 0.033364333452295, 0.017604243021788, 0.0417778807218433, -0.0266860377378277, -0.00837804674737573, -0.00682932784787414, -0.0185360321639612, 0.00923449433845097, 0.0103019254283266, -0.0913713589057094, -0.0780111274536073, -0.103938786698107, -0.0269051436957957, 0.0449437782925636, 0.0344537680314544, -0.116121963366209, -0.0604073968525698, 0.0342100668274374, -0.00321012859252068, -0.0071150608221477, -0.0301359480319713, 0.0516911215418863, -0.0137328211449501, -0.0330283840404653, 0.488208361335366, 0.702362350920954, 1.25985049764384, 1.15403067874422, 0.534224411962442, 0.693911211524711, 1.25763749578798, 1.40112741396321, 0.544899092090007, 0.673227653107653, 1.47663490650412, 1.94476224295417, -0.0600572970215252, -0.0129462981571512, -0.0383334777746061, 0.0618573160951042, 0.0275218234991843, 0.0130731844584642, -0.0693311489782209, 0.0476073305366232, -0.00861177497025037, -0.104231047599455, -0.00601795258733618, 0.00995636294223921, -0.110540740333587, -0.122682130683992, -0.132968263756207, 0.148317263761989, 0.0741651928462058, -0.0835035702275452, -0.159334208125366, 0.161942977081381, 0.0539361700938887, -0.0858800421247186, -0.134071027660114, 0.138783846835874, 0.0687933674969333, -0.0724591490523034, -0.139306280370751, -0.0452073659278695, -0.0228496292950282, -0.0497100849928933, -0.0158659044150101, -0.00822479371015103, -0.00264628084991393, -0.0296908946602319, -0.0394721249953512, -0.03708872669238, -0.0249870847892577, -0.0171591736580541, -0.0600572970215252, 0.13435793157891, 0.0648999991313963, 0.0163257727891063, 0.0222001474813885, 0.00586415479170394, 0.0553201434932848, -0.00774333873252011, -0.00406552748573637, -0.00893407703646892, 0.00190480732538636, 0.00711445555663318, -0.00993507647126291, -0.00346764772535688, -0.00584038224322596, -0.0043281721243053, 0.15574465153135, 0.0490907248507852, -0.0786770150818312, -0.139085742363334, 0.120827352177701, 0.0615567447500341, -0.0451906419084792, -0.0976632797843361, 0.13451304238041, 0.0369020237224324, -0.0791267384446038, -0.13875441713431, -0.0368039242195606, -0.00169230710749049, 0.0114243324019316, 0.0563346056670746, 0.00663271019471846, -0.020670859392103, 0.0104200414391291, 0.0370330313771152, -0.00935414748115647, -0.0310623448791116, 0.0168446332834493, -0.0129462981571512, 0.0648999991313963, 0.148076578586997, 0.0102311998810714, 0.0360677109152718, 0.0157559499489283, 0.0471580164973155, -0.024660772824432, 0.00213812557707964, -0.0216906952994601, -0.0148339752411867, 0.018408323287891, -0.0189706498549756, -0.0118850547463466, -0.00878063137456458, -0.00695610027104775, -0.0301086318650708, -0.0176892046762685, 0.0106760261359684, 0.0271974707303992, -0.0919049501204477, -0.0681981562446394, -0.0765124055981449, -0.137740647801462, -0.0723053721025387, 0.00995076809763175, 0.00181309216736721, 0.0668223880050227, -0.0249465575779013, -0.0100558231103412, 0.0145262770963222, 0.0236919740312106, 0.0194650089727021, 0.0206585540721674, -0.0145720736487281, -0.0431359339076604, -0.00786735696938561, -0.0597662654253913, 0.0156862359188135, -0.0383334777746061, 0.0163257727891063, 0.0102311998810714, 1.46059310082162, 1.05092773064802, 1.14570089017992, -0.000204120425811351, 1.0924386835732, 0.800404470692711, 0.866078362448077, 1.06893041162047, 0.767172388010609, 0.812608776357063, 5.64931156303635e-06, 0.0102626772585488, 0.00490334272631464, 0.00933114409349929, 0.00394541784101922, -0.00123494659659295, -0.0071037068500013, -0.0267557831346448, -0.0246495398767953, -0.0495654183174262, -0.129333918235308, 0.0235559639025693, 0.064092949342599, -0.0214221821010069, -0.0448927653441914, -0.157562953805574, -0.00378787104048014, 0.0972680602138176, 0.103285956119985, 0.00885836454286384, 0.00761202709150559, 0.00331953340972421, -0.00738449211276925, -0.0369559317351903, -0.0500516446493022, 0.033364333452295, 0.0618573160951042, 0.0222001474813885, 0.0360677109152718, 1.05092773064801, 1.33851241644687, 1.0552643434031, 0.0164763439450202, 0.793710751295646, 1.05114166617281, 0.785239966039694, 0.790106915227831, 1.02177932037355, 0.772375497705721, -0.0457924736654755, -0.0294483813254788, -0.0249420219318624, -0.0308508864967572, -0.00847157050708947, 0.0122565252286696, 0.0263069574461733, -0.0881523522151011, -0.0554549756281442, -0.0848028980005815, -0.117770415220244, -0.0229809041838941, 0.0369408306412495, -0.0300017587548346, 0.0318365551567393, -0.0510773841561005, -0.0243505783648102, 0.0637726699623928, -0.000130894536512413, 0.0422893759118978, 0.01977840827582, 0.0304989245705094, -0.0854782307059588, -0.0235867391754614, -0.0205554406398311, 0.017604243021788, 0.0275218234991843, 0.00586415479170394, 0.0157559499489283, 1.14570089017992, 1.0552643434031, 1.40456321796895, 0.002375688741747, 0.854793847130113, 0.798909003312101, 1.08161905449057, 0.881489941991868, 0.812215657741097, 1.0699033568652, -0.0256936941471223, -0.0192954594118436, 0.00388272127596111, 0.110777263526235, 0.0757812257161068, -0.0592467719700587, -0.128992483494249, 0.128137179839506, 0.0717566895580845, -0.0833098166158672, -0.155270199765802, 0.14579926213725, 0.0799479942345352, -0.0877579746528331, -0.151806904210309, -0.0144087480818, 0.0045802720538116, 0.012559878300944, 0.0205212058726693, 0.00923383989163264, 0.00887062962888688, 0.0248134478800154, 0.0247920992730687, 0.00555327458708413, 0.00347675808841012, 0.0417778807218433, 0.0130731844584642, 0.0553201434932848, 0.0471580164973155, -0.000204120425811351, 0.0164763439450202, 0.002375688741747, 0.0860106118444142, -0.019900141742653, -0.00853341765654292, -0.0214039745610184, 0.00329365658923978, 0.018798081814193, 0.00581948595103035, -0.00488309654925988, 0.00560377528198599, -0.0120229152759623, 0.00948677799058492, -0.0661029519937833, -0.0412145589031353, 0.107707346627344, 0.000212298869746286, 5.75617068664727e-06, -0.000535439721952491, 0.00279700652525929, -0.0346675510650626, 0.005592058134837, 0.10479954023783, 0.148960920619109, -0.000875250301410178, 0.0072931844882099, -0.00486631793652915, -0.00732081477362901, 0.0428107979498749, 0.0857746926208272, 0.019809047212115, -0.0973040204633905, -0.0315362339684262, -0.0686655886007816, -0.0266860377378277, -0.0693311489782208, -0.00774333873252011, -0.024660772824432, 1.0924386835732, 0.793710751295646, 0.854793847130113, -0.019900141742653, 1.38970316324119, 0.981893200602164, 1.08597201729475, 1.11220858116664, 0.765698559147951, 0.82894666600568, 0.0142986764803992, 0.010614318885733, 0.0160737255995745, -0.0041208933928698, -0.0252830371146104, -0.0176224865986117, 0.0946179973328222, 0.0367164551098905, 0.0204479578807549, -0.0120376783586505, -0.029357693070375, 0.0278921595163239, 0.0487302938257758, 0.0704790601404688, 0.0609038763998929, -0.102613201353469, 0.0359904807552618, 0.0635513532398554, 0.0875644703300589, 0.0357418739580544, 0.0129649274588139, -0.0278335902754632, -0.0424219899345994, 0.00408552460269165, 0.00959998906957744, -0.00837804674737573, 0.0476073305366232, -0.00406552748573637, 0.00213812557707964, 0.800404470692711, 1.05114166617281, 0.798909003312101, -0.00853341765654293, 0.981893200602164, 1.28126824855252, 0.953252779725438, 0.79984192501233, 1.00662688239373, 0.729381828929068, -0.0329019063108192, -0.0136956271030428, -0.0107407756452412, -0.10125465414037, -0.0476247917553443, -0.0380983101009663, 0.127452670358713, -0.00373340788228093, 7.74832431628925e-05, 0.00713013186502569, 0.00385735432182198, -0.00295841798764691, 0.0493669036835192, 0.0725730814762955, 0.134305485249476, 0.000573271171747709, -0.0170014618557995, 0.0254927803908756, -0.00850467831901116, 0.142453716502339, 0.0930336131142227, 0.0775381432887526, -0.10773335775287, -0.000542481453965015, -0.0063427562724809, -0.00682932784787413, -0.00861177497025037, -0.00893407703646892, -0.0216906952994601, 0.866078362448077, 0.785239966039694, 1.08161905449057, -0.0214039745610184, 1.08597201729475, 0.953252779725438, 1.35498763334885, 0.910480398190892, 0.75340726253597, 1.0564303582682, 0.00579222433398088, -0.00895211036399707, 0.0266774382853429, 0.0785862105655182, -0.0264569477560322, -0.0402960408343714, 0.110027033838194, -0.000447770974788044, 0.00318648418314012, 0.00519137622969441, -0.0242062457633788, -0.0143270955539256, -0.0138630042221855, 0.0132397293060142, 0.031169280380637, -0.0360467188781403, -0.0151073514301341, 0.0064493662501605, 0.0127667492155082, 0.0307344604466001, 0.0639141756310739, 0.0170624923090307, -0.0699475831262572, -0.0349182900330659, -0.0513443431595157, -0.0185360321639612, -0.104231047599455, 0.00190480732538636, -0.0148339752411867, 1.06893041162047, 0.790106915227831, 0.881489941991868, 0.00329365658923978, 1.11220858116664, 0.79984192501233, 0.910480398190892, 1.45343080732047, 1.05062470048119, 1.10783285444028, 0.00482548296748707, 0.0192021029400786, 0.0155516790039065, 0.0555335781266458, 0.0323015886413371, -0.0522514103795643, 0.0789431409081487, 0.0150078675242675, 0.0406344952019763, 0.0317853983114957,   > test-esem.R: -0.0532930035886563, 0.0186565372953721, 0.00884439739842633, 0.00392597583562098, -0.0100546599966053, -0.106138266021218, 0.0160490751802889, 0.0984423108227788, 0.17068413621789, 0.0109412500742362, 0.00174847079953263, -0.00828708360663369, -0.0137548007419955, -0.0522477483772834, -0.00110780602245253, 0.00923449433845097, -0.00601795258733618, 0.00711445555663318, 0.018408323287891, 0.76717238801061, 1.02177932037355, 0.812215657741097, 0.018798081814193, 0.765698559147951, 1.00662688239373, 0.75340726253597, 1.05062470048119, 1.30720068817729, 1.01913616621123, -0.0536652115645841, -0.019213861470815, -0.0172908302087426, -0.0638553156597295, -0.0169096775619646, -0.0445701055393561, 0.0904561706745593, -0.0585860797001331, 0.00971501942601363, 0.0120483492539913, -0.00857750561667388, -0.0162965216843151, -0.00367260312735112, 0.00574676565142401, 0.0170376579092655, -0.00157974786814208, 0.00744705291894205, 0.0677157657287604, 0.0662442892600502, 0.0843708232885414, 0.0826773995441732, 0.0575131343085677, -0.0746340366111427, -0.0194906592088833, -0.0123907039927979, 0.0103019254283266, 0.00995636294223919, -0.00993507647126291, -0.0189706498549756, 0.812608776357063, 0.772375497705721, 1.0699033568652, 0.00581948595103035, 0.82894666600568, 0.729381828929068, 1.0564303582682, 1.10783285444028, 1.01913616621123, 1.33101934741115, -0.0160332825788463, -0.0194673047264479, 0.0076134333416248, -0.0280957630388753, -0.00652515275734159, 0.0398832411621319, 0.00997923231952932, -0.00317128543875011, -0.00345081806988481, 0.0335091835712613, 0.0767430961264705, -0.00917673037167745, 0.0351601326522669, 0.0545645893278382, 0.0337745421542386, 0.155628564410312, 0.0506965702279097, -0.0765907638253904, -0.152316211916453, 0.134776830038491, 0.0577716250034614, -0.0862221610584902, -0.14926687959738, 0.0890398368213926, 0.0395883128666614, -0.0913713589057094, -0.110540740333588, -0.00346764772535688, -0.0118850547463466, 5.64931156303779e-06, -0.0457924736654755, -0.0256936941471223, -0.00488309654925988, 0.0142986764803992, -0.0329019063108192, 0.00579222433398088, 0.00482548296748707, -0.0536652115645841, -0.0160332825788463, 0.120861870687388, 0.0659352781421284, 0.0637822024211485, -0.00123067312283201, -0.0245753162929485, 0.005478944302505, -0.00851490425935088, -0.0178213100814595, -0.0155256161093073, -0.0158877338895445, -0.00352146452370504, 0.00379043849646447, 0.00839475638588036, 0.00351526032811938, -0.00021124007570947, 0.153818992474422, 0.0646531323927172, -0.07315026181056, -0.145043021521224, 0.118952680023042, 0.0240332716358964, -0.0960292154420592, -0.122855476743761, 0.131204656804499, 0.0716961735842288, -0.0780111274536073, -0.122682130683992, -0.00584038224322596, -0.00878063137456458, 0.0102626772585488, -0.0294483813254788, -0.0192954594118436, 0.00560377528198599, 0.010614318885733, -0.0136956271030428, -0.00895211036399707, 0.0192021029400786, -0.019213861470815, -0.0194673047264479, 0.0659352781421284, 0.118049819131347, 0.0482278273076368, -0.0342607132374605, -0.0110359863231057, 0.0388495658350893, 0.020452480807499, -0.00791788041762326, 0.0078896468194146, 0.0458021905858184, 0.0679798285981358, 0.00436030761914487, 0.0119231550404147, 0.0482644269885699, 0.0322855829479996, 0.166944494374591, 0.0136332585073733, -0.0918071222196781, -0.119582194867943, 0.141748124176178, 0.0621044192912241, -0.121941490751061, -0.153408764961069, 0.153319351221939, 0.0545351763997117, -0.103938786698107, -0.132968263756207, -0.00432817212430529, -0.00695610027104775, 0.00490334272631464, -0.0249420219318624, 0.00388272127596111, -0.0120229152759623, 0.0160737255995745, -0.0107407756452412, 0.0266774382853429, 0.0155516790039065, -0.0172908302087426, 0.0076134333416248, 0.0637822024211485, 0.0482278273076368, 0.0987931493939915)), NACOV.user = FALSE, missing.flag = FALSE, missing = list(NULL), missing.h1 = list( NULL), YLp = list(NULL), zero.cell.tables = list( c("x1", "x1", "x2", "x4", "x4", "x5", "x2", "x3", "x3", "x5", "x6", "x6"))))   > test-esem.R: 15: do.call(lavaan::efa, efa_args)   > test-esem.R: 16: withCallingHandlers(expr, warning = function(w) if (inherits(w, classes)) tryInvokeRestart("muffleWarning"))   > test-esem.R: 17: suppressWarnings(do.call(lavaan::efa, efa_args))   > test-esem.R: 18: doTryCatch(return(expr), name, parentenv, handler)   > test-esem.R: 19: tryCatchOne(expr, names, parentenv, handlers[[1L]])   > test-esem.R: 20: tryCatchList(expr, classes, parentenv, handlers)   > test-esem.R: 21: tryCatch(suppressWarnings(do.call(lavaan::efa, efa_args)), error = function(e) { structure(list(msg = conditionMessage(e)), class = "esem_fit_error")})   > test-esem.R: 22: .esem_fit_one(k, data_df, estimator, cor, ordered_cols, lav_missing, ss_in = ss, r_lv_in = r_lv, item_names = item_names, p = p, keep_fit = keep_fits)   > test-esem.R: 23: ...future.FUN(...future.X_jj, ...)   > test-esem.R: 24: FUN(X[[i]], ...)   > test-esem.R: 25: lapply(seq_along(...future.elements_ii), FUN = function(jj) { ...future.X_jj <- ...future.elements_ii[[jj]] assign(".Random.seed", ...future.seeds_ii[[jj]], envir = globalenv(), inherits = FALSE) { ...future.FUN(...future.X_jj, ...) }})   > test-esem.R: 26: (function (...) { "# future::getGlobalsAndPackages(): FUN() uses '...' internally " "# without having an '...' argument. This means '...' is treated" "# as a global variable. This may happen when FUN() is an " "# anonymous function. " "# " "# If an anonymous function, we will make sure to restore the " "# function environment of FUN() to the calling environment. " "# We assume FUN() an anonymous function if it lives in the " "# global environment, which is where globals are written. " penv <- env <- environment(...future.FUN) repeat { if (identical(env, globalenv()) || identical(env, emptyenv())) break penv <- env env <- parent.env(env) } if (identical(penv, globalenv())) { environment(...future.FUN) <- environment() } else if (!identical(penv, emptyenv()) && !is.null(penv) && !isNamespace(penv)) { parent.env(penv) <- environment() } rm(list = c("env", "penv"), inherits = FALSE) { "# future.apply:::future_xapply(): preserve future option" ...future.globals.maxSize.org <- getOption("future.globals.maxSize") if (!identical(...future.globals.maxSize.org, ...future.globals.maxSize)) { oopts <- options(future.globals.maxSize = ...future.globals.maxSize) on.exit(options(oopts), add = TRUE) } { "# future.apply::future_lapply(): process chunk of elements while setting random seeds" lapply(seq_along(...future.elements_ii), FUN = function(jj) { ...future.X_jj <- ...future.elements_ii[[jj]] assign(".Random.seed", ...future.seeds_ii[[jj]], envir = globalenv(), inherits = FALSE) { ...future.FUN(...future.X_jj, ...) } }) } }})()   > test-esem.R: 27: do.call(function(...) { "# future::getGlobalsAndPackages(): FUN() uses '...' internally " "# without having an '...' argument. This means '...' is treated" "# as a global variable. This may happen when FUN() is an " "# anonymous function. " "# " "# If an anonymous function, we will make sure to restore the " "# function environment of FUN() to the calling environment. " "# We assume FUN() an anonymous function if it lives in the "   > test-esem.R: "# global environment, which is where globals are written. " penv <- env <- environment(...future.FUN) repeat { if (identical(env, globalenv()) || identical(env, emptyenv())) break penv <- env env <- parent.env(env) } if (identical(penv, globalenv())) { environment(...future.FUN) <- environment() } else if (!identical(penv, emptyenv()) && !is.null(penv) && !isNamespace(penv)) { parent.env(penv) <- environment() } rm(list = c("env", "penv"), inherits = FALSE) { "# future.apply:::future_xapply(): preserve future option" ...future.globals.maxSize.org <- getOption("future.globals.maxSize") if (!identical(...future.globals.maxSize.org, ...future.globals.maxSize)) { oopts <- options(future.globals.maxSize = ...future.globals.maxSize) on.exit(options(oopts), add = TRUE) } { "# future.apply::future_lapply(): process chunk of elements while setting random seeds" lapply(seq_along(...future.elements_ii), FUN = function(jj) { ...future.X_jj <- ...future.elements_ii[[jj]] assign(".Random.seed", ...future.seeds_ii[[jj]], envir = globalenv(), inherits = FALSE) { ...future.FUN(...future.X_jj, ...) } }) } }}, args = future.call.arguments)   > test-esem.R: 28: eval(quote({ { "# future::getGlobalsAndPackages(): wrapping the original future" "# expression in do.call(), because function called uses '...' " "# as a global variable " do.call(function(...) { "# future::getGlobalsAndPackages(): FUN() uses '...' internally " "# without having an '...' argument. This means '...' is treated" "# as a global variable. This may happen when FUN() is an " "# anonymous function. " "# " "# If an anonymous function, we will make sure to restore the " "# function environment of FUN() to the calling environment. " "# We assume FUN() an anonymous function if it lives in the " "# global environment, which is where globals are written. " penv <- env <- environment(...future.FUN) repeat { if (identical(env, globalenv()) || identical(env, emptyenv())) break penv <- env env <- parent.env(env) } if (identical(penv, globalenv())) { environment(...future.FUN) <- environment() } else if (!identical(penv, emptyenv()) && !is.null(penv) && !isNamespace(penv)) { parent.env(penv) <- environment() } rm(list = c("env", "penv"), inherits = FALSE) { "# future.apply:::future_xapply(): preserve future option" ...future.globals.maxSize.org <- getOption("future.globals.maxSize") if (!identical(...future.globals.maxSize.org, ...future.globals.maxSize)) { oopts <- options(future.globals.maxSize = ...future.globals.maxSize) on.exit(options(oopts), add = TRUE) } { "# future.apply::future_lapply(): process chunk of elements while setting random seeds" lapply(seq_along(...future.elements_ii), FUN = function(jj) { ...future.X_jj <- ...future.elements_ii[[jj]] assign(".Random.seed", ...future.seeds_ii[[jj]], envir = globalenv(), inherits = FALSE) { ...future.FUN(...future.X_jj, ...) } }) } } }, args = future.call.arguments) }}), new.env())   > test-esem.R: 29: eval(quote({ { "# future::getGlobalsAndPackages(): wrapping the original future" "# expression in do.call(), because function called uses '...' " "# as a global variable " do.call(function(...) { "# future::getGlobalsAndPackages(): FUN() uses '...' internally " "# without having an '...' argument. This means '...' is treated" "# as a global variable. This may happen when FUN() is an " "# anonymous function. " "# " "# If an anonymous function, we will make sure to restore the " "# function environment of FUN() to the calling environment. " "# We assume FUN() an anonymous function if it lives in the " "# global environment, which is where globals are written. " penv <- env <- environment(...future.FUN) repeat { if (identical(env, globalenv()) || identical(env, emptyenv())) break penv <- env env <- parent.env(env) } if (identical(penv, globalenv())) { environment(...future.FUN) <- environment() } else if (!identical(penv, emptyenv()) && !is.null(penv) && !isNamespace(penv)) { parent.env(penv) <- environment() } rm(list = c("env", "penv"), inherits = FALSE) { "# future.apply:::future_xapply(): preserve future option" ...future.globals.maxSize.org <- getOption("future.globals.maxSize") if (!identical(...future.globals.maxSize.org, ...future.globals.maxSize)) { oopts <- options(future.globals.maxSize = ...future.globals.maxSize) on.exit(options(oopts), add = TRUE) } { "# future.apply::future_lapply(): process chunk of elements while setting random seeds" lapply(seq_along(...future.elements_ii), FUN = function(jj) { ...future.X_jj <- ...future.elements_ii[[jj]] assign(".Random.seed", ...future.seeds_ii[[jj]], envir = globalenv(), inherits = FALSE) { ...future.FUN(...future.X_jj, ...) } }) } } }, args = future.call.arguments) }}), new.env())   > test-esem.R: 30: eval(expr, p)   > test-esem.R: 31: eval(expr, p)   > test-esem.R: 32: eval.parent(substitute(eval(quote(expr), envir)))   > test-esem.R: 33: local({ { "# future::getGlobalsAndPackages(): wrapping the original future" "# expression in do.call(), because function called uses '...' " "# as a global variable " do.call(function(...) { "# future::getGlobalsAndPackages(): FUN() uses '...' internally " "# without having an '...' argument. This means '...' is treated" "# as a global variable. This may happen when FUN() is an " "# anonymous function. " "# " "# If an anonymous function, we will make sure to restore the " "# function environment of FUN() to the calling environment. " "# We assume FUN() an anonymous function if it lives in the " "# global environment, which is where globals are written. " penv <- env <- environment(...future.FUN) repeat { if (identical(env, globalenv()) || identical(env, emptyenv())) break penv <- env env <- parent.env(env) } if (identical(penv, globalenv())) { environment(...future.FUN) <- environment() } else if (!identical(penv, emptyenv()) && !is.null(penv) && !isNamespace(penv)) {   > test-esem.R: parent.env(penv) <- environment() } rm(list = c("env", "penv"), inherits = FALSE) { "# future.apply:::future_xapply(): preserve future option" ...future.globals.maxSize.org <- getOption("future.globals.maxSize") if (!identical(...future.globals.maxSize.org, ...future.globals.maxSize)) { oopts <- options(future.globals.maxSize = ...future.globals.maxSize) on.exit(options(oopts), add = TRUE) } { "# future.apply::future_lapply(): process chunk of elements while setting random seeds" lapply(seq_along(...future.elements_ii), FUN = function(jj) { ...future.X_jj <- ...future.elements_ii[[jj]] assign(".Random.seed", ...future.seeds_ii[[jj]], envir = globalenv(), inherits = FALSE) { ...future.FUN(...future.X_jj, ...) } }) } } }, args = future.call.arguments) }})   > test-esem.R: 34: eval(expr, envir = globalenv())   > test-esem.R: 35: eval(expr, envir = globalenv())   > test-esem.R: 36: withVisible({ eval(expr, envir = globalenv())})   > test-esem.R: 37: withCallingHandlers({ ...future.value <- withVisible({ eval(expr, envir = globalenv()) }) seed <- globalenv()[[".Random.seed"]] FutureResult(value = ...future.value[["value"]], visible = ...future.value[["visible"]], conditions = ...future.conditions, rng = !identical(seed, ...future.rng), seed = seed, uuid = uuid, misuseGlobalEnv = if (checkGlobalenv) list(added = diff_globalenv(...future.globalenv.names)) else NULL, misuseConnections = if (checkConnections) diff_connections(get_connections(details = isTRUE(attr(checkConnections, "details", exact = TRUE))), ...future.connections) else NULL, misuseDevices = if (checkDevices) diff_devices(...future.devices, base::.Devices) else NULL, misuseDefaultDevice = ...future.option.defaultDevice, started = ...future.startTime)}, condition = onEvalCondition)   > test-esem.R: 38: tryCatchList(expr, classes, parentenv, handlers)   > test-esem.R: 39: tryCatch({ withCallingHandlers({ ...future.value <- withVisible({ eval(expr, envir = globalenv()) }) seed <- globalenv()[[".Random.seed"]] FutureResult(value = ...future.value[["value"]], visible = ...future.value[["visible"]], conditions = ...future.conditions, rng = !identical(seed, ...future.rng), seed = seed, uuid = uuid, misuseGlobalEnv = if (checkGlobalenv) list(added = diff_globalenv(...future.globalenv.names)) else NULL, misuseConnections = if (checkConnections) diff_connections(get_connections(details = isTRUE(attr(checkConnections, "details", exact = TRUE))), ...future.connections) else NULL, misuseDevices = if (checkDevices) diff_devices(...future.devices, base::.Devices) else NULL, misuseDefaultDevice = ...future.option.defaultDevice, started = ...future.startTime) }, condition = onEvalCondition)}, finally = { setTimeLimit(cpu = Inf, elapsed = Inf, transient = FALSE)})   > test-esem.R: 40: doTryCatch(return(expr), name, parentenv, handler)   > test-esem.R: 41: tryCatchOne(expr, names, parentenv, handlers[[1L]])   > test-esem.R: 42: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])   > test-esem.R: 43: doTryCatch(return(expr), name, parentenv, handler)   > test-esem.R: 44: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), names[nh], parentenv, handlers[[nh]])   > test-esem.R: 45: tryCatchList(expr, classes, parentenv, handlers)   > test-esem.R: 46: tryCatch({ tryCatch({ withCallingHandlers({ ...future.value <- withVisible({ eval(expr, envir = globalenv()) }) seed <- globalenv()[[".Random.seed"]] FutureResult(value = ...future.value[["value"]], visible = ...future.value[["visible"]], conditions = ...future.conditions, rng = !identical(seed, ...future.rng), seed = seed, uuid = uuid, misuseGlobalEnv = if (checkGlobalenv) list(added = diff_globalenv(...future.globalenv.names)) else NULL, misuseConnections = if (checkConnections) diff_connections(get_connections(details = isTRUE(attr(checkConnections, "details", exact = TRUE))), ...future.connections) else NULL, misuseDevices = if (checkDevices) diff_devices(...future.devices, base::.Devices) else NULL, misuseDefaultDevice = ...future.option.defaultDevice, started = ...future.startTime) }, condition = onEvalCondition) }, finally = { setTimeLimit(cpu = Inf, elapsed = Inf, transient = FALSE) })}, interrupt = onEvalErrorOrInterrupt, error = onEvalErrorOrInterrupt)   > test-esem.R: 47: evalFutureInternal(data)   > test-esem.R: 48: doTryCatch(return(expr), name, parentenv, handler)   > test-esem.R: 49: tryCatchOne(expr, names, parentenv, handlers[[1L]])   > test-esem.R: 50: tryCatchList(expr, classes, parentenv, handlers)   > test-esem.R: 51: tryCatch({ evalFutureInternal(data)}, error = function(ex) { msg <- sprintf("future::evalFuture() failed on %s (pid %s) at %s", Sys.info()[["nodename"]], Sys.getpid(), format(Sys.time(), "%FT%T")) if (!requireNamespace("future")) { msg <- sprintf("%s. Package 'future' is not available (worker library path: %s)", msg, paste(sQuote(.libPaths()), collapse = ", ")) } else { ns <- getNamespace("future") if (!exists("evalFutureInternal", mode = "function", envir = ns, inherits = FALSE)) { msg <- sprintf("%s. Package 'future' version %s is too old. Please update and retry", msg, packageVersion("future")) } else { msg <- sprintf("%s. Using package 'future' v%s", msg, packageVersion("future")) } } msg <- sprintf("%s. Possible other reasons: %s", msg, conditionMessage(ex)) call <- conditionCall(ex) if (!is.null(call)) { call <- deparse(call, width.cutoff = 500L) call <- paste(call, collapse = " -> ") msg <- sprintf("%s [in %s]", msg, call) } ex <- simpleError(msg) class(ex) <- c("FutureLaunchError", "FutureError", class(ex)) ex})   > test-esem.R: 52: evalFuture(data)   > test-esem.R: 53: eval(expr, env)   > test-esem.R: 54: doTryCatch(return(expr), name, parentenv, handler)   > test-esem.R: 55: tryCatchOne(expr, names, parentenv, handlers[[1L]])   > test-esem.R: 56: tryCatchList(expr, classes, parentenv, handlers)   > test-esem.R: 57: tryCatch(expr, error = function(e) { call <- conditionCall(e) if (!is.null(call)) { if (identical(call[[1L]], quote(doTryCatch))) call <- sys.call(-4L) dcall <- deparse(call, nlines = 1L) prefix <- paste("Error in", dcall, ": ") LONG <- 75L sm <- strsplit(conditionMessage(e), "\n")[[1L]] w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w") if (is.na(w)) w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L], type = "b") if (w > LONG) prefix <- paste0(prefix, "\n ") } else prefix <- "Error : " msg <- paste0(prefix, conditionMessage(e), "\n") .Internal(seterrmessage(msg[1L])) if (!silent && isTRUE(getOption("show.error.messages"))) { cat(msg, file = outFile) .Internal(printDeferredWarnings()) } invisible(structure(msg, class = "try-error", condition = e))})   > test-esem.R: 58: try(eval(expr, env), silent = TRUE)   > test-esem.R: 59: serialize(what, NULL, xdr = FALSE)   > test-esem.R: 60: sendMaster(try(eval(expr, env), silent = TRUE), FALSE)   > test-esem.R: 61: mcparallel(evalFuture(data))   > test-esem.R: 62: (function() { oopts <- options(mc.cores = NULL) on.exit(options(oopts)) mcparallel(evalFuture(data))})()   > test-esem.R: 63: launchFuture.MulticoreFutureBackend(backend, future = future)   > test-esem.R: 64: launchFuture(backend, future = future)   > test-esem.R: 65: doTryCatch(return(expr), name, parentenv, handler)   > test-esem.R: 66: tryCatchOne(expr, names, parentenv, handlers[[1L]])   > test-esem.R: 67: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])   > test-esem.R: 68: doTryCatch(return(expr), name, parentenv, handler)   > test-esem.R: 69:   > test-esem.R: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), names[nh], parentenv, handlers[[nh]])   > test-esem.R: 70: tryCatchList(expr, classes, parentenv, handlers)   > test-esem.R: 71: tryCatch({ launchFuture(backend, future = future)}, FutureError = function(ex) { future[["state"]] <- "failed" stop(ex)}, error = function(ex) { msg <- conditionMessage(ex) label <- sQuoteLabel(future) msg <- sprintf("Caught an unexpected error of class %s when trying to launch future (%s) on backend of class %s. The reason was: %s", class(ex)[1], label, class(backend)[1], msg) future[["state"]] <- "failed" stop(FutureLaunchError(msg, future = future))})   > test-esem.R: 72: run.Future(future)   > test-esem.R: 73: run(future)   > test-esem.R: 74: future(expr, substitute = FALSE, envir = future.envir, stdout = future.stdout, conditions = future.conditions, globals = globals_ii, packages = packages_ii, seed = future.seed, label = labels[ii])   > test-esem.R: 75: doTryCatch(return(expr), name, parentenv, handler)   > test-esem.R: 76: tryCatchOne(expr, names, parentenv, handlers[[1L]])   > test-esem.R: 77: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])   > test-esem.R: 78: doTryCatch(return(expr), name, parentenv, handler)   > test-esem.R: 79: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), names[nh], parentenv, handlers[[nh]])   > test-esem.R: 80: tryCatchList(expr, classes, parentenv, handlers)   > test-esem.R: 81: tryCatch({ for (ii in seq_along(chunks)) { chunk <- chunks[[ii]] if (debug) mdebugf("Chunk #%d of %d ...", ii, length(chunks)) args_ii <- get_chunk(chunk_args, chunk) globals_ii <- globals globals_ii[["...future.elements_ii"]] <- args_ii packages_ii <- packages if (scanForGlobals) { if (debug) mdebugf(" - Finding globals in '%s' for chunk #%d ...", args_name, ii) gp <- getGlobalsAndPackages(args_ii, envir = envir, globals = TRUE) globals_args <- gp$globals packages_args <- gp$packages gp <- NULL if (debug) { mdebugf(" + additional globals found: [n=%d] %s", length(globals_args), commaq(names(globals_args))) mdebugf(" + additional namespaces needed: [n=%d] %s", length(packages_args), commaq(packages_args)) } if (length(globals_args) > 0L) { reserved <- intersect(c("...future.FUN", "...future.elements_ii", "...future.seeds_ii"), names(globals_args)) if (length(reserved) > 0) { stop("Detected globals in '%s' using reserved variables names: ", args_name, commaq(reserved)) } globals_args <- as.FutureGlobals(globals_args) globals_ii <- unique(c(globals_ii, globals_args)) if (length(packages_args) > 0L) packages_ii <- unique(c(packages_ii, packages_args)) } if (debug) mdebugf(" - Finding globals in '%s' for chunk #%d ... DONE", args_name, ii) } args_ii <- NULL if (!is.null(globals.maxSize)) { globals_ii["...future.globals.maxSize"] <- list(globals.maxSize) } if (length(chunks) > 1L) { options(future.globals.maxSize = length(chunks) * globals.maxSize.default) if (debug) mdebugf(" - Adjusted option 'future.globals.maxSize': %.0f -> %d * %.0f = %.0f (bytes)", globals.maxSize.default, length(chunks), globals.maxSize.default, getOption("future.globals.maxSize")) on.exit(options(future.globals.maxSize = globals.maxSize), add = TRUE) } if (is.null(seeds)) { if (debug) mdebug(" - seeds: <none>") } else { if (debug) mdebugf(" - seeds: [%d] <seeds>", length(chunk)) globals_ii[["...future.seeds_ii"]] <- seeds[chunk] } if (debug) { mdebugf(" - All globals exported: [n=%d] %s", length(globals_ii), commaq(names(globals_ii))) } fs[[ii]] <- future(expr, substitute = FALSE, envir = future.envir, stdout = future.stdout, conditions = future.conditions, globals = globals_ii, packages = packages_ii, seed = future.seed, label = labels[ii]) if (debug) { mdebug("Created future:") mprint(fs[[ii]]) } rm(list = c("chunk", "globals_ii")) if (debug) mdebugf("Chunk #%d of %d ... DONE", ii, nchunks) } if (debug) mdebugf("Launching %d futures (chunks) ... DONE", nchunks) if (debug) mdebugf("Resolving %d futures (chunks) ...", nchunks) if (isFALSE(future.seed)) { withCallingHandlers({ values <- local({ oopts <- options(future.rng.onMisuse.keepFuture = FALSE) on.exit(options(oopts)) value(fs) }) }, RngFutureCondition = function(cond) { idx <- NULL uuid <- attr(cond, "uuid") if (!is.null(uuid)) { for (kk in seq_along(fs)) { if (identical(fs[[kk]]$uuid, uuid)) idx <- kk } } else { f <- attr(cond, "future") if (is.null(f)) return() if (!isFALSE(f$seed)) return() for (kk in seq_along(fs)) { if (identical(fs[[kk]], f)) idx <- kk } } if (is.null(idx)) return() f <- fs[[idx]] label <- sQuoteLabel(f) message <- sprintf("UNRELIABLE VALUE: One of the %s iterations (%s) unexpectedly generated random numbers without declaring so. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'future.seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via a parallel RNG method. To disable this check, use 'future.seed = NULL', or set option 'future.rng.onMisuse' to \"ignore\".", sQuote(.packageName), label) cond$message <- message if (inherits(cond, "warning")) { warning(cond) invokeRestart("muffleWarning") } else if (inherits(cond, "error")) { stop(cond) } }) } else { value(fs) }}, interrupt = function(int) { onInterrupt(int, fcn_name = fcn_name, debug = debug)}, error = function(e) { onError(e, futures = fs, debug = debug)})   > test-esem.R: 82: future_xapply(FUN = FUN, nX = nX, chunk_args = X, args = list(...), get_chunk = `chunkWith[[`, expr = expr, envir = envir, future.envir = future.envir, future.globals = future.globals, future.packages = future.packages, future.scheduling = future.scheduling, future.chunk.size = future.chunk.size, future.stdout = future.stdout, future.conditions = future.conditions, future.seed = future.seed, future.label = future.label, fcn_name = fcn_name, args_name = args_name, debug = debug)   > test-esem.R: 83: future.apply::future_lapply(X, FUN, future.seed = TRUE)   > test-esem.R: 84: .esem_lapply(seq.int(2L, k_max), function(k) { .esem_fit_one(k, data_df, estimator, cor, ordered_cols, lav_missing, ss_in = ss, r_lv_in = r_lv, item_names = item_names, p = p, keep_fit = keep_fits)})   > test-esem.R: 85: esem_levels(data_mat, k_max = k_max, estimator = estimator_eff, cor = cor, R_external = R_ext, keep_fits = keep_fits, missing = missing)   > test-esem.R: 86: ackwards(d, k_max = 3, engine = "esem", cor = "polychoric", seed = 42)   > test-esem.R: 87: withCallingHandlers(expr, warning = function(w) if (inherits(w, classes)) tryInvokeRestart("muffleWarning"))   > test-esem.R: 88: suppressWarnings(ackwards(d, k_max = 3, engine = "esem", cor = "polychoric", seed = 42))   > test-esem.R: 89: withCallingHandlers(expr, message = function(c) if (inherits(c, classes)) tryInvokeRestart("muffleMessage"))   > test-esem.R: 90: suppressMessages(suppressWarnings(ackwards(d, k_max = 3, engine = "esem",   > test-esem.R: cor = "polychoric", seed = 42)))   > test-esem.R: 91: eval(code, test_env)   > test-esem.R: 92: eval(code, test_env)   > test-esem.R: 93: withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt)   > test-esem.R: 94: doTryCatch(return(expr), name, parentenv, handler)   > test-esem.R: 95: tryCatchOne(expr, names, parentenv, handlers[[1L]])   > test-esem.R: 96: tryCatchList(expr, classes, parentenv, handlers)   > test-esem.R: 97: tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal)   > test-esem.R: 98: doWithOneRestart(return(expr), restart)   > test-esem.R: 99: withOneRestart(expr, restarts[[1L]])   > test-esem.R: 100: withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { })   > test-esem.R: 101: test_code(code, parent.frame())   > test-esem.R: 102: test_that("ESEM results are identical across serial and parallel future plans", { skip_if_not_installed("lavaan") skip_if_not_installed("future") skip_if_not_installed("future.apply") skip_on_os("windows") skip_if(!future::supportsMulticore(), "multicore plan unavailable here") d <- .make_ordinal_data() future::plan(future::sequential) serial <- suppressMessages(suppressWarnings(ackwards(d, k_max = 3, engine = "esem", cor = "polychoric", seed = 42))) future::plan(future::multicore, workers = 2) on.exit(future::plan(future::sequential), add = TRUE) par <- suppressMessages(suppressWarnings(ackwards(d, k_max = 3, engine = "esem", cor = "polychoric", seed = 42))) for (ki in seq_along(serial$levels)) { expect_equal(serial$levels[[ki]]$loadings, par$levels[[ki]]$loadings, info = paste("loadings level", ki)) } expect_equal(tidy(serial)$r, tidy(par)$r) })   > test-esem.R: 103: eval(code, test_env)   > test-esem.R: 104: eval(code, test_env)   > test-esem.R: 105: withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt)   > test-esem.R: 106: doTryCatch(return(expr), name, parentenv, handler)   > test-esem.R: 107: tryCatchOne(expr, names, parentenv, handlers[[1L]])   > test-esem.R: 108: tryCatchList(expr, classes, parentenv, handlers)   > test-esem.R: 109: tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal)   > test-esem.R: 110: doWithOneRestart(return(expr), restart)   > test-esem.R: 111: withOneRestart(expr, restarts[[1L]])   > test-esem.R: 112: withRestarts(tryCatch(withCallingHandlers({ eval(code, test_env) new_expectations <- the$test_expectations > starting_expectations if (snapshot_skipped) { skip("On CRAN") } else if (!new_expectations && skip_on_empty) { skip_empty() }}, expectation = handle_expectation, packageNotFoundError = function(e) { if (on_cran()) { skip(paste0("{", e$package, "} is not installed.")) }}, snapshot_on_cran = function(cnd) { snapshot_skipped <<- TRUE invokeRestart("muffle_cran_snapshot")}, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error, interrupt = handle_interrupt), error = handle_fatal), end_test = function() { })   > test-esem.R: 113: test_code(code = exprs, env = env, reporter = get_reporter() %||% StopReporter$new())   > test-esem.R: 114: source_file(path, env = env(env), desc = desc, shuffle = shuffle, error_call = error_call)   > test-esem.R: 115: test_one_file(path, env = the$testing_env, shuffle = shuffle)   > test-esem.R: 116: doTryCatch(return(expr), name, parentenv, handler)   > test-esem.R: 117: tryCatchOne(expr, names, parentenv, handlers[[1L]])   > test-esem.R: 118: tryCatchList(expr, classes, parentenv, handlers)   > test-esem.R: 119: tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") NULL})   > test-esem.R: 120: with_reporter(multi, test_one_file(path, env = the$testing_env, shuffle = shuffle))   > test-esem.R: 121: asNamespace("testthat")$queue_task(path, shuffle)   > test-esem.R: 122: (function (path, shuffle) { asNamespace("testthat")$queue_task(path, shuffle)})(base::quote("test-esem.R"), base::quote(FALSE))   > test-esem.R: 123: (function (what, args, quote = FALSE, envir = parent.frame()) { if (!is.list(args)) stop("second argument must be a list") if (quote) args <- lapply(args, enquote) .Internal(do.call(what, args, envir))})(base::quote(function (path, shuffle) { asNamespace("testthat")$queue_task(path, shuffle)}), base::quote(list("test-esem.R", FALSE)), envir = base::quote(<environment>), quote = base::quote(TRUE))   > test-esem.R: 124: base::do.call(base::do.call, base::c(base::readRDS("/Volumes/Temp/tmp/RtmpBTjBjq/callr-fun-73ad59b302c9"), base::list(envir = .GlobalEnv, quote = TRUE)), envir = .GlobalEnv, quote = TRUE)   > test-esem.R: 125: base::saveRDS(base::do.call(base::do.call, base::c(base::readRDS("/Volumes/Temp/tmp/RtmpBTjBjq/callr-fun-73ad59b302c9"), base::list(envir = .GlobalEnv, quote = TRUE)), envir = .GlobalEnv, quote = TRUE), file = "/Volumes/Temp/tmp/RtmpBTjBjq/callr-rs-result-73ad777c0820", compress = FALSE)   > test-esem.R: 126: base::withCallingHandlers({ { NULL NULL } base::saveRDS(base::do.call(base::do.call, base::c(base::readRDS("/Volumes/Temp/tmp/RtmpBTjBjq/callr-fun-73ad59b302c9"), base::list(envir = .GlobalEnv, quote = TRUE)), envir = .GlobalEnv, quote = TRUE), file = "/Volumes/Temp/tmp/RtmpBTjBjq/callr-rs-result-73ad777c0820", compress = FALSE) base::flush(base::stdout()) base::flush(base::stderr()) { NULL NULL } base::invisible()}, error = function(e) { { callr_data <- base::as.environment("tools:callr")$`__callr_data__` err <- callr_data$err   > test-esem.R: if (FALSE) { base::assign(".Traceback", base::.traceback(4), envir = callr_data) utils::dump.frames("__callr_dump__") base::assign(".Last.dump", .GlobalEnv$`__callr_dump__`, envir = callr_data) base::rm("__callr_dump__", envir = .GlobalEnv) } e <- err$process_call(e) e2 <- err$new_error("error in callr subprocess") class <- base::class class(e2) <- base::c("callr_remote_error", class(e2)) e2 <- err$add_trace_back(e2) cut <- base::which(e2$trace$scope == "global")[1] if (!base::is.na(cut)) { e2$trace <- e2$trace[-(1:cut), ] } if (callr_data$has_otel) { callr_data$otel_span$record_exception(e2) } base::saveRDS(base::list("error", e2, e), file = base::paste0("/Volumes/Temp/tmp/RtmpBTjBjq/callr-rs-result-73ad777c0820", ".error")) }}, interrupt = function(e) { { callr_data <- base::as.environment("tools:callr")$`__callr_data__` err <- callr_data$err if (FALSE) { base::assign(".Traceback", base::.traceback(4), envir = callr_data) utils::dump.frames("__callr_dump__") base::assign(".Last.dump", .GlobalEnv$`__callr_dump__`, envir = callr_data) base::rm("__callr_dump__", envir = .GlobalEnv) } e <- err$process_call(e) e2 <- err$new_error("error in callr subprocess") class <- base::class class(e2) <- base::c("callr_remote_error", class(e2)) e2 <- err$add_trace_back(e2) cut <- base::which(e2$trace$scope == "global")[1] if (!base::is.na(cut)) { e2$trace <- e2$trace[-(1:cut), ] } if (callr_data$has_otel) { callr_data$otel_span$record_exception(e2) } base::saveRDS(base::list("error", e2, e), file = base::paste0("/Volumes/Temp/tmp/RtmpBTjBjq/callr-rs-result-73ad777c0820", ".error")) }}, callr_message = function(e) { base::try({ callr_data <- base::as.environment("tools:callr")$`__callr_data__` pxlib <- callr_data$pxlib if (base::is.null(e$code)) { e$code <- "301" } msg <- base::paste0("base64::", pxlib$base64_encode(base::serialize(e, NULL))) data <- base::paste0(e$code, " ", base::nchar(msg), "\n", msg) if (callr_data$has_otel) { callr_data$otel_span$add_event("callr message", attributes = list(status_code = e$code)) } pxlib$write_fd(3L, data) if (base::inherits(e, "cli_message") && !base::is.null(base::findRestart("cli_message_handled"))) { base::invokeRestart("cli_message_handled") } else if (base::inherits(e, "message") && !base::is.null(base::findRestart("muffleMessage"))) { base::invokeRestart("muffleMessage") } })})   > test-esem.R: 127: doTryCatch(return(expr), name, parentenv, handler)   > test-esem.R: 128: tryCatchOne(expr, names, parentenv, handlers[[1L]])   > test-esem.R: 129: tryCatchList(expr, names[-nh], parentenv, handlers[-nh])   > test-esem.R: 130: doTryCatch(return(expr), name, parentenv, handler)   > test-esem.R: 131: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), names[nh], parentenv, handlers[[nh]])   > test-esem.R: 132: tryCatchList(expr, classes, parentenv, handlers)   > test-esem.R: 133: base::tryCatch(base::withCallingHandlers({ { NULL NULL } base::saveRDS(base::do.call(base::do.call, base::c(base::readRDS("/Volumes/Temp/tmp/RtmpBTjBjq/callr-fun-73ad59b302c9"), base::list(envir = .GlobalEnv, quote = TRUE)), envir = .GlobalEnv, quote = TRUE), file = "/Volumes/Temp/tmp/RtmpBTjBjq/callr-rs-result-73ad777c0820", compress = FALSE) base::flush(base::stdout()) base::flush(base::stderr()) { NULL NULL } base::invisible()}, error = function(e) { { callr_data <- base::as.environment("tools:callr")$`__callr_data__` err <- callr_data$err if (FALSE) { base::assign(".Traceback", base::.traceback(4), envir = callr_data) utils::dump.frames("__callr_dump__") base::assign(".Last.dump", .GlobalEnv$`__callr_dump__`, envir = callr_data) base::rm("__callr_dump__", envir = .GlobalEnv) } e <- err$process_call(e) e2 <- err$new_error("error in callr subprocess") class <- base::class class(e2) <- base::c("callr_remote_error", class(e2)) e2 <- err$add_trace_back(e2) cut <- base::which(e2$trace$scope == "global")[1] if (!base::is.na(cut)) { e2$trace <- e2$trace[-(1:cut), ] } if (callr_data$has_otel) { callr_data$otel_span$record_exception(e2) } base::saveRDS(base::list("error", e2, e), file = base::paste0("/Volumes/Temp/tmp/RtmpBTjBjq/callr-rs-result-73ad777c0820", ".error")) }}, interrupt = function(e) { { callr_data <- base::as.environment("tools:callr")$`__callr_data__` err <- callr_data$err if (FALSE) { base::assign(".Traceback", base::.traceback(4), envir = callr_data) utils::dump.frames("__callr_dump__") base::assign(".Last.dump", .GlobalEnv$`__callr_dump__`, envir = callr_data) base::rm("__callr_dump__", envir = .GlobalEnv) } e <- err$process_call(e) e2 <- err$new_error("error in callr subprocess") class <- base::class class(e2) <- base::c("callr_remote_error", class(e2)) e2 <- err$add_trace_back(e2) cut <- base::which(e2$trace$scope == "global")[1] if (!base::is.na(cut)) { e2$trace <- e2$trace[-(1:cut), ] } if (callr_data$has_otel) { callr_data$otel_span$record_exception(e2) } base::saveRDS(base::list("error", e2, e), file = base::paste0("/Volumes/Temp/tmp/RtmpBTjBjq/callr-rs-result-73ad777c0820", ".error")) }}, callr_message = function(e) { base::try({ callr_data <- base::as.environment("tools:callr")$`__callr_data__` pxlib <- callr_data$pxlib if (base::is.null(e$code)) { e$code <- "301" } msg <- base::paste0("base64::", pxlib$base64_encode(base::serialize(e, NULL))) data <- base::paste0(e$code, " ", base::nchar(msg), "\n", msg) if (callr_data$has_otel) { callr_data$otel_span$add_event("callr message", attributes = list(status_code = e$code)) } pxlib$write_fd(3L, data) if (base::inherits(e, "cli_message") && !base::is.null(base::findRestart("cli_message_handled"))) { base::invokeRestart("cli_message_handled") } else if (base::inherits(e, "message") && !base::is.null(base::findRestart("muffleMessage"))) { base::invokeRestart("muffleMessage") } })}), error = function(e) { { NULL NULL } if (FALSE) { base::try(base::stop(e)) } if (FALSE) { base::q(save = "no", status = 1) } base::invisible()}, interrupt = function(e) { { NULL NULL } if (FALSE) { base::q(save = "no", status = 1) } base::invisible()})   > test-esem.R: An irrecoverable exception occurred. R is aborting now ...   Saving _problems/test-esem-630.R   > test-check-items.R:   > test-check-items.R: -- Item quality check (ackwards) -----------------------------------------------   > test-check-items.R: Basis: pearson   > test-check-items.R: Items: 5   > test-check-items.R: Flagged: 0   > test-check-items.R: v No item problems detected.   > test-check-items.R: --------------------------------------------------------------------------------   > test-check-items.R: Constant items must be dropped (no variance). A near-constant item (one   > test-check-items.R: response dominates) can yield a meaningless factor; a sparse category can make   > test-check-items.R: `cor = "polychoric"` fail -- collapse rare categories, try `correct = 0`, or   > test-check-items.R: drop the item. Full per-item table: treat this object as a data frame.   > test-check-items.R:   > test-check-items.R: -- Item quality check (ackwards) -----------------------------------------------   > test-check-items.R: Basis: polychoric   > test-check-items.R: Items: 7   > test-check-items.R: Flagged: 2   > test-check-items.R:   > test-check-items.R: -- Flagged items --   > test-check-items.R:   > test-check-items.R: x constant: "const"   > test-check-items.R: ! near-constant: "nc"   > test-check-items.R: --------------------------------------------------------------------------------   > test-check-items.R: Constant items must be dropped (no variance). A near-constant item (one   > test-check-items.R: response dominates) can yield a meaningless factor; a sparse category can make   > test-check-items.R: `cor = "polychoric"` fail -- collapse rare categories, try `correct = 0`, or   > test-check-items.R: drop the item. Full per-item table: treat this object as a data frame.   > test-comparability.R: i Fitting 2 split-half replicates (pca, k = 1-3)...   > test-comparability.R: v Fitting 2 split-half replicates (pca, k = 1-3)... [34ms]   > test-comparability.R:   > test-comparability.R:   > test-comparability.R: -- Split-Half Factor Comparability (ackwards) ----------------------------------   > test-comparability.R: Engine: pca   > test-comparability.R: Basis: pearson   > test-comparability.R: n: 1,000 (500 per half)   > test-comparability.R: Splits: 2   > test-comparability.R: Levels: 1-3   > test-comparability.R:   > test-comparability.R: -- Comparability by level (median across splits) --   > test-comparability.R:   > test-comparability.R: k = 1: median r 1.00, min r 1.00 (m1f1)   > test-comparability.R: k = 2: median r 1.00, min r 1.00 (m2f2)   > test-comparability.R: k = 3: median r .73, min r .28 (m3f2) [1/2 splits usable]   > test-comparability.R: --------------------------------------------------------------------------------   > test-comparability.R: Per-factor detail (incl. Tucker's φ) in `$summary`; per-split values in   > test-comparability.R: `$coefficients`.   > test-comparability.R: Conventional benchmarks: >= .90 replication floor (Everett, 1983; Saucier et   > test-comparability.R: al., 2005), >= .95 factors interchangeable (Lorenzo-Seva & ten Berge, 2006) --   > test-comparability.R: conventions, not tests. Interpret levels whose factors all replicate.   > test-comparability.R: i Fitting 2 split-half replicates (pca, k = 1-3)...   > test-comparability.R: v Fitting 2 split-half replicates (pca, k = 1-3)... [37ms]   > test-comparability.R:   > test-comparability.R:   > test-comparability.R: -- Split-Half Factor Comparability (ackwards) ----------------------------------   > test-comparability.R: Engine: pca   > test-comparability.R: Basis: pearson   > test-comparability.R: n: 1,000 (500 per half)   > test-comparability.R: Splits: 2   > test-comparability.R: Levels: 1-3   > test-comparability.R:   > test-comparability.R: -- Comparability by level (median across splits) --   > test-comparability.R:   > test-comparability.R: k = 1: median r 1.00, min r 1.00 (m1f1)   > test-comparability.R: k = 2: median r 1.00, min r 1.00 (m2f2)   > test-comparability.R: k = 3: no usable splits (half-solutions did not converge)   > test-comparability.R: --------------------------------------------------------------------------------   > test-comparability.R: Per-factor detail (incl. Tucker's φ) in `$summary`; per-split values in   > test-comparability.R: `$coefficients`.   > test-comparability.R: Conventional benchmarks: >= .90 replication floor (Everett, 1983; Saucier et   > test-comparability.R: al., 2005), >= .95 factors interchangeable (Lorenzo-Seva & ten Berge, 2006) --   > test-comparability.R: conventions, not tests. Interpret levels whose factors all replicate.   > test-comparability.R:   > test-comparability.R: -- Split-Half Factor Comparability (ackwards) ----------------------------------   > test-comparability.R: Engine: pca   > test-comparability.R: Basis: pearson   > test-comparability.R: n: 1,000 (500 per half)   > test-comparability.R: Splits: 4   > test-comparability.R: Levels: 1-5   > test-comparability.R:   > test-comparability.R: -- Comparability by level (median across splits) --   > test-comparability.R:   > test-comparability.R: k = 1: median r 1.00, min r 1.00 (m1f1)   > test-comparability.R: k = 2: median r 1.00, min r 1.00 (m2f1)   > test-comparability.R: k = 3: median r .80, min r .58 (m3f2)   > test-comparability.R: k = 4: median r 1.00, min r 1.00 (m4f2)   > test-comparability.R: k = 5: median r .99, min r .14 (m5f5)   > test-comparability.R: --------------------------------------------------------------------------------   > test-comparability.R: Per-factor detail (incl. Tucker's φ) in `$summary`; per-split values in   > test-comparability.R: `$coefficients`.   > test-comparability.R: Conventional benchmarks: >= .90 replication floor (Everett, 1983; Saucier et   > test-comparability.R: al., 2005), >= .95 factors interchangeable (Lorenzo-Seva & ten Berge, 2006) --   > test-comparability.R: conventions, not tests. Interpret levels whose factors all replicate.   > test-comparability.R:   > test-comparability.R: -- Split-Half Factor Comparability (ackwards) ----------------------------------   > test-comparability.R: Engine: pca   > test-comparability.R: Basis: pearson   > test-comparability.R: n: 1,000 (500 per half)   > test-comparability.R: Splits: 4   > test-comparability.R: Levels: 1-5   > test-comparability.R:   > test-comparability.R: -- Comparability by level (median across splits) --   > test-comparability.R:   > test-comparability.R: k = 1: median r 1.00, min r 1.00 (m1f1)   > test-comparability.R: k = 2: median r 1.00, min r 1.00 (m2f1)   > test-comparability.R: k = 3: median r .80, min r .58 (m3f2)   > test-comparability.R: k = 4: median r 1.00, min r 1.00 (m4f2)   > test-comparability.R: k = 5: median r .99, min r .14 (m5f5)   > test-comparability.R: --------------------------------------------------------------------------------   > test-comparability.R: Per-factor detail (incl. Tucker's φ) in `$summary`; per-split values in   > test-comparability.R: `$coefficients`.   > test-comparability.R: Conventional benchmarks: >= .90 replication floor (Everett, 1983; Saucier et   > test-comparability.R: al., 2005), >= .95 factors interchangeable (Lorenzo-Seva & ten Berge, 2006) --   > test-comparability.R: conventions, not tests. Interpret levels whose factors all replicate.   > test-cor-input.R:   > test-cor-input.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------   > test-cor-input.R: Engine: pca   > test-cor-input.R: Rotation: varimax   > test-cor-input.R: Basis: (user-supplied matrix)   > test-cor-input.R: n: NA   > test-cor-input.R: k (max): 3   > test-cor-input.R:   > test-cor-input.R: -- Levels --   > test-cor-input.R:   > test-cor-input.R: v k = 1: 1 factor, 41.8% variance   > test-comparability.R:   > test-comparability.R: Likely variables with missing values are i6   > test-cor-input.R: v k = 2: 2 factors, 58.5% variance   > test-comparability.R:   > test-comparability.R: Likely variables with missing values are i6   > test-cor-input.R: v k = 3: 3 factors, 72.2% variance   > test-cor-input.R:   > test-cor-input.R: -- Edges --   > test-cor-input.R:   > test-cor-input.R: 5 of 8 edges have |r| >= 0.3   > test-cor-input.R: --------------------------------------------------------------------------------   > test-cor-input.R: Note: This is a series of linked solutions, not a fitted hierarchical model.   > test-cor-input.R: Cross-level edges are descriptive score correlations. Per-level fit indices   > test-cor-input.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --   > test-cor-input.R: they do not validate the edges or the hierarchy itself.   > test-cor-input.R:   > test-cor-input.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------   > test-cor-input.R: Engine: pca   > test-cor-input.R: Rotation: varimax   > test-cor-input.R: Basis: (user-supplied matrix)   > test-cor-input.R: n: NA   > test-cor-input.R: k (max): 3   > test-cor-input.R:   > test-cor-input.R: -- Levels --   > test-cor-input.R:   > test-cor-input.R: v k = 1: 1 factor, 41.8% variance   > test-cor-input.R: v k = 2: 2 factors, 58.5% variance   > test-cor-input.R: v k = 3: 3 factors, 72.2% variance   > test-cor-input.R:   > test-cor-input.R: -- Edges --   > test-cor-input.R:   > test-cor-input.R: 5 of 8 edges have |r| >= 0.3   > test-cor-input.R: --------------------------------------------------------------------------------   > test-cor-input.R: Note: This is a series of linked solutions, not a fitted hierarchical model.   > test-cor-input.R: Cross-level edges are descriptive score correlations. Per-level fit indices   > test-cor-input.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --   > test-cor-input.R: they do not validate the edges or the hierarchy itself.   > test-comparability.R:   > test-comparability.R: -- Split-Half Factor Comparability (ackwards) ----------------------------------   > test-comparability.R: Engine: pca   > test-comparability.R: Basis: pearson   > test-comparability.R: n: 1,000 (500 per half)   > test-comparability.R: Splits: 2   > test-comparability.R: Levels: 1-2 (requested 1-3; full-sample fit truncated)   > test-comparability.R:   > test-comparability.R: -- Comparability by level (median across splits) --   > test-comparability.R:   > test-comparability.R: k = 1: median r 1.00, min r 1.00 (m1f1)   > test-comparability.R: k = 2: median r 1.00, min r 1.00 (m2f2)   > test-comparability.R: --------------------------------------------------------------------------------   > test-comparability.R: Per-factor detail (incl. Tucker's φ) in `$summary`; per-split values in   > test-comparability.R: `$coefficients`.   > test-comparability.R: Conventional benchmarks: >= .90 replication floor (Everett, 1983; Saucier et   > test-comparability.R: al., 2005), >= .95 factors interchangeable (Lorenzo-Seva & ten Berge, 2006) --   > test-comparability.R: conventions, not tests. Interpret levels whose factors all replicate.   > test-cor-input.R:   > test-cor-input.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------   > test-cor-input.R: Variables: 6   > test-cor-input.R: n: 875   > test-cor-input.R: Basis: (user-supplied matrix)   > test-cor-input.R: Tested k: 1-5   > test-cor-input.R:   > test-cor-input.R: -- Criteria (k = 1-5) --   > test-cor-input.R:   > test-cor-input.R: k = 1: PA-PC v PA-FA v MAP 0.0461* VSS-1 0.7025 VSS-2 0.0000   > test-cor-input.R: k = 2: PA-PC - PA-FA v MAP 0.1279 VSS-1 0.7919* VSS-2 0.8159   > test-cor-input.R: k = 3: PA-PC - PA-FA v MAP 0.2614 VSS-1 0.7666 VSS-2 0.8879   > test-cor-input.R: k = 4: PA-PC - PA-FA - MAP 0.4712 VSS-1 0.6796 VSS-2 0.9054*   > test-cor-input.R: k = 5: PA-PC - PA-FA - MAP 1.0000 VSS-1 0.7009 VSS-2 0.8935   > test-cor-input.R: + CD skipped (requires raw data; not available for matrix input).   > test-cor-input.R:   > test-cor-input.R: -- Recommendations --   > test-cor-input.R:   > test-cor-input.R: * PA-PC: k <= 1   > test-cor-input.R: * PA-FA: k <= 3   > test-cor-input.R: * MAP: k = 1   > test-cor-input.R: * VSS-1: k = 2   > test-cor-input.R: * VSS-2: k = 4   > test-cor-input.R: Consensus range: k = 1-4   > test-cor-input.R: --------------------------------------------------------------------------------   > test-cor-input.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels   > test-cor-input.R: above the consensus to observe factor fragmentation is intentional.   > test-cor-input.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,   > test-cor-input.R: 2023). PA-FA and CD are more conservative. Use the range.   > test-cor-input.R:   > test-cor-input.R: -- Factor / Component Count Suggestion (ackwards) ------------------------------   > test-cor-input.R: Variables: 6   > test-cor-input.R: n: 875   > test-cor-input.R: Basis: (user-supplied matrix)   > test-cor-input.R: Tested k: 1-5   > test-cor-input.R:   > test-cor-input.R: -- Criteria (k = 1-5) --   > test-cor-input.R:   > test-cor-input.R: k = 1: PA-PC v PA-FA v MAP 0.0461* VSS-1 0.7025 VSS-2 0.0000   > test-cor-input.R: k = 2: PA-PC - PA-FA v MAP 0.1279 VSS-1 0.7919* VSS-2 0.8159   > test-cor-input.R: k = 3: PA-PC - PA-FA v MAP 0.2614 VSS-1 0.7666 VSS-2 0.8879   > test-cor-input.R: k = 4: PA-PC - PA-FA - MAP 0.4712 VSS-1 0.6796 VSS-2 0.9054*   > test-cor-input.R: k = 5: PA-PC - PA-FA - MAP 1.0000 VSS-1 0.7009 VSS-2 0.8935   > test-cor-input.R: + CD skipped (requires raw data; not available for matrix input).   > test-cor-input.R:   > test-cor-input.R: -- Recommendations --   > test-cor-input.R:   > test-cor-input.R: * PA-PC: k <= 1   > test-cor-input.R: * PA-FA: k <= 3   > test-cor-input.R: * MAP: k = 1   > test-cor-input.R: * VSS-1: k = 2   > test-cor-input.R: * VSS-2: k = 4   > test-cor-input.R: Consensus range: k = 1-4   > test-cor-input.R: --------------------------------------------------------------------------------   > test-cor-input.R: Note: k_max in ackwards() is a maximum depth. Setting k_max one or two levels   > test-cor-input.R: above the consensus to observe factor fragmentation is intentional.   > test-cor-input.R: Caution: PA-PC tends to overextract; structures may not replicate (Forbes,   > test-cor-input.R: 2023). PA-FA and CD are more conservative. Use the range.   > test-cor-input.R: i Running parallel analysis (5 iterations, PC + FA)...   > test-cor-input.R: v Running parallel analysis (5 iterations, PC + FA)... [129ms]   > test-cor-input.R:   > test-cor-input.R: i Running MAP and VSS...   > test-cor-input.R: v Running MAP and VSS... [61ms]   > test-cor-input.R:   > test-cor-input.R: i Running Comparison Data (CD)...   > test-data.R: i Running parallel analysis (5 iterations, PC + FA)...   > test-data.R: v Running parallel analysis (5 iterations, PC + FA)... [68ms]   > test-data.R:   > test-data.R: i Running MAP and VSS...   > test-data.R: v Running MAP and VSS... [42ms]   > test-data.R:   > test-data.R: i Running Comparison Data (CD)...   > test-cor-input.R: v Running Comparison Data (CD)... [238ms]   > test-cor-input.R:   > test-efa.R:   > test-efa.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------   > test-efa.R: Engine: efa   > test-efa.R: Rotation: varimax   > test-efa.R: Basis: pearson   > test-efa.R: n: 2,800   > test-efa.R: k (max): 3   > test-efa.R:   > test-efa.R: -- Levels --   > test-efa.R:   > test-efa.R: v k = 1: 1 factor, 17.0% variance   > test-efa.R: v k = 2: 2 factors, 26.1% variance   > test-efa.R: v k = 3: 3 factors, 32.1% variance   > test-efa.R:   > test-efa.R: -- Edges --   > test-efa.R:   > test-efa.R: 5 of 8 edges have |r| >= 0.3   > test-efa.R: --------------------------------------------------------------------------------   > test-efa.R: Note: This is a series of linked solutions, not a fitted hierarchical model.   > test-efa.R: Cross-level edges are descriptive score correlations. Per-level fit indices   > test-efa.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --   > test-efa.R: they do not validate the edges or the hierarchy itself.   > test-efa.R: Error in solve.default(r) :   > test-efa.R: system is computationally singular: reciprocal condition number = 5.19743e-17   > test-data.R: v Running Comparison Data (CD)... [2.5s]   > test-data.R:   > test-factor-labels.R: i Redundancy pruning (direct criterion, |r| >= 0.9) flagged 3 nodes.   > test-factor-labels.R: i Nodes are retained in the object; inspect with `x$prune$nodes` and `x$prune$chains`.   > test-factor-labels.R: i Redundancy pruning (direct criterion, |r| >= 0.9) flagged 3 nodes.   > test-factor-labels.R: i Nodes are retained in the object; inspect with `x$prune$nodes` and `x$prune$chains`.   > test-factorability.R:   > test-factorability.R: -- Factorability screen (ackwards) ---------------------------------------------   > test-factorability.R: Basis: pearson   > test-factorability.R: Observations: 1,000   > test-factorability.R: Variables: 16   > test-factorability.R: N:p ratio: 62.5:1   > test-factorability.R:   > test-factorability.R: -- Sampling adequacy --   > test-factorability.R:   > test-factorability.R: Overall KMO: 0.86 (meritorious)   > test-factorability.R:   > test-factorability.R: Bartlett's test of sphericity: chi-square(120) = 6476.3, p < .001   > test-factorability.R:   > test-factorability.R: -- Identifiability --   > test-factorability.R:   > test-factorability.R: Ledermann bound: at most 10 common factors are identifiable from 16 variables   > test-factorability.R: (EFA/ESEM; PCA is unbounded).   > test-factorability.R: --------------------------------------------------------------------------------   > test-factorability.R: KMO bands (Kaiser 1974), the N:p >= 5/10 rules, and Bartlett at .05 are widely   > test-factorability.R: used *rules of thumb*, not settled thresholds -- required N depends on   > test-factorability.R: communalities and factor overdetermination (MacCallum et al. 1999). Read the   > test-factorability.R: numbers, not a pass/fail.   > test-factorability.R:   > test-factorability.R: -- Factorability screen (ackwards) ---------------------------------------------   > test-factorability.R: Basis: pearson   > test-factorability.R: Observations: 1,000   > test-factorability.R: Variables: 16   > test-factorability.R: N:p ratio: 62.5:1   > test-factorability.R:   > test-factorability.R: -- Sampling adequacy --   > test-factorability.R:   > test-factorability.R: Overall KMO: 0.86 (meritorious)   > test-factorability.R:   > test-factorability.R: Bartlett's test of sphericity: chi-square(120) = 6476.3, p < .001   > test-factorability.R:   > test-factorability.R: -- Identifiability --   > test-factorability.R:   > test-factorability.R: Ledermann bound: at most 10 common factors are identifiable from 16 variables   > test-factorability.R: (EFA/ESEM; PCA is unbounded).   > test-factorability.R: --------------------------------------------------------------------------------   > test-factorability.R: KMO bands (Kaiser 1974), the N:p >= 5/10 rules, and Bartlett at .05 are widely   > test-factorability.R: used *rules of thumb*, not settled thresholds -- required N depends on   > test-factorability.R: communalities and factor overdetermination (MacCallum et al. 1999). Read the   > test-factorability.R: numbers, not a pass/fail.   > test-interpret.R:   > test-interpret.R: -- Salient factors by item (ackwards) ------------------------------------------   > test-interpret.R: Engine: pca   > test-interpret.R: Cut: |loading| >= 0.25   > test-interpret.R: Top-n: all   > test-interpret.R:   > test-interpret.R: -- Level 3 (3 factors) --   > test-interpret.R:   > test-interpret.R: x1: First indicator   > test-interpret.R: m3f1 [-0.939]   > test-interpret.R: m3f3 [0.279]   > test-interpret.R:   > test-interpret.R: x2   > test-interpret.R: m3f1 [-0.955]   > test-interpret.R:   > test-interpret.R: x3   > test-interpret.R: m3f1 [-0.950]   > test-interpret.R:   > test-interpret.R: x4   > test-interpret.R: m3f2 [0.937]   > test-interpret.R:   > test-interpret.R: x5   > test-interpret.R: m3f2 [0.952]   > test-interpret.R:   > test-interpret.R: x6   > test-interpret.R: m3f2 [0.956]   > test-interpret.R: --------------------------------------------------------------------------------   > test-interpret.R: Loadings reflect primary-parent sign alignment. Use tidy(x, what = "loadings")   > test-interpret.R: for the full matrix.   > test-interpret.R:   > test-interpret.R: -- Salient items by factor (ackwards) ------------------------------------------   > test-interpret.R: Engine: pca   > test-interpret.R: Cut: |loading| >= 0.25   > test-interpret.R: Top-n: all   > test-interpret.R:   > test-interpret.R: -- Level 3 (3 factors) --   > test-interpret.R:   > test-interpret.R: m3f1   > test-interpret.R: x2 [-0.955]   > test-interpret.R: x3 [-0.950]   > test-interpret.R: x1: First indicator [-0.939]   > test-interpret.R:   > test-interpret.R: m3f2   > test-interpret.R: x6 [0.956]   > test-interpret.R: x5 [0.952]   > test-interpret.R: x4 [0.937]   > test-interpret.R:   > test-interpret.R: m3f3   > test-interpret.R: x1: First indicator [0.279]   > test-interpret.R: --------------------------------------------------------------------------------   > test-interpret.R: Loadings reflect primary-parent sign alignment. Use tidy(x, what = "loadings")   > test-interpret.R: for the full matrix.   > test-interpret.R:   > test-interpret.R: -- Salient items by factor (ackwards) ------------------------------------------   > test-interpret.R: Engine: pca   > test-interpret.R: Cut: |loading| >= 0.3   > test-interpret.R: Top-n: all   > test-interpret.R:   > test-interpret.R: -- Level 1 (1 factor) --   > test-interpret.R:   > test-interpret.R: m1f1   > test-interpret.R: x4 [0.709]   > test-interpret.R: x5 [0.706]   > test-interpret.R: x6 [0.706]   > test-interpret.R: x3 [-0.701]   > test-interpret.R: x2 [-0.676]   > test-interpret.R: x1 [-0.662]   > test-interpret.R:   > test-interpret.R: -- Level 2 (2 factors) --   > test-interpret.R:   > test-interpret.R: m2f1   > test-interpret.R: x6 [0.954]   > test-interpret.R: x5 [0.951]   > test-interpret.R: x4 [0.946]   > test-interpret.R:   > test-interpret.R: m2f2   > test-interpret.R: x2 [-0.952]   > test-interpret.R: x1 [-0.944]   > test-interpret.R: x3 [-0.941]   > test-interpret.R:   > test-interpret.R: -- Level 3 (3 factors) --   > test-interpret.R:   > test-interpret.R: m3f1   > test-interpret.R: x2 [-0.955]   > test-interpret.R: x3 [-0.950]   > test-interpret.R: x1 [-0.939]   > test-interpret.R:   > test-interpret.R: m3f2   > test-interpret.R: x6 [0.956]   > test-interpret.R: x5 [0.952]   > test-interpret.R: x4 [0.937]   > test-interpret.R: --------------------------------------------------------------------------------   > test-interpret.R: Loadings reflect primary-parent sign alignment. Use tidy(x, what = "loadings")   > test-interpret.R: for the full matrix.   > test-interpret.R:   > test-interpret.R: -- Salient items by factor (ackwards) ------------------------------------------   > test-interpret.R: Engine: pca   > test-interpret.R: Cut: |loading| >= 0.3   > test-interpret.R: Top-n: all   > test-interpret.R:   > test-interpret.R: -- Level 1 (1 factor) --   > test-interpret.R:   > test-interpret.R: m1f1   > test-interpret.R: x4 [0.709]   > test-interpret.R: x5 [0.706]   > test-interpret.R: x6 [0.706]   > test-interpret.R: x3 [-0.701]   > test-interpret.R: x2 [-0.676]   > test-interpret.R: x1 [-0.662]   > test-interpret.R:   > test-interpret.R: -- Level 2 (2 factors) --   > test-interpret.R:   > test-interpret.R: m2f1   > test-interpret.R: x6 [0.954]   > test-interpret.R: x5 [0.951]   > test-interpret.R: x4 [0.946]   > test-interpret.R:   > test-interpret.R: m2f2   > test-interpret.R: x2 [-0.952]   > test-interpret.R: x1 [-0.944]   > test-interpret.R: x3 [-0.941]   > test-interpret.R:   > test-interpret.R: -- Level 3 (3 factors) --   > test-interpret.R:   > test-interpret.R: m3f1   > test-interpret.R: x2 [-0.955]   > test-interpret.R: x3 [-0.950]   > test-interpret.R: x1 [-0.939]   > test-interpret.R:   > test-interpret.R: m3f2   > test-interpret.R: x6 [0.956]   > test-interpret.R: x5 [0.952]   > test-interpret.R: x4 [0.937]   > test-interpret.R: --------------------------------------------------------------------------------   > test-interpret.R: Loadings reflect primary-parent sign alignment. Use tidy(x, what = "loadings")   > test-interpret.R: for the full matrix.   > test-interpret.R:   > test-interpret.R: -- Salient items by factor (ackwards) ------------------------------------------   > test-interpret.R: Engine: pca   > test-interpret.R: Cut: |loading| >= 0.99   > test-interpret.R: Top-n: all   > test-interpret.R: No items met the |loading| >= 0.99 threshold.   > test-interpret.R:   > test-interpret.R: -- Salient items by factor (ackwards) ------------------------------------------   > test-interpret.R: Engine: pca   > test-interpret.R: Cut: |loading| >= 0.3   > test-interpret.R: Top-n: all   > test-interpret.R:   > test-interpret.R: -- Level 3 (3 factors) --   > test-interpret.R:   > test-interpret.R: m3f1   > test-interpret.R: A4: Love children [0.778]   > test-interpret.R: A5: Make people feel at ease [0.735]   > test-interpret.R: A3: Know how to comfort others [0.728]   > test-interpret.R: A2: Inquire about others' well-being [0.562]   > test-interpret.R:   > test-interpret.R: m3f2   > test-interpret.R: A1: Am indifferent to the feelings of others [-0.919]   > test-interpret.R: A2: Inquire about others' well-being [0.529]   > test-interpret.R: A3: Know how to comfort others [0.387]   > test-interpret.R:   > test-interpret.R: m3f3   > test-interpret.R: C1: Am exacting in my work [0.995]   > test-interpret.R: --------------------------------------------------------------------------------   > test-interpret.R: Loadings reflect primary-parent sign alignment. Use tidy(x, what = "loadings")   > test-interpret.R: for the full matrix.   > test-interpret.R:   > test-interpret.R: -- Salient items by factor (ackwards) ------------------------------------------   > test-interpret.R: Engine: pca   > test-interpret.R: Cut: |loading| >= 0.3   > test-interpret.R: Top-n: all   > test-interpret.R:   > test-interpret.R: -- Level 3 (3 factors) --   > test-interpret.R:   > test-interpret.R: m3f1   > test-interpret.R: A4: Love children [0.778]   > test-interpret.R: A5: Make people feel at ease [0.735]   > test-interpret.R: A3: Know how to comfort others [0.728]   > test-interpret.R: A2: Inquire about others' well-being [0.562]   > test-interpret.R:   > test-interpret.R: m3f2   > test-interpret.R: A1: Am indifferent to the feelings of others [-0.919]   > test-interpret.R: A2: Inquire about others' well-being [0.529]   > test-interpret.R: A3: Know how to comfort others [0.387]   > test-interpret.R:   > test-interpret.R: m3f3   > test-interpret.R: C1: Am exacting in my work [0.995]   > test-interpret.R: --------------------------------------------------------------------------------   > test-interpret.R: Loadings reflect primary-parent sign alignment. Use tidy(x, what = "loadings")   > test-interpret.R: for the full matrix.   > test-label_template.R: c(   > test-label_template.R: "m1f1" = "m1f1",   > test-label_template.R: "m2f1" = "m2f1",   > test-label_template.R: "m2f2" = "m2f2"   > test-label_template.R: )   > test-label_template.R: `label_template()` scaffold (id style):   > test-pca.R: Error in solve.default(r) :   > test-pca.R: Lapack routine dgesv: system is exactly singular: U[6,6] = 0   > test-print.R:   > test-print.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------   > test-print.R: Engine: pca   > test-print.R: Rotation: varimax   > test-print.R: Basis: pearson   > test-print.R: n: 2,800   > test-print.R: k (max): 3   > test-print.R:   > test-print.R: -- Levels --   > test-print.R:   > test-print.R: v k = 1: 1 factor, 20.1% variance   > test-print.R: v k = 2: 2 factors, 31.1% variance   > test-print.R: v k = 3: 3 factors, 39.6% variance   > test-print.R:   > test-print.R: -- Edges --   > test-print.R:   > test-print.R: 5 of 8 edges have |r| >= 0.3   > test-print.R: --------------------------------------------------------------------------------   > test-print.R: Note: This is a series of linked solutions, not a fitted hierarchical model.   > test-print.R: Cross-level edges are descriptive score correlations. Per-level fit indices   > test-print.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --   > test-print.R: they do not validate the edges or the hierarchy itself.   > test-print.R:   > test-print.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------   > test-print.R: Engine: pca   > test-print.R: Rotation: varimax   > test-print.R: Basis: pearson   > test-print.R: n: 2,800   > test-print.R: k (max): 3   > test-print.R:   > test-print.R: -- Levels --   > test-print.R:   > test-print.R: v k = 1: 1 factor, 20.1% variance   > test-print.R: v k = 2: 2 factors, 31.1% variance   > test-print.R: v k = 3: 3 factors, 39.6% variance   > test-print.R:   > test-print.R: -- Edges --   > test-print.R:   > test-print.R: 5 of 8 edges have |r| >= 0.3   > test-print.R: --------------------------------------------------------------------------------   > test-print.R: Note: This is a series of linked solutions, not a fitted hierarchical model.   > test-print.R: Cross-level edges are descriptive score correlations. Per-level fit indices   > test-print.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --   > test-print.R: they do not validate the edges or the hierarchy itself.   > test-print.R:   > test-print.R: -- Summary: Bass-Ackwards Analysis (ackwards) ----------------------------------   > test-print.R: Engine: pca   > test-print.R: Rotation: varimax   > test-print.R: Basis: pearson   > test-print.R: n: 2,800   > test-print.R: k (max): 3   > test-print.R:   > test-print.R: -- Levels --   > test-print.R:   > test-print.R: k = 1: 1 factor (30.2% cumulative variance)   > test-print.R: m1f1 30.2% eigenvalue 3.02   > test-print.R:   > test-print.R: k = 2: 2 factors (48.1% cumulative variance)   > test-print.R: m2f1 24.5% eigenvalue 3.02   > test-print.R: m2f2 23.6% eigenvalue 1.79   > test-print.R:   > test-print.R: k = 3: 3 factors (57.4% cumulative variance)   > test-print.R: m3f1 24.1% eigenvalue 3.02   > test-print.R: m3f2 22.1% eigenvalue 1.79   > test-print.R: m3f3 11.2% eigenvalue 0.93   > test-print.R:   > test-print.R: -- Lineage (primary parents) --   > test-print.R:   > test-print.R: m1f1 > m2f1, m2f2   > test-print.R: m2f1 > m3f1   > test-print.R: m2f2 > m3f2, m3f3   > test-print.R: --------------------------------------------------------------------------------   > test-print.R: Note: This is a series of linked solutions, not a fitted hierarchical model.   > test-print.R: Cross-level edges are descriptive score correlations. Per-level fit indices   > test-print.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --   > test-print.R: they do not validate the edges or the hierarchy itself.   > test-print.R:   > test-print.R: -- Summary: Bass-Ackwards Analysis (ackwards) ----------------------------------   > test-print.R: Engine: pca   > test-print.R: Rotation: varimax   > test-print.R: Basis: pearson   > test-print.R: n: 2,800   > test-print.R: k (max): 3   > test-print.R:   > test-print.R: -- Levels --   > test-print.R:   > test-print.R: k = 1: 1 factor (30.2% cumulative variance)   > test-print.R: m1f1 30.2% eigenvalue 3.02   > test-print.R:   > test-print.R: k = 2: 2 factors (48.1% cumulative variance)   > test-print.R: m2f1 24.5% eigenvalue 3.02   > test-print.R: m2f2 23.6% eigenvalue 1.79   > test-print.R:   > test-print.R: k = 3: 3 factors (57.4% cumulative variance)   > test-print.R: m3f1 24.1% eigenvalue 3.02   > test-print.R: m3f2 22.1% eigenvalue 1.79   > test-print.R: m3f3 11.2% eigenvalue 0.93   > test-print.R:   > test-print.R: -- Lineage (primary parents) --   > test-print.R:   > test-print.R: m1f1 > m2f1, m2f2   > test-print.R: m2f1 > m3f1   > test-print.R: m2f2 > m3f2, m3f3   > test-print.R: --------------------------------------------------------------------------------   > test-print.R: Note: This is a series of linked solutions, not a fitted hierarchical model.   > test-print.R: Cross-level edges are descriptive score correlations. Per-level fit indices   > test-print.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --   > test-print.R: they do not validate the edges or the hierarchy itself.   > test-prune.R: Error in solve.default(r) :   > test-prune.R: Lapack routine dgesv: system is exactly singular: U[4,4] = 0   > test-prune.R: Error in solve.default(r) :   > test-prune.R: Lapack routine dgesv: system is exactly singular: U[4,4] = 0   > test-prune.R: Error in solve.default(r) :   > test-prune.R: Lapack routine dgesv: system is exactly singular: U[4,4] = 0   > test-prune.R:   > test-prune.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------   > test-prune.R: Engine: pca   > test-prune.R: Rotation: varimax   > test-prune.R: Basis: pearson   > test-prune.R: n: 500   > test-prune.R: k (max): 4   > test-prune.R:   > test-prune.R: -- Levels --   > test-prune.R:   > test-prune.R: v k = 1: 1 factor, 97.3% variance   > test-prune.R: v k = 2: 2 factors, 99.8% variance   > test-prune.R: v k = 3: 3 factors, 99.8% variance   > test-prune.R: v k = 4: 4 factors, 99.9% variance   > test-prune.R:   > test-prune.R: -- Edges --   > test-prune.R:   > test-prune.R: 8 of 20 edges have |r| >= 0.3   > test-prune.R:   > test-prune.R: -- Pruning --   > test-prune.R:   > test-prune.R: Redundancy (direct, |r| >= 0.9): 6 nodes flagged   > test-prune.R: --------------------------------------------------------------------------------   > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes   > test-prune.R: remain in the object; all edges are preserved. Inspect with `x$prune$nodes` and   > test-prune.R: `tidy(x, what = "nodes")`.   > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.   > test-prune.R: Cross-level edges are descriptive score correlations. Per-level fit indices   > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --   > test-prune.R: they do not validate the edges or the hierarchy itself.   > test-prune.R:   > test-prune.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------   > test-prune.R: Engine: pca   > test-prune.R: Rotation: varimax   > test-prune.R: Basis: pearson   > test-prune.R: n: 500   > test-prune.R: k (max): 4   > test-prune.R:   > test-prune.R: -- Levels --   > test-prune.R:   > test-prune.R: v k = 1: 1 factor, 97.3% variance   > test-prune.R: v k = 2: 2 factors, 99.8% variance   > test-prune.R: v k = 3: 3 factors, 99.8% variance   > test-prune.R: v k = 4: 4 factors, 99.9% variance   > test-prune.R:   > test-prune.R: -- Edges --   > test-prune.R:   > test-prune.R: 8 of 20 edges have |r| >= 0.3   > test-prune.R:   > test-prune.R: -- Pruning --   > test-prune.R:   > test-prune.R: Redundancy (direct, |r| >= 0.9): 6 nodes flagged   > test-prune.R: --------------------------------------------------------------------------------   > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes   > test-prune.R: remain in the object; all edges are preserved. Inspect with `x$prune$nodes` and   > test-prune.R: `tidy(x, what = "nodes")`.   > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.   > test-prune.R: Cross-level edges are descriptive score correlations. Per-level fit indices   > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --   > test-prune.R: they do not validate the edges or the hierarchy itself.   > test-prune.R:   > test-prune.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------   > test-prune.R: Engine: pca   > test-prune.R: Rotation: varimax   > test-prune.R: Basis: pearson   > test-prune.R: n: 1,000   > test-prune.R: k (max): 3   > test-prune.R:   > test-prune.R: -- Levels --   > test-prune.R:   > test-prune.R: v k = 1: 1 factor, 41.8% variance   > test-prune.R: v k = 2: 2 factors, 58.5% variance   > test-prune.R: v k = 3: 3 factors, 72.2% variance   > test-prune.R:   > test-prune.R: -- Edges --   > test-prune.R:   > test-prune.R: 5 of 8 edges have |r| >= 0.3   > test-prune.R:   > test-prune.R: -- Pruning --   > test-prune.R:   > test-prune.R: Artifact: Tucker's phi computed for 11 cross-level factor pairs   > test-prune.R: Structural signals: 3 factors flagged (inspect `x$prune$structural`)   > test-prune.R: --------------------------------------------------------------------------------   > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes   > test-prune.R: remain in the object; all edges are preserved. Inspect with `x$prune$nodes` and   > test-prune.R: `tidy(x, what = "nodes")`.   > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.   > test-prune.R: Cross-level edges are descriptive score correlations. Per-level fit indices   > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --   > test-prune.R: they do not validate the edges or the hierarchy itself.   > test-prune.R:   > test-prune.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------   > test-prune.R: Engine: pca   > test-prune.R: Rotation: varimax   > test-prune.R: Basis: pearson   > test-prune.R: n: 1,000   > test-prune.R: k (max): 3   > test-prune.R:   > test-prune.R: -- Levels --   > test-prune.R:   > test-prune.R: v k = 1: 1 factor, 41.8% variance   > test-prune.R: v k = 2: 2 factors, 58.5% variance   > test-prune.R: v k = 3: 3 factors, 72.2% variance   > test-prune.R:   > test-prune.R: -- Edges --   > test-prune.R:   > test-prune.R: 5 of 8 edges have |r| >= 0.3   > test-prune.R:   > test-prune.R: -- Pruning --   > test-prune.R:   > test-prune.R: Artifact: Tucker's phi computed for 11 cross-level factor pairs   > test-prune.R: Structural signals: 3 factors flagged (inspect `x$prune$structural`)   > test-prune.R: --------------------------------------------------------------------------------   > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes   > test-prune.R: remain in the object; all edges are preserved. Inspect with `x$prune$nodes` and   > test-prune.R: `tidy(x, what = "nodes")`.   > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.   > test-prune.R: Cross-level edges are descriptive score correlations. Per-level fit indices   > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --   > test-prune.R: they do not validate the edges or the hierarchy itself.   > test-prune.R:   > test-prune.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------   > test-prune.R: Engine: pca   > test-prune.R: Rotation: varimax   > test-prune.R: Basis: pearson   > test-prune.R: n: 500   > test-prune.R: k (max): 4   > test-prune.R:   > test-prune.R: -- Levels --   > test-prune.R:   > test-prune.R: v k = 1: 1 factor, 97.3% variance   > test-prune.R: v k = 2: 2 factors, 99.8% variance   > test-prune.R: v k = 3: 3 factors, 99.8% variance   > test-prune.R: v k = 4: 4 factors, 99.9% variance   > test-prune.R:   > test-prune.R: -- Edges --   > test-prune.R:   > test-prune.R: 8 of 20 edges have |r| >= 0.3   > test-prune.R:   > test-prune.R: -- Pruning --   > test-prune.R:   > test-prune.R: Redundancy (direct, |r| >= 0.9, phi > 0.9): 5 nodes flagged   > test-prune.R: --------------------------------------------------------------------------------   > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes   > test-prune.R: remain in the object; all edges are preserved. Inspect with `x$prune$nodes` and   > test-prune.R: `tidy(x, what = "nodes")`.   > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.   > test-prune.R: Cross-level edges are descriptive score correlations. Per-level fit indices   > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --   > test-prune.R: they do not validate the edges or the hierarchy itself.   > test-prune.R:   > test-prune.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------   > test-prune.R: Engine: pca   > test-prune.R: Rotation: varimax   > test-prune.R: Basis: pearson   > test-prune.R: n: 500   > test-prune.R: k (max): 4   > test-prune.R:   > test-prune.R: -- Levels --   > test-prune.R:   > test-prune.R: v k = 1: 1 factor, 97.3% variance   > test-prune.R: v k = 2: 2 factors, 99.8% variance   > test-prune.R: v k = 3: 3 factors, 99.8% variance   > test-prune.R: v k = 4: 4 factors, 99.9% variance   > test-prune.R:   > test-prune.R: -- Edges --   > test-prune.R:   > test-prune.R: 8 of 20 edges have |r| >= 0.3   > test-prune.R:   > test-prune.R: -- Pruning --   > test-prune.R:   > test-prune.R: Redundancy (direct, |r| >= 0.9, phi > 0.9): 5 nodes flagged   > test-prune.R: --------------------------------------------------------------------------------   > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes   > test-prune.R: remain in the object; all edges are preserved. Inspect with `x$prune$nodes` and   > test-prune.R: `tidy(x, what = "nodes")`.   > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.   > test-prune.R: Cross-level edges are descriptive score correlations. Per-level fit indices   > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --   > test-prune.R: they do not validate the edges or the hierarchy itself.   > test-prune.R:   > test-prune.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------   > test-prune.R: Engine: pca   > test-prune.R: Rotation: varimax   > test-prune.R: Basis: pearson   > test-prune.R: n: 150   > test-prune.R: k (max): 3   > test-prune.R:   > test-prune.R: -- Levels --   > test-prune.R:   > test-prune.R: v k = 1: 1 factor, 20.9% variance   > test-prune.R: v k = 2: 2 factors, 38.4% variance   > test-prune.R: v k = 3: 3 factors, 55.5% variance   > test-prune.R:   > test-prune.R: -- Edges --   > test-prune.R:   > test-prune.R: 6 of 8 edges have |r| >= 0.3   > test-prune.R:   > test-prune.R: -- Pruning --   > test-prune.R:   > test-prune.R: Artifact: Tucker's phi computed for 11 cross-level factor pairs   > test-prune.R: Structural signals: 3 factors flagged (inspect `x$prune$structural`)   > test-prune.R: --------------------------------------------------------------------------------   > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes   > test-prune.R: remain in the object; all edges are preserved. Inspect with `x$prune$nodes` and   > test-prune.R: `tidy(x, what = "nodes")`.   > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.   > test-prune.R: Cross-level edges are descriptive score correlations. Per-level fit indices   > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --   > test-prune.R: they do not validate the edges or the hierarchy itself.   > test-prune.R:   > test-prune.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------   > test-prune.R: Engine: pca   > test-prune.R: Rotation: varimax   > test-prune.R: Basis: pearson   > test-prune.R: n: 150   > test-prune.R: k (max): 3   > test-prune.R:   > test-prune.R: -- Levels --   > test-prune.R:   > test-prune.R: v k = 1: 1 factor, 20.9% variance   > test-prune.R: v k = 2: 2 factors, 38.4% variance   > test-prune.R: v k = 3: 3 factors, 55.5% variance   > test-prune.R:   > test-prune.R: -- Edges --   > test-prune.R:   > test-prune.R: 6 of 8 edges have |r| >= 0.3   > test-prune.R:   > test-prune.R: -- Pruning --   > test-prune.R:   > test-prune.R: Artifact: Tucker's phi computed for 11 cross-level factor pairs   > test-prune.R: Structural signals: 3 factors flagged (inspect `x$prune$structural`)   > test-prune.R: --------------------------------------------------------------------------------   > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes   > test-prune.R: remain in the object; all edges are preserved. Inspect with `x$prune$nodes` and   > test-prune.R: `tidy(x, what = "nodes")`.   > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.   > test-prune.R: Cross-level edges are descriptive score correlations. Per-level fit indices   > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --   > test-prune.R: they do not validate the edges or the hierarchy itself.   > test-prune.R:   > test-prune.R: -- Summary: Bass-Ackwards Analysis (ackwards) ----------------------------------   > test-prune.R: Engine: pca   > test-prune.R: Rotation: varimax   > test-prune.R: Basis: pearson   > test-prune.R: n: 150   > test-prune.R: k (max): 3   > test-prune.R:   > test-prune.R: -- Levels --   > test-prune.R:   > test-prune.R: k = 1: 1 factor (20.9% cumulative variance)   > test-prune.R: m1f1 20.9% eigenvalue 1.25   > test-prune.R:   > test-prune.R: k = 2: 2 factors (38.4% cumulative variance)   > test-prune.R: m2f1 20.3% eigenvalue 1.25   > test-prune.R: m2f2 18.1% eigenvalue 1.05   > test-prune.R:   > test-prune.R: k = 3: 3 factors (55.5% cumulative variance)   > test-prune.R: m3f1 19.5% eigenvalue 1.25   > test-prune.R: m3f2 18.4% eigenvalue 1.05   > test-prune.R: m3f3 17.6% eigenvalue 1.02   > test-prune.R:   > test-prune.R: -- Lineage (primary parents) --   > test-prune.R:   > test-prune.R: m1f1 > m2f1, m2f2   > test-prune.R: m2f1 > m3f1   > test-prune.R: m2f2 > m3f2, m3f3   > test-prune.R:   > test-prune.R: -- Pruning --   > test-prune.R:   > test-prune.R: Artifact: Tucker's phi computed for 11 cross-level pairs   > test-prune.R: Structural signals: 3 factors flagged (inspect `x$prune$structural`)   > test-prune.R: --------------------------------------------------------------------------------   > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes   > test-prune.R: remain in the object with all edges preserved.   > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.   > test-prune.R: Cross-level edges are descriptive score correlations. Per-level fit indices   > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --   > test-prune.R: they do not validate the edges or the hierarchy itself.   > test-scores.R: Error in solve.default(r) :   > test-scores.R: Lapack routine dgesv: system is exactly singular: U[4,4] = 0   > test-prune.R: i Redundancy pruning (direct criterion, |r| >= 0.9 and phi > 0.95) flagged 6 nodes.   > test-prune.R: i Nodes are retained in the object; inspect with `x$prune$nodes` and `x$prune$chains`.   > test-prune.R: Error in solve.default(r) :   > test-prune.R: Lapack routine dgesv: system is exactly singular: U[4,4] = 0   > test-prune.R: Error in solve.default(r) :   > test-prune.R: Lapack routine dgesv: system is exactly singular: U[4,4] = 0   > test-prune.R:   > test-prune.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------   > test-prune.R: Engine: pca   > test-prune.R: Rotation: varimax   > test-prune.R: Basis: pearson   > test-prune.R: n: 100   > test-prune.R: k (max): 3   > test-prune.R:   > test-prune.R: -- Levels --   > test-prune.R:   > test-prune.R: v k = 1: 1 factor, 21.2% variance   > test-prune.R: v k = 2: 2 factors, 40.9% variance   > test-prune.R: v k = 3: 3 factors, 57.5% variance   > test-prune.R:   > test-prune.R: -- Edges --   > test-prune.R:   > test-prune.R: 5 of 8 edges have |r| >= 0.3   > test-prune.R:   > test-prune.R: -- Pruning --   > test-prune.R:   > test-prune.R: Manual: 1 node explicitly flagged (m2f1)   > test-prune.R: --------------------------------------------------------------------------------   > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes   > test-prune.R: remain in the object; all edges are preserved. Inspect with `x$prune$nodes` and   > test-prune.R: `tidy(x, what = "nodes")`.   > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.   > test-prune.R: Cross-level edges are descriptive score correlations. Per-level fit indices   > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --   > test-prune.R: they do not validate the edges or the hierarchy itself.   > test-prune.R:   > test-prune.R: -- Bass-Ackwards Analysis (ackwards) -------------------------------------------   > test-prune.R: Engine: pca   > test-prune.R: Rotation: varimax   > test-prune.R: Basis: pearson   > test-prune.R: n: 100   > test-prune.R: k (max): 3   > test-prune.R:   > test-prune.R: -- Levels --   > test-prune.R:   > test-prune.R: v k = 1: 1 factor, 21.2% variance   > test-prune.R: v k = 2: 2 factors, 40.9% variance   > test-prune.R: v k = 3: 3 factors, 57.5% variance   > test-prune.R:   > test-prune.R: -- Edges --   > test-prune.R:   > test-prune.R: 5 of 8 edges have |r| >= 0.3   > test-prune.R:   > test-prune.R: -- Pruning --   > test-prune.R:   > test-prune.R: Manual: 1 node explicitly flagged (m2f1)   > test-prune.R: --------------------------------------------------------------------------------   > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes   > test-prune.R: remain in the object; all edges are preserved. Inspect with `x$prune$nodes` and   > test-prune.R: `tidy(x, what = "nodes")`.   > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.   > test-prune.R: Cross-level edges are descriptive score correlations. Per-level fit indices   > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --   > test-prune.R: they do not validate the edges or the hierarchy itself.   > test-prune.R:   > test-prune.R: -- Summary: Bass-Ackwards Analysis (ackwards) ----------------------------------   > test-prune.R: Engine: pca   > test-prune.R: Rotation: varimax   > test-prune.R: Basis: pearson   > test-prune.R: n: 100   > test-prune.R: k (max): 3   > test-prune.R:   > test-prune.R: -- Levels --   > test-prune.R:   > test-prune.R: k = 1: 1 factor (21.2% cumulative variance)   > test-prune.R: m1f1 21.2% eigenvalue 1.27   > test-prune.R:   > test-prune.R: k = 2: 2 factors (40.9% cumulative variance)   > test-prune.R: m2f1 21.1% eigenvalue 1.27   > test-prune.R: m2f2 19.7% eigenvalue 1.18   > test-prune.R:   > test-prune.R: k = 3: 3 factors (57.5% cumulative variance)   > test-prune.R: m3f1 19.5% eigenvalue 1.27   > test-prune.R: m3f2 19.4% eigenvalue 1.18   > test-prune.R: m3f3 18.6% eigenvalue 1.00   > test-prune.R:   > test-prune.R: -- Lineage (primary parents) --   > test-prune.R:   > test-prune.R: m1f1 > m2f1, m2f2   > test-prune.R: m2f1 > m3f1, m3f3   > test-prune.R: m2f2 > m3f2   > test-prune.R:   > test-prune.R: -- Pruning --   > test-prune.R:   > test-prune.R: Manual: 1 node explicitly flagged (m2f1)   > test-prune.R: --------------------------------------------------------------------------------   > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes   > test-prune.R: remain in the object with all edges preserved.   > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.   > test-prune.R: Cross-level edges are descriptive score correlations. Per-level fit indices   > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --   > test-prune.R: they do not validate the edges or the hierarchy itself.   > test-prune.R:   > test-prune.R: -- Summary: Bass-Ackwards Analysis (ackwards) ----------------------------------   > test-prune.R: Engine: pca   > test-prune.R: Rotation: varimax   > test-prune.R: Basis: pearson   > test-prune.R: n: 100   > test-prune.R: k (max): 3   > test-prune.R:   > test-prune.R: -- Levels --   > test-prune.R:   > test-prune.R: k = 1: 1 factor (21.2% cumulative variance)   > test-prune.R: m1f1 21.2% eigenvalue 1.27   > test-prune.R:   > test-prune.R: k = 2: 2 factors (40.9% cumulative variance)   > test-prune.R: m2f1 21.1% eigenvalue 1.27   > test-prune.R: m2f2 19.7% eigenvalue 1.18   > test-prune.R:   > test-prune.R: k = 3: 3 factors (57.5% cumulative variance)   > test-prune.R: m3f1 19.5% eigenvalue 1.27   > test-prune.R: m3f2 19.4% eigenvalue 1.18   > test-prune.R: m3f3 18.6% eigenvalue 1.00   > test-prune.R:   > test-prune.R: -- Lineage (primary parents) --   > test-prune.R:   > test-prune.R: m1f1 > m2f1, m2f2   > test-prune.R: m2f1 > m3f1, m3f3   > test-prune.R: m2f2 > m3f2   > test-prune.R:   > test-prune.R: -- Pruning --   > test-prune.R:   > test-prune.R: Manual: 1 node explicitly flagged (m2f1)   > test-prune.R: --------------------------------------------------------------------------------   > test-prune.R: Note: Pruning is interpretive relabeling, not re-estimation. Flagged nodes   > test-prune.R: remain in the object with all edges preserved.   > test-prune.R: Note: This is a series of linked solutions, not a fitted hierarchical model.   > test-prune.R: Cross-level edges are descriptive score correlations. Per-level fit indices   > test-prune.R: (EFA/ESEM) describe how well a k-factor model fits the items at that level --   > test-prune.R: they do not validate the edges or the hierarchy itself.   [ FAIL 1 | WARN 1 | SKIP 11 | PASS 2285 ]      ══ Skipped tests (11) ══════════════════════════════════════════════════════════   • NEWS.md not reachable from this test context (1):     'test-docs-no-milestone-refs.R:20:3'   • On CRAN (5): 'test-print-snapshot.R:16:1', 'test-print-snapshot.R:23:1',     'test-print-snapshot.R:29:1', 'test-print-snapshot.R:38:1',     'test-print-snapshot.R:48:1'   • README files not reachable from this test context (1):     'test-docs-no-milestone-refs.R:31:3'   • data-raw/sim16.R not reachable from this test context (1):     'test-data.R:173:3'   • inst/CITATION not reachable from this test context (1):     'test-docs-citations.R:43:3'   • man/ackwards.Rd not reachable from this test context (1):     'test-docs-citations.R:21:3'   • vignettes/ not reachable from this test context (1):     'test-docs-no-milestone-refs.R:42:3'      ══ Failed tests ════════════════════════════════════════════════════════════════   ── Error ('test-esem.R:628:3'): ESEM results are identical across serial and parallel future plans ──   <FutureInterruptError/FutureError/error/FutureCondition/condition>   Error: A future ('future_lapply-2') of class MulticoreFuture was interrupted, while running on localhost (pid 31045) [future 'future_lapply-2' (e053405ffeba2fa9bdcee7645a24b9ea-32); on e053405ffeba2fa9bdcee7645a24b9ea@macos13-vm.local<29633> at 2026-07-25 08:34:16.3932]   Backtrace:        ▆     1. ├─base::suppressMessages(...) at test-esem.R:628:3     2. │ └─base::withCallingHandlers(...)     3. ├─base::suppressWarnings(...)     4. │ └─base::withCallingHandlers(...)     5. └─ackwards::ackwards(...)     6. └─ackwards:::esem_levels(...)     7. └─ackwards:::.esem_lapply(...)     8. └─future.apply::future_lapply(X, FUN, future.seed = TRUE)     9. └─future.apply:::future_xapply(...)    10. └─base::tryCatch(...)    11. └─base (local) tryCatchList(expr, classes, parentenv, handlers)    12. └─base (local) tryCatchOne(...)    13. └─value[[3L]](cond)    14. └─future.apply:::onError(e, futures = fs, debug = debug)      [ FAIL 1 | WARN 1 | SKIP 11 | PASS 2285 ]   Error:   ! Test failures.   Execution halted
• checking for unstated dependencies in vignettes ... OK
• checking package vignettes ... OK
• checking re-building of vignette outputs ... [12s/11s] OK
• checking PDF version of manual ... [3s/3s] OK
• DONE

Status: 1 ERROR