Running ‘testthat.R’ [11s/15s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > # This file is part of the standard setup for testthat.
  > # It is recommended that you do not modify it.
  > #
  > # Where should you do additional test configuration?
  > # Learn more about the roles of various files in:
  > # * https://r-pkgs.org/tests.html
  > # * https://testthat.r-lib.org/reference/test_package.html#special-files
  > 
  > library(testthat)
  > library(lavaanExtra)
  Suggested citation: Thériault, R. (2022).lavaanExtra: Convenience functions for lavaan 
  (R package version 0.1.3) [Computer software]. https://lavaanExtra.remi-theriault.com/ 
  > 
  > test_check("lavaanExtra")
  This is lavaan 0.6-13
  lavaan is FREE software! Please report any bugs.
  lavaan 0.6.13 ended normally after 35 iterations
  
    Estimator                                         ML
    Optimization method                           NLMINB
    Number of model parameters                        21
  
    Number of observations                           301
  
  Model Test User Model:
                                                Standard      Scaled
    Test Statistic                                85.306      87.132
    Degrees of freedom                                24          24
    P-value (Chi-square)                           0.000       0.000
    Scaling correction factor                                  0.979
      Yuan-Bentler correction (Mplus variant)                       
  
  Model Test Baseline Model:
  
    Test statistic                               918.852     880.082
    Degrees of freedom                                36          36
    P-value                                        0.000       0.000
    Scaling correction factor                                  1.044
  
  User Model versus Baseline Model:
  
    Comparative Fit Index (CFI)                    0.931       0.925
    Tucker-Lewis Index (TLI)                       0.896       0.888
                                                                    
    Robust Comparative Fit Index (CFI)                         0.930
    Robust Tucker-Lewis Index (TLI)                            0.895
  
  Loglikelihood and Information Criteria:
  
    Loglikelihood user model (H0)              -3737.745   -3737.745
    Scaling correction factor                                  1.133
        for the MLR correction                                      
    Loglikelihood unrestricted model (H1)      -3695.092   -3695.092
    Scaling correction factor                                  1.051
        for the MLR correction                                      
                                                                    
    Akaike (AIC)                                7517.490    7517.490
    Bayesian (BIC)                              7595.339    7595.339
    Sample-size adjusted Bayesian (SABIC)       7528.739    7528.739
  
  Root Mean Square Error of Approximation:
  
    RMSEA                                          0.092       0.093
    90 Percent confidence interval - lower         0.071       0.073
    90 Percent confidence interval - upper         0.114       0.115
    P-value H_0: RMSEA <= 0.050                    0.001       0.001
    P-value H_0: RMSEA >= 0.080                    0.840       0.862
                                                                    
    Robust RMSEA                                               0.092
    90 Percent confidence interval - lower                     0.072
    90 Percent confidence interval - upper                     0.114
    P-value H_0: Robust RMSEA <= 0.050                         0.001
    P-value H_0: Robust RMSEA >= 0.080                         0.849
  
  Standardized Root Mean Square Residual:
  
    SRMR                                           0.065       0.065
  
  Parameter Estimates:
  
    Standard errors                             Sandwich
    Information bread                           Observed
    Observed information based on                Hessian
  
  Latent Variables:
                     Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    visual =~                                                             
      x1                1.000                               0.900    0.772
      x2                0.554    0.132    4.191    0.000    0.498    0.424
      x3                0.729    0.141    5.170    0.000    0.656    0.581
    textual =~                                                            
      x4                1.000                               0.990    0.852
      x5                1.113    0.066   16.946    0.000    1.102    0.855
      x6                0.926    0.061   15.089    0.000    0.917    0.838
    speed =~                                                              
      x7                1.000                               0.619    0.570
      x8                1.180    0.130    9.046    0.000    0.731    0.723
      x9                1.082    0.266    4.060    0.000    0.670    0.665
  
  Covariances:
                     Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    visual ~~                                                             
      textual           0.408    0.099    4.110    0.000    0.459    0.459
      speed             0.262    0.060    4.366    0.000    0.471    0.471
    textual ~~                                                            
      speed             0.173    0.056    3.081    0.002    0.283    0.283
  
  Variances:
                     Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
     .x1                0.549    0.156    3.509    0.000    0.549    0.404
     .x2                1.134    0.112   10.135    0.000    1.134    0.821
     .x3                0.844    0.100    8.419    0.000    0.844    0.662
     .x4                0.371    0.050    7.382    0.000    0.371    0.275
     .x5                0.446    0.057    7.870    0.000    0.446    0.269
     .x6                0.356    0.047    7.658    0.000    0.356    0.298
     .x7                0.799    0.097    8.222    0.000    0.799    0.676
     .x8                0.488    0.120    4.080    0.000    0.488    0.477
     .x9                0.566    0.119    4.768    0.000    0.566    0.558
      visual            0.809    0.180    4.486    0.000    1.000    1.000
      textual           0.979    0.121    8.075    0.000    1.000    1.000
      speed             0.384    0.107    3.596    0.000    1.000    1.000
  
  R-Square:
                     Estimate
      x1                0.596
      x2                0.179
      x3                0.338
      x4                0.725
      x5                0.731
      x6                0.702
      x7                0.324
      x8                0.523
      x9                0.442
  
  lavaan 0.6.13 ended normally after 37 iterations
  
    Estimator                                         ML
    Optimization method                           NLMINB
    Number of model parameters                        19
  
    Number of observations                           301
  
  Model Test User Model:
                                                Standard      Scaled
    Test Statistic                                57.873      60.657
    Degrees of freedom                                17          17
    P-value (Chi-square)                           0.000       0.000
    Scaling correction factor                                  0.954
      Yuan-Bentler correction (Mplus variant)                       
  
  Model Test Baseline Model:
  
    Test statistic                               785.270     772.454
    Degrees of freedom                                28          28
    P-value                                        0.000       0.000
    Scaling correction factor                                  1.017
  
  User Model versus Baseline Model:
  
    Comparative Fit Index (CFI)                    0.946       0.941
    Tucker-Lewis Index (TLI)                       0.911       0.903
                                                                    
    Robust Comparative Fit Index (CFI)                         0.945
    Robust Tucker-Lewis Index (TLI)                            0.909
  
  Loglikelihood and Information Criteria:
  
    Loglikelihood user model (H0)              -3317.623   -3317.623
    Scaling correction factor                                  1.094
        for the MLR correction                                      
    Loglikelihood unrestricted model (H1)      -3288.687   -3288.687
    Scaling correction factor                                  1.028
        for the MLR correction                                      
                                                                    
    Akaike (AIC)                                6673.246    6673.246
    Bayesian (BIC)                              6743.682    6743.682
    Sample-size adjusted Bayesian (SABIC)       6683.424    6683.424
  
  Root Mean Square Error of Approximation:
  
    RMSEA                                          0.089       0.092
    90 Percent confidence interval - lower         0.065       0.067
    90 Percent confidence interval - upper         0.115       0.119
    P-value H_0: RMSEA <= 0.050                    0.006       0.004
    P-value H_0: RMSEA >= 0.080                    0.751       0.804
                                                                    
    Robust RMSEA                                               0.090
    90 Percent confidence interval - lower                     0.066
    90 Percent confidence interval - upper                     0.115
    P-value H_0: Robust RMSEA <= 0.050                         0.004
    P-value H_0: Robust RMSEA >= 0.080                         0.773
  
  Standardized Root Mean Square Residual:
  
    SRMR                                           0.059       0.059
  
  Parameter Estimates:
  
    Standard errors                             Sandwich
    Information bread                           Observed
    Observed information based on                Hessian
  
  Latent Variables:
                     Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    visual =~                                                             
      x2                1.000                               0.544    0.463
      x3                1.522    0.582    2.614    0.009    0.829    0.734
    textual =~                                                            
      x4                1.000                               0.986    0.848
      x5                1.125    0.068   16.540    0.000    1.109    0.861
      x6                0.928    0.060   15.421    0.000    0.914    0.836
    speed =~                                                              
      x7                1.000                               0.646    0.594
      x8                1.168    0.133    8.750    0.000    0.754    0.746
      x9                0.983    0.169    5.817    0.000    0.634    0.630
  
  Covariances:
                     Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
    visual ~~                                                             
      textual           0.140    0.083    1.690    0.091    0.260    0.260
      speed             0.132    0.042    3.139    0.002    0.375    0.375
    textual ~~                                                            
      speed             0.173    0.059    2.949    0.003    0.273    0.273
  
  Variances:
                     Estimate  Std.Err  z-value  P(>|z|)   Std.lv  Std.all
     .x2                1.085    0.157    6.928    0.000    1.085    0.786
     .x3                0.588    0.285    2.066    0.039    0.588    0.461
     .x4                0.379    0.050    7.513    0.000    0.379    0.281
     .x5                0.430    0.058    7.424    0.000    0.430    0.259
     .x6                0.360    0.047    7.687    0.000    0.360    0.301
     .x7                0.766    0.081    9.482    0.000    0.766    0.648
     .x8                0.453    0.095    4.763    0.000    0.453    0.444
     .x9                0.613    0.088    6.967    0.000    0.613    0.603
      visual            0.296    0.127    2.333    0.020    1.000    1.000
      textual           0.971    0.121    8.023    0.000    1.000    1.000
      speed             0.417    0.089    4.707    0.000    1.000    1.000
  
  R-Square:
                     Estimate
      x2                0.214
      x3                0.539
      x4                0.719
      x5                0.741
      x6                0.699
      x7                0.352
      x8                0.556
      x9                0.397
  
  [ FAIL 10 | WARN 0 | SKIP 5 | PASS 30 ]
  
  ══ Skipped tests ═══════════════════════════════════════════════════════════════
  • On CRAN (5)
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-nice_tidySEM.R:44'): nice_tidySEM on CFA ───────────────────────
  <rlib_error_package_not_found/rlang_error/error/condition>
  Error in `nice_tidySEM(fit.cfa)`: The package `tidySEM` is required for this function.
  Backtrace:
      ▆
   1. ├─testthat::expect_s3_class(nice_tidySEM(fit.cfa), c("gg", "ggplot")) at test-nice_tidySEM.R:44:2
   2. │ └─testthat::quasi_label(enquo(object), arg = "object")
   3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
   4. └─lavaanExtra::nice_tidySEM(fit.cfa)
   5.   └─rlang::check_installed(c("tidySEM"), reason = "for this function.")
  ── Error ('test-nice_tidySEM.R:51'): nice_tidySEM on CFA with manual structure ──
  <rlib_error_package_not_found/rlang_error/error/condition>
  Error in `nice_tidySEM(fit.cfa, layout = manual.structure)`: The package `tidySEM` is required for this function.
  Backtrace:
      ▆
   1. ├─testthat::expect_s3_class(...) at test-nice_tidySEM.R:51:2
   2. │ └─testthat::quasi_label(enquo(object), arg = "object")
   3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
   4. └─lavaanExtra::nice_tidySEM(fit.cfa, layout = manual.structure)
   5.   └─rlang::check_installed(c("tidySEM"), reason = "for this function.")
  ── Error ('test-nice_tidySEM.R:58'): nice_tidySEM on SEM ───────────────────────
  <rlib_error_package_not_found/rlang_error/error/condition>
  Error in `nice_tidySEM(fit.sem)`: The package `tidySEM` is required for this function.
  Backtrace:
      ▆
   1. ├─testthat::expect_s3_class(nice_tidySEM(fit.sem), c("gg", "ggplot")) at test-nice_tidySEM.R:58:2
   2. │ └─testthat::quasi_label(enquo(object), arg = "object")
   3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
   4. └─lavaanExtra::nice_tidySEM(fit.sem)
   5.   └─rlang::check_installed(c("tidySEM"), reason = "for this function.")
  ── Error ('test-nice_tidySEM.R:65'): nice_tidySEM on SEM with manual structure ──
  <rlib_error_package_not_found/rlang_error/error/condition>
  Error in `nice_tidySEM(fit.sem, layout = manual.structure)`: The package `tidySEM` is required for this function.
  Backtrace:
      ▆
   1. ├─testthat::expect_s3_class(...) at test-nice_tidySEM.R:65:2
   2. │ └─testthat::quasi_label(enquo(object), arg = "object")
   3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
   4. └─lavaanExtra::nice_tidySEM(fit.sem, layout = manual.structure)
   5.   └─rlang::check_installed(c("tidySEM"), reason = "for this function.")
  ── Error ('test-nice_tidySEM.R:72'): nice_tidySEM on SEM with automatic structure ──
  <rlib_error_package_not_found/rlang_error/error/condition>
  Error in `nice_tidySEM(fit.sem2, layout = indirect)`: The package `tidySEM` is required for this function.
  Backtrace:
      ▆
   1. ├─testthat::expect_s3_class(...) at test-nice_tidySEM.R:72:2
   2. │ └─testthat::quasi_label(enquo(object), arg = "object")
   3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
   4. └─lavaanExtra::nice_tidySEM(fit.sem2, layout = indirect)
   5.   └─rlang::check_installed(c("tidySEM"), reason = "for this function.")
  ── Error ('test-nice_tidySEM.R:79'): nice_tidySEM on SEM with automatic structure and labels ──
  <rlib_error_package_not_found/rlang_error/error/condition>
  Error in `nice_tidySEM(fit.sem2, layout = indirect, label = label)`: The package `tidySEM` is required for this function.
  Backtrace:
      ▆
   1. ├─testthat::expect_s3_class(...) at test-nice_tidySEM.R:79:2
   2. │ └─testthat::quasi_label(enquo(object), arg = "object")
   3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
   4. └─lavaanExtra::nice_tidySEM(fit.sem2, layout = indirect, label = label)
   5.   └─rlang::check_installed(c("tidySEM"), reason = "for this function.")
  ── Error ('test-nice_tidySEM.R:88'): nice_tidySEM on lavaan ────────────────────
  <rlib_error_package_not_found/rlang_error/error/condition>
  Error in `nice_tidySEM(fit.lavaan)`: The package `tidySEM` is required for this function.
  Backtrace:
      ▆
   1. ├─testthat::expect_s3_class(nice_tidySEM(fit.lavaan), c("gg", "ggplot")) at test-nice_tidySEM.R:88:2
   2. │ └─testthat::quasi_label(enquo(object), arg = "object")
   3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
   4. └─lavaanExtra::nice_tidySEM(fit.lavaan)
   5.   └─rlang::check_installed(c("tidySEM"), reason = "for this function.")
  ── Error ('test-nice_tidySEM.R:95'): nice_tidySEM on lavaan with manual structure ──
  <rlib_error_package_not_found/rlang_error/error/condition>
  Error in `nice_tidySEM(fit.lavaan, layout = manual.structure)`: The package `tidySEM` is required for this function.
  Backtrace:
      ▆
   1. ├─testthat::expect_s3_class(...) at test-nice_tidySEM.R:95:2
   2. │ └─testthat::quasi_label(enquo(object), arg = "object")
   3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
   4. └─lavaanExtra::nice_tidySEM(fit.lavaan, layout = manual.structure)
   5.   └─rlang::check_installed(c("tidySEM"), reason = "for this function.")
  ── Error ('test-nice_tidySEM.R:102'): nice_tidySEM on sem with hide_nonsig_edges ──
  <rlib_error_package_not_found/rlang_error/error/condition>
  Error in `nice_tidySEM(fit.sem, hide_nonsig_edges = TRUE)`: The package `tidySEM` is required for this function.
  Backtrace:
      ▆
   1. ├─testthat::expect_s3_class(...) at test-nice_tidySEM.R:102:2
   2. │ └─testthat::quasi_label(enquo(object), arg = "object")
   3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
   4. └─lavaanExtra::nice_tidySEM(fit.sem, hide_nonsig_edges = TRUE)
   5.   └─rlang::check_installed(c("tidySEM"), reason = "for this function.")
  ── Error ('test-nice_tidySEM.R:109'): nice_tidySEM on sem with plot = FALSE ────
  <rlib_error_package_not_found/rlang_error/error/condition>
  Error in `nice_tidySEM(fit.sem, plot = FALSE)`: The package `tidySEM` is required for this function.
  Backtrace:
      ▆
   1. ├─testthat::expect_s3_class(...) at test-nice_tidySEM.R:109:2
   2. │ └─testthat::quasi_label(enquo(object), arg = "object")
   3. │   └─rlang::eval_bare(expr, quo_get_env(quo))
   4. └─lavaanExtra::nice_tidySEM(fit.sem, plot = FALSE)
   5.   └─rlang::check_installed(c("tidySEM"), reason = "for this function.")
  
  [ FAIL 10 | WARN 0 | SKIP 5 | PASS 30 ]
  Error: Test failures
  Execution halted