Running ‘testthat.R’ [8s/10s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(x12)
  Loading required package: x13binary
  x12 is ready to use.
  Use the package x12GUI for a Graphical User Interface. 
  
  By default the X13-ARIMA-SEATS binaries provided by the R package x13binary
  
  are used but this can be changed with x12path(validpath) 
  
  ---------------
  
  Suggestions and bug-reports can be submitted at: https://github.com/statistikat/x12/issues
  > test_check("x12")
  
   X-13ARIMA-SEATS Seasonal Adjustment Program
   Version Number 1.1 Build 39
   Execution began  Oct 22, 2018  00.36.59 
  
    Reading input spec file from Rout.spc
    Storing any program output into Rout.html
    Storing any program error messages into Rout_err.html
    Storing any diagnostics output into gra_Rout/Rout.udg
  
     
    NOTE: The X-13ARIMA-SEATS diagnostic file (.udg) has been stored
          in the directory specified by the graphics (-g) option.
  
    NOTE: The X-13ARIMA-SEATS diagnostic file (.udg) is generated 
          since the graphics (-g) option was specified.
     
    WARNING: At least one visually significant seasonal peak has been found
             in the estimated spectrum of the regARIMA residuals.
     
    WARNING: At least one visually significant trading day peak has been
             found in one or more of the estimated spectra.
  
   Execution complete for Rout.spc at  Oct 22, 2018  00.37.00 
  
  	Time Series
  
  Frequency: 12 
  Span: 1st month,1949 to 12th month,1960 
  
  	Model Definition
  
  ARIMA Model: (0 1 1)(0 1 1) (Automatic Model Choice)
  Model Span:  
  Transformation: Automatic selection : Log(y) 
  Regression Model: none 
  
  	Outlier Detection
  
  No outlier detection performed
  
  	Seasonal Adjustment
  
  Identifiable Seasonality: yes 
  Seasonal Peaks: rsd 
  Trading Day Peaks: sa irr 
  Overall Index of Quality of SA
  (Acceptance Region from 0 to 1)
  Q: 0.26 
  Number of M statistics outside the limits: 0 
  
  SA decomposition: multiplicative 
  Moving average used to estimate the seasonal factors: 3x3 
  Moving average used to estimate the final trend-cycle: 9-term Henderson filter
  ── 1. Error: AirPassenger ts x12 run (@test_AirPassengers.R#5)  ────────────────
  replacement has 2 rows, data has 1
  1: summary(xts) at testthat/test_AirPassengers.R:5
  2: summary(xts)
  3: .local(object, ...)
  4: summary.output.workhorse(object@dg, fullSummary = fullSummary, spectra.detail = spectra.detail, 
         almostout = almostout, rsd.autocorr = rsd.autocorr, quality.stat = quality.stat, 
         likelihood.stat = likelihood.stat, aape = aape, id.rsdseas = id.rsdseas, slidingspans = slidingspans, 
         history = history, identify = identify)
  5: `[<-`(`*tmp*`, dim(summary.output)[1] + 1, 1, value = "Span")
  6: `[<-.data.frame`(`*tmp*`, dim(summary.output)[1] + 1, 1, value = "Span")
  7: xpdrows.data.frame(x, rows, new.rows)
  8: `[[<-`(`*tmp*`, i, value = c("Frequency", NA))
  9: `[[<-.data.frame`(`*tmp*`, i, value = c("Frequency", NA))
  10: stop(sprintf(ngettext(N, "replacement has %d row, data has %d", "replacement has %d rows, data has %d"), 
         N, nrows), domain = NA)
  
  
   X-13ARIMA-SEATS Seasonal Adjustment Program
   Version Number 1.1 Build 39
   Execution began  Oct 22, 2018  00.37.01 
  
    Reading input spec file from Rout.spc
    Storing any program output into Rout.html
    Storing any program error messages into Rout_err.html
    Storing any diagnostics output into gra_Rout/Rout.udg
  
     
    NOTE: The X-13ARIMA-SEATS diagnostic file (.udg) has been stored
          in the directory specified by the graphics (-g) option.
  
    NOTE: The X-13ARIMA-SEATS diagnostic file (.udg) is generated 
          since the graphics (-g) option was specified.
     
    WARNING: At least one visually significant seasonal peak has been found
             in the estimated spectrum of the regARIMA residuals.
     
    WARNING: At least one visually significant trading day peak has been
             found in one or more of the estimated spectra.
  
   Execution complete for Rout.spc at  Oct 22, 2018  00.37.02 
  --------------------------  Rout  ------------------------------------
  -----------------------------------------------------------------------------------
  
  	Time Series
  
  Frequency: 12 
  Span: 1st month,1949 to 12th month,1960 
  
  	Model Definition
  
  ARIMA Model: (0 1 1)(0 1 1) (Automatic Model Choice)
  Model Span:  
  Transformation: Automatic selection : Log(y) 
  Regression Model: none 
  
  	Outlier Detection
  
  No outlier detection performed
  
  	Seasonal Adjustment
  
  Identifiable Seasonality: yes 
  Seasonal Peaks: rsd 
  Trading Day Peaks: sa irr 
  Overall Index of Quality of SA
  (Acceptance Region from 0 to 1)
  Q: 0.26 
  Number of M statistics outside the limits: 0 
  
  SA decomposition: multiplicative 
  Moving average used to estimate the seasonal factors: 3x3 
  Moving average used to estimate the final trend-cycle: 9-term Henderson filter
  ── 2. Error: AirPassenger x12Single x12 run (@test_AirPassengers.R#12)  ────────
  replacement has 2 rows, data has 1
  1: summary(xs) at testthat/test_AirPassengers.R:12
  2: summary(xs)
  3: .local(object, ...)
  4: summary(object@x12Output, fullSummary = fullSummary, spectra.detail = spectra.detail, 
         almostout = almostout, rsd.autocorr = rsd.autocorr, quality.stat = quality.stat, 
         likelihood.stat = likelihood.stat, aape = aape, id.rsdseas = id.rsdseas, slidingspans = slidingspans, 
         history = history, identify = identify, print = print)
  5: summary(object@x12Output, fullSummary = fullSummary, spectra.detail = spectra.detail, 
         almostout = almostout, rsd.autocorr = rsd.autocorr, quality.stat = quality.stat, 
         likelihood.stat = likelihood.stat, aape = aape, id.rsdseas = id.rsdseas, slidingspans = slidingspans, 
         history = history, identify = identify, print = print)
  6: .local(object, ...)
  7: summary.output.workhorse(object@dg, fullSummary = fullSummary, spectra.detail = spectra.detail, 
         almostout = almostout, rsd.autocorr = rsd.autocorr, quality.stat = quality.stat, 
         likelihood.stat = likelihood.stat, aape = aape, id.rsdseas = id.rsdseas, slidingspans = slidingspans, 
         history = history, identify = identify)
  8: `[<-`(`*tmp*`, dim(summary.output)[1] + 1, 1, value = "Span")
  9: `[<-.data.frame`(`*tmp*`, dim(summary.output)[1] + 1, 1, value = "Span")
  10: xpdrows.data.frame(x, rows, new.rows)
  11: `[[<-`(`*tmp*`, i, value = c("Frequency", NA))
  12: `[[<-.data.frame`(`*tmp*`, i, value = c("Frequency", NA))
  13: stop(sprintf(ngettext(N, "replacement has %d row, data has %d", "replacement has %d rows, data has %d"), 
         N, nrows), domain = NA)
  
  
   X-13ARIMA-SEATS Seasonal Adjustment Program
   Version Number 1.1 Build 39
   Execution began  Oct 22, 2018  00.37.02 
  
    Reading input spec file from Series_1.spc
    Storing any program output into Series_1.html
    Storing any program error messages into Series_1_err.html
    Storing any diagnostics output into gra_Series_1/Series_1.udg
  
     
    NOTE: The X-13ARIMA-SEATS diagnostic file (.udg) has been stored
          in the directory specified by the graphics (-g) option.
  
    NOTE: The X-13ARIMA-SEATS diagnostic file (.udg) is generated 
          since the graphics (-g) option was specified.
     
    WARNING: At least one visually significant seasonal peak has been found
             in the estimated spectrum of the regARIMA residuals.
     
    WARNING: At least one visually significant trading day peak has been
             found in one or more of the estimated spectra.
  
   Execution complete for Series_1.spc at  Oct 22, 2018  00.37.04 
  
   X-13ARIMA-SEATS Seasonal Adjustment Program
   Version Number 1.1 Build 39
   Execution began  Oct 22, 2018  00.37.04 
  
    Reading input spec file from Series_2.spc
    Storing any program output into Series_2.html
    Storing any program error messages into Series_2_err.html
    Storing any diagnostics output into gra_Series_2/Series_2.udg
  
     
    NOTE: The X-13ARIMA-SEATS diagnostic file (.udg) has been stored
          in the directory specified by the graphics (-g) option.
  
    NOTE: The X-13ARIMA-SEATS diagnostic file (.udg) is generated 
          since the graphics (-g) option was specified.
     
    WARNING: At least one visually significant seasonal peak has been found
             in the estimated spectrum of the regARIMA residuals.
     
    WARNING: At least one visually significant trading day peak has been
             found in one or more of the estimated spectra.
  
   Execution complete for Series_2.spc at  Oct 22, 2018  00.37.06 
  
   X-13ARIMA-SEATS Seasonal Adjustment Program
   Version Number 1.1 Build 39
   Execution began  Oct 22, 2018  00.37.06 
  
    Reading input spec file from Series_3.spc
    Storing any program output into Series_3.html
    Storing any program error messages into Series_3_err.html
    Storing any diagnostics output into gra_Series_3/Series_3.udg
  
     
    NOTE: The X-13ARIMA-SEATS diagnostic file (.udg) has been stored
          in the directory specified by the graphics (-g) option.
  
    NOTE: The X-13ARIMA-SEATS diagnostic file (.udg) is generated 
          since the graphics (-g) option was specified.
     
    WARNING: At least one visually significant seasonal peak has been found
             in the estimated spectrum of the regARIMA residuals.
     
    WARNING: At least one visually significant trading day peak has been
             found in one or more of the estimated spectra.
  
   Execution complete for Series_3.spc at  Oct 22, 2018  00.37.07 
  Time difference of 5.275244 secs
  -----------------------------------------------------------------------------------
  --------------------------   Series_1   ------------------------------------
  -----------------------------------------------------------------------------------
  
  	Time Series
  
  Frequency: 12 
  Span: 1st month,1949 to 12th month,1960 
  
  	Model Definition
  
  ARIMA Model: (0 1 1)(0 1 1) (Automatic Model Choice)
  Model Span:  
  Transformation: Automatic selection : Log(y) 
  Regression Model: none 
  
  	Outlier Detection
  
  No outlier detection performed
  
  	Seasonal Adjustment
  
  Identifiable Seasonality: yes 
  Seasonal Peaks: rsd 
  Trading Day Peaks: sa irr 
  Overall Index of Quality of SA
  (Acceptance Region from 0 to 1)
  Q: 0.26 
  Number of M statistics outside the limits: 0 
  
  SA decomposition: multiplicative 
  Moving average used to estimate the seasonal factors: 3x3 
  Moving average used to estimate the final trend-cycle: 9-term Henderson filter
  ── 3. Error: AirPassenger x12Batch x12 run (@test_AirPassengers.R#19)  ─────────
  replacement has 2 rows, data has 1
  1: summary(xb) at testthat/test_AirPassengers.R:19
  2: summary(xb)
  3: .local(object, ...)
  4: summary(object@x12List[[i]], fullSummary = fullSummary, spectra.detail = spectra.detail, 
         almostout = almostout, rsd.autocorr = rsd.autocorr, quality.stat = quality.stat, 
         likelihood.stat = likelihood.stat, aape = aape, id.rsdseas = id.rsdseas, slidingspans = slidingspans, 
         history = history, identify = identify, oldOutput = oldOutput, print = print)
  5: summary(object@x12List[[i]], fullSummary = fullSummary, spectra.detail = spectra.detail, 
         almostout = almostout, rsd.autocorr = rsd.autocorr, quality.stat = quality.stat, 
         likelihood.stat = likelihood.stat, aape = aape, id.rsdseas = id.rsdseas, slidingspans = slidingspans, 
         history = history, identify = identify, oldOutput = oldOutput, print = print)
  6: .local(object, ...)
  7: summary(object@x12Output, fullSummary = fullSummary, spectra.detail = spectra.detail, 
         almostout = almostout, rsd.autocorr = rsd.autocorr, quality.stat = quality.stat, 
         likelihood.stat = likelihood.stat, aape = aape, id.rsdseas = id.rsdseas, slidingspans = slidingspans, 
         history = history, identify = identify, print = print)
  8: summary(object@x12Output, fullSummary = fullSummary, spectra.detail = spectra.detail, 
         almostout = almostout, rsd.autocorr = rsd.autocorr, quality.stat = quality.stat, 
         likelihood.stat = likelihood.stat, aape = aape, id.rsdseas = id.rsdseas, slidingspans = slidingspans, 
         history = history, identify = identify, print = print)
  9: .local(object, ...)
  10: summary.output.workhorse(object@dg, fullSummary = fullSummary, spectra.detail = spectra.detail, 
         almostout = almostout, rsd.autocorr = rsd.autocorr, quality.stat = quality.stat, 
         likelihood.stat = likelihood.stat, aape = aape, id.rsdseas = id.rsdseas, slidingspans = slidingspans, 
         history = history, identify = identify)
  11: `[<-`(`*tmp*`, dim(summary.output)[1] + 1, 1, value = "Span")
  12: `[<-.data.frame`(`*tmp*`, dim(summary.output)[1] + 1, 1, value = "Span")
  13: xpdrows.data.frame(x, rows, new.rows)
  14: `[[<-`(`*tmp*`, i, value = c("Frequency", NA))
  15: `[[<-.data.frame`(`*tmp*`, i, value = c("Frequency", NA))
  16: stop(sprintf(ngettext(N, "replacement has %d row, data has %d", "replacement has %d rows, data has %d"), 
         N, nrows), domain = NA)
  
  ══ testthat results  ═══════════════════════════════════════════════════════════
  OK: 0 SKIPPED: 0 FAILED: 3
  1. Error: AirPassenger ts x12 run (@test_AirPassengers.R#5) 
  2. Error: AirPassenger x12Single x12 run (@test_AirPassengers.R#12) 
  3. Error: AirPassenger x12Batch x12 run (@test_AirPassengers.R#19) 
  
  Error: testthat unit tests failed
  Execution halted