Running ‘testthat.R’ [108s/134s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(pavo)
  Welcome to pavo 2! Take a look at the latest features (and update your bibliography) in our recent publication: Maia R., Gruson H., Endler J. A., White T. E. (2019) pavo 2: new tools for the spectral and spatial analysis of colour in R. Methods in Ecology and Evolution, 10, 1097-1107.
  > 
  > test_check("pavo")
  Colorspace & visual model options:
   * Colorspace: trispace 
   * Quantal catch: Qi 
   * Visual system, chromatic: cie10 
   * Visual system, achromatic: none 
   * Illuminant: ideal, scale = 1 (von Kries colour correction applied) 
   * Background: ideal 
   * Relative: TRUE 
   * Max possible chromatic volume: NA 
  
  Colorspace & visual model options:
   * Colorspace: tcs 
   * Quantal catch: Qi 
   * Visual system, chromatic: avg.uv 
   * Visual system, achromatic: none 
   * Illuminant: ideal, scale = 1 (von Kries colour correction not applied) 
   * Background: ideal 
   * Relative: TRUE 
   * Max possible chromatic volume: 0.215735 
  
  'avalue' automatically set to 1.8275e-01
  wavelengths found in column 1
  wavelengths found in column 1
  wavelengths found in column 1
  The spectral data contain 831 negative value(s),
   which may produce unexpected results if used in models.
  Consider using procspec() to correct them.
  wavelengths found in column 1
  The spectral data contain 1245 negative value(s),
   which may produce unexpected results if used in models.
  Consider using procspec() to correct them.
  wavelengths found in column 1
  The spectral data contain 426 negative value(s),
   which may produce unexpected results if used in models.
  Consider using procspec() to correct them.
  wavelengths found in column 1
  The spectral data contain 426 negative value(s),
   which may produce unexpected results if used in models.
  Consider using procspec() to correct them.
  wavelengths found in column 1
  The spectral data contain 633 negative value(s),
   which may produce unexpected results if used in models.
  Consider using procspec() to correct them.
  wavelengths found in column 1
  The spectral data contain 633 negative value(s),
   which may produce unexpected results if used in models.
  Consider using procspec() to correct them.
  The spectral data contain 628 negative value(s),
   which may produce unexpected results if used in models.
  Consider using procspec() to correct them.
  The spectral data contain 633 negative value(s),
   which may produce unexpected results if used in models.
  Consider using procspec() to correct them.
  The spectral data contain 422 negative value(s),
   which may produce unexpected results if used in models.
  Consider using procspec() to correct them.
  The spectral data contain 1245 negative value(s),
   which may produce unexpected results if used in models.
  Consider using procspec() to correct them.
  wavelengths found in column 1
  The spectral data contain 829 negative value(s),
   which may produce unexpected results if used in models.
  Consider using procspec() to correct them.
  wavelengths found in column 1
  The spectral data contain 829 negative value(s),
   which may produce unexpected results if used in models.
  Consider using procspec() to correct them.
  The spectral data contain 3 NA's(s), which should be reviewed closely.
  The spectral data contain 634 negative value(s),
   which may produce unexpected results if used in models.
  Consider using procspec() to correct them.
  Calculating noise-weighted Euclidean distances
  Quantum catch are relative, distances may not be meaningful
  Number of cones assumed to be 4
  Quantum catch are relative, distances may not be meaningful
  Quantum catch are relative, distances may not be meaningful
  Quantum catch are relative, distances may not be meaningful
  Calculating noise-weighted Euclidean distances and noise-weighted luminance contrasts
  Calculating noise-weighted Euclidean distances
  Calculating noise-weighted Euclidean distances
  Quantum catch are relative, distances may not be meaningful
  Calculating noise-weighted Euclidean distances
  Calculating noise-weighted Euclidean distances
  Calculating noise-weighted Euclidean distances
  Calculating noise-weighted Euclidean distances
  Calculating noise-weighted Euclidean distances
  Quantum catch are relative, distances may not be meaningful
  Calculating noise-weighted Euclidean distances
  Calculating noise-weighted Euclidean distances
  Quantum catch are relative, distances may not be meaningful
  Calculating noise-weighted Euclidean distances
  Quantum catch are relative, distances may not be meaningful
  Calculating noise-weighted Euclidean distances
  Calculating CIE2000 distances
  Calculating CIE2000 distances
  Calculating noise-weighted Euclidean distances and noise-weighted luminance contrasts
  Quantum catch are relative, distances may not be meaningful
  Calculating noise-weighted Euclidean distances
  Quantum catch are relative, distances may not be meaningful
  Calculating noise-weighted Euclidean distances
  Input is not a "vismodel" object and argument "qcatch" is undefined; assuming quantum catch are not transformed (i.e. qcatch = "Qi")
  Quantum catch are relative, distances may not be meaningful
  Calculating unweighted Euclidean distances
  Input is not a "vismodel" object and argument "qcatch" is undefined; assuming quantum catch are not transformed (i.e. qcatch = "Qi")
  Quantum catch are relative, distances may not be meaningful
  Calculating unweighted Euclidean distances
  Input is not a "vismodel" object and argument "qcatch" is undefined; assuming quantum catch are not transformed (i.e. qcatch = "Qi")
  Quantum catch are relative, distances may not be meaningful
  Calculating unweighted Euclidean distances
  Quantum catch are relative, distances may not be meaningful
  Calculating noise-weighted Euclidean distances and noise-weighted luminance contrasts
  Quantum catch are relative, distances may not be meaningful
  Calculating noise-weighted Euclidean distances and noise-weighted luminance contrasts
  Quantum catch are relative, distances may not be meaningful
  Calculating noise-weighted Euclidean distances and noise-weighted luminance contrasts
  Colorspace & visual model options:
   * Colorspace: tcs 
   * Quantal catch: Qi 
   * Visual system, chromatic: user-defined 
   * Visual system, achromatic: none 
   * Illuminant: ideal, scale = 1 (von Kries colour correction not applied) 
   * Background: ideal 
   * Relative: TRUE 
   * Max possible chromatic volume: 0.1294362 
  
  Input is not a "vismodel" object and argument "qcatch" is undefined; assuming quantum catch are not transformed (i.e. qcatch = "Qi")
  Input is not a "vismodel" object and argument "qcatch" is undefined; assuming quantum catch are not transformed (i.e. qcatch = "Qi")
  Input is not a "vismodel" object and argument "qcatch" is undefined; assuming quantum catch are not transformed (i.e. qcatch = "Qi")
  Input is not a "vismodel" object and argument "qcatch" is undefined; assuming quantum catch are not transformed (i.e. qcatch = "Qi")
  Input is not a "vismodel" object and argument "qcatch" is undefined; assuming quantum catch are not transformed (i.e. qcatch = "Qi")
  Using custom visual system to estimate cie neutral point
  Using custom illuminant to estimate cie neutral point
  Input is not a "vismodel" object and argument "qcatch" is undefined; assuming quantum catch are not transformed (i.e. qcatch = "Qi")
  Input is not a "vismodel" object and argument "qcatch" is undefined; assuming quantum catch are not transformed (i.e. qcatch = "Qi")
  Input is not a "vismodel" object and argument "qcatch" is undefined; assuming quantum catch are not transformed (i.e. qcatch = "Qi")
  Input is not a "vismodel" object and argument "qcatch" is undefined; assuming quantum catch are not transformed (i.e. qcatch = "Qi")
  Rescaling values to [0,1]
  Rescaling values to [0,1]
  Rescaling values to [0,1]
  Rescaling values to [0,1]
  Rescaling values to [0,1]
  Image classification in progress...
  Image classification in progress...
  Image classification in progress...
  Attempting to coerce image to class rimg.
  Attempting to coerce image to class rimg.
  Attempting to coerce image to class rimg.
  Attempting to coerce image to class rimg.
  Attempting to coerce image to class rimg.
  One or more images are not of class 'rimg'; attempting to coerce.
  Image classification in progress...
  One or more images are not of class 'rimg'; attempting to coerce.
  Image classification in progress...
  One or more images are not of class 'rimg'; attempting to coerce.
  Image classification in progress...
  Image classification in progress...
  One or more image matrices are not of class 'rimg'; attempting to coerce.
  Using single set of coldists for all images.
  Specified grid-sampling density exceeds dimensions of at least one image. Overwriting xpts to equal the smallest dimension in the image set.
  Using single set of coldists for all images.
  Using single set of hsl values for all images.
  Using single set of coldists for all images.
  Using single set of hsl values for all images.
  Image classification in progress...
  One or more image matrices are not of class 'rimg'; attempting to coerce.
  Specified grid-sampling density exceeds dimensions of at least one image. Overwriting xpts to equal the smallest dimension in the image set.
  Image classification in progress...
  Image classification in progress...
  Using single set of coldists for all images.
  Using single set of hsl values for all images.
  Image classification in progress...
  4 files found; importing spectra:
  1 files found; importing spectra:
  6 files found; importing spectra:
  5 files found; importing spectra:
  processing options applied:
  smoothing spectra with a span of 0.25
  processing options applied:
  smoothing spectra with a span of 0.1
  processing options applied:
  smoothing spectra with a span of 30
  processing options applied:
  smoothing spectra with a span of 0.1
  processing options applied:
  smoothing spectra with a span of 50
  processing options applied:
  binned spectra to 17-nm intervals
  processing options applied:
  binned spectra to 12-nm intervals
  processing options applied:
  Scaling spectra to a maximum value of 1
  processing options applied:
  Scaling spectra to a minimum value of zero
  processing options applied:
  Scaling spectra to a minimum value of zero
  Scaling spectra to a maximum value of 1
  processing options applied:
  Scaling spectra to a total area of 1
  processing options applied:
  Centering spectra to a mean of zero
  processing options applied:
  Negative value correction: converted negative values to zero
  processing options applied:
  Negative value correction: added min to all reflectance
  processing options applied:
  Scaling spectra to a minimum value of zero
  Scaling spectra to a maximum value of 1
  Scaling spectra to a total area of 1
  Centering spectra to a mean of zero
  binned spectra to 17-nm intervals
  The length of argument 'sensnames' does not equal the number of curves specified by 'peaksens'. Reverting to default names.
  wavelengths found in column 1
  The spectral data contain 425 negative value(s),
   which may produce unexpected results if used in models.
  Consider using procspec() to correct them.
  wavelengths found in column 1
  wavelengths found in column 1
  ══ Skipped tests ═══════════════════════════════════════════════════════════════
  • On CRAN (2)
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error (test-images.R:128:3): classify ───────────────────────────────────────
  Error in `FUN(X[[i]], ...)`: only defined on a data frame with all numeric-alike variables
  Backtrace:
      █
   1. └─pavo::classify(imgfakes, kcols = fake_IDs) test-images.R:128:2
   2.   └─base::Summary.data.frame(...)
   3.     └─base::lapply(...)
   4.       └─base:::FUN(X[[i]], ...)
  
  [ FAIL 1 | WARN 36 | SKIP 2 | PASS 369 ]
  Error: Test failures
  Execution halted