Running ‘testthat.R’ [73s/76s]
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/testing-design.html#sec-tests-files-overview
  > # * https://testthat.r-lib.org/articles/special-files.html
  > 
  > library(testthat)
  > library(ComBatFamQC)
  > 
  > test_check("ComBatFamQC")
  Loading required package: shiny
  GAMLSS-RS iteration 1: Global Deviance = 11577.26 
  GAMLSS-RS iteration 2: Global Deviance = 11577.26 
  GAMLSS-RS iteration 3: Global Deviance = 11577.26 
  GAMLSS-RS iteration 1: Global Deviance = 11582.7 
  GAMLSS-RS iteration 2: Global Deviance = 11582.68 
  GAMLSS-RS iteration 3: Global Deviance = 11582.68 
  [1] "Batch levels that contain less than 3 observations are dropped: <strong>no batch level is dropped</strong>."
  [1] "A <strong>noticeable deviation of the mean from zero</strong> in the additive-residual box plot indicates the presence of an additive batch effect"
  [1] "A <strong>substantial variation</strong> in the multiplicative-residual box plot demonstrates a potential multiplicative batch effect."
  [1] "eg: covariate1*covariate2,covariate3*covariate4 <br><br>"
  Starting data preparation for the batch effect diagnostic and harmonization stage...
  Taking the result from the visual preparation stage as input...
  No observation is dropped due to missing values.
  Starting Empirical Bayes assumption check...
  Starting data preparation for the batch effect diagnostic and harmonization stage...
  The result from the visual prepration stage is not provided! The required parameters should be specified...
  No observation is dropped due to missing values.
  Batch levels that contain less than 3 observations are dropped: no batch level is dropped.
  Starting Empirical Bayes assumption check...
  Starting data preparation for the batch effect diagnostic and harmonization stage...
  The result from the visual prepration stage is not provided! The required parameters should be specified...
  No observation is dropped due to missing values.
  Batch levels that contain less than 3 observations are dropped: no batch level is dropped.
  Starting first-time harmonization...
  Starting data preparation for the batch effect diagnostic and harmonization stage...
  No observation is dropped due to missing values.
  Batch levels that contain less than 3 observations are dropped: no batch level is dropped.
  Starting data preparation for the batch effect diagnostic and harmonization stage...
  The result from the visual prepration stage is not provided! The required parameters should be specified...
  No observation is dropped due to missing values.
  Batch levels that contain less than 3 observations are dropped: no batch level is dropped.
  Starting first-time harmonization...
  fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
  fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
  fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
  fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
  Starting data preparation for the batch effect diagnostic and harmonization stage...
  No observation is dropped due to missing values.
  Batch levels that contain less than 3 observations are dropped: no batch level is dropped.
  Starting data preparation for the batch effect diagnostic and harmonization stage...
  The result from the visual prepration stage is not provided! The required parameters should be specified...
  No observation is dropped due to missing values.
  Batch levels that contain less than 3 observations are dropped: no batch level is dropped.
  Starting first-time harmonization...
  Starting data preparation for the batch effect diagnostic and harmonization stage...
  No observation is dropped due to missing values.
  Batch levels that contain less than 3 observations are dropped: no batch level is dropped.
  Starting data preparation for the batch effect diagnostic and harmonization stage...
  The result from the visual prepration stage is not provided! The required parameters should be specified...
  No observation is dropped due to missing values.
  Batch levels that contain less than 3 observations are dropped: no batch level is dropped.
  Starting first-time harmonization...
  Starting data preparation for the batch effect diagnostic and harmonization stage...
  No observation is dropped due to missing values.
  Batch levels that contain less than 3 observations are dropped: no batch level is dropped.
  Starting out-of-sample harmonization using the saved ComBat Model...
  Starting data preparation for the batch effect diagnostic and harmonization stage...
  The result from the visual prepration stage is not provided! The required parameters should be specified...
  No observation is dropped due to missing values.
  Batch levels that contain less than 3 observations are dropped: no batch level is dropped.
  Starting first-time harmonization...
  fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
  fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
  fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
  fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
  Starting data preparation for the batch effect diagnostic and harmonization stage...
  The result from the visual prepration stage is not provided! The required parameters should be specified...
  No observation is dropped due to missing values.
  Batch levels that contain less than 3 observations are dropped: no batch level is dropped.
  Starting first-time harmonization...
  fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
  fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
  fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
  fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
  Starting data preparation for the batch effect diagnostic and harmonization stage...
  The result from the visual prepration stage is not provided! The required parameters should be specified...
  No observation is dropped due to missing values.
  Batch levels that contain less than 3 observations are dropped: 1 level(s) are dropped, corresponding to 2 observations.
  Starting out-of-sample harmonization using the reference dataset...
  Joining with `by = join_by(ID, site, subid, IMAGE_ID, VISIT, visit, EXAM_DATE, dateAcquired, timedays, timeyrs, AGE, baselineAge, SEX, DIAGNOSIS, MMSCORE, manufac, manufac.model, manufac.model.site, manufac.model.strength.site, manufac.model.coil.site, manufac.model.coil.strength.site, manufac.model.strength.site.indicator, manufac.model.coil.strength.site.indicator, strength, Field_Strength, Manufacturer, Mfg_Model, Weighting, Pulse_Sequence, Slice_Thickness, TE, TR, TI, Coil, Flip_Angle, Acquisition_Plane, Matrix_X, Matrix_Y, Matrix_Z, Pixel_Spacing_X, Pixel_Spacing_Y, X, thickness.left.fusiform, thickness.left.inferior.parietal, thickness.left.inferior.temporal, thickness.left.isthmus.cingulate, thickness.left.lateral.occipital, thickness.left.lateral.orbitofrontal, thickness.left.lingual, thickness.left.medial.orbitofrontal, thickness.left.middle.temporal, thickness.left.parahippocampal, thickness.left.paracentral, thickness.left.pars.opercularis, thickness.left.pars.orbitalis, thickness.left.pars.triangularis, thickness.left.pericalcarine, thickness.left.postcentral, thickness.left.posterior.cingulate, thickness.left.precentral, thickness.left.precuneus, thickness.left.rostral.anterior.cingulate, thickness.left.rostral.middle.frontal, thickness.left.superior.frontal, thickness.left.superior.parietal, thickness.left.superior.temporal, thickness.left.supramarginal, thickness.left.transverse.temporal, thickness.left.insula, thickness.right.caudal.anterior.cingulate, thickness.right.caudal.middle.frontal, thickness.right.cuneus, thickness.right.entorhinal, thickness.right.fusiform, thickness.right.inferior.parietal, thickness.right.inferior.temporal, thickness.right.isthmus.cingulate, thickness.right.lateral.occipital, thickness.right.lateral.orbitofrontal, thickness.right.lingual, thickness.right.medial.orbitofrontal, thickness.right.middle.temporal, thickness.right.parahippocampal, thickness.right.paracentral, thickness.right.pars.opercularis, thickness.right.pars.orbitalis, thickness.right.pars.triangularis, thickness.right.pericalcarine, thickness.right.postcentral, thickness.right.posterior.cingulate, thickness.right.precentral, thickness.right.precuneus, thickness.right.rostral.anterior.cingulate, thickness.right.rostral.middle.frontal, thickness.right.superior.frontal, thickness.right.superior.parietal, thickness.right.superior.temporal, thickness.right.supramarginal, thickness.right.transverse.temporal, thickness.right.insula)`
  The reference data is included in the new unharmonized dataset
  Joining with `by = join_by(ID, site, subid, IMAGE_ID, VISIT, visit, EXAM_DATE, dateAcquired, timedays, timeyrs, AGE, baselineAge, SEX, DIAGNOSIS, MMSCORE, manufac, manufac.model, manufac.model.site, manufac.model.strength.site, manufac.model.coil.site, manufac.model.coil.strength.site, manufac.model.strength.site.indicator, manufac.model.coil.strength.site.indicator, strength, Field_Strength, Manufacturer, Mfg_Model, Weighting, Pulse_Sequence, Slice_Thickness, TE, TR, TI, Coil, Flip_Angle, Acquisition_Plane, Matrix_X, Matrix_Y, Matrix_Z, Pixel_Spacing_X, Pixel_Spacing_Y, X, thickness.left.fusiform, thickness.left.inferior.parietal, thickness.left.inferior.temporal, thickness.left.isthmus.cingulate, thickness.left.lateral.occipital, thickness.left.lateral.orbitofrontal, thickness.left.lingual, thickness.left.medial.orbitofrontal, thickness.left.middle.temporal, thickness.left.parahippocampal, thickness.left.paracentral, thickness.left.pars.opercularis, thickness.left.pars.orbitalis, thickness.left.pars.triangularis, thickness.left.pericalcarine, thickness.left.postcentral, thickness.left.posterior.cingulate, thickness.left.precentral, thickness.left.precuneus, thickness.left.rostral.anterior.cingulate, thickness.left.rostral.middle.frontal, thickness.left.superior.frontal, thickness.left.superior.parietal, thickness.left.superior.temporal, thickness.left.supramarginal, thickness.left.transverse.temporal, thickness.left.insula, thickness.right.caudal.anterior.cingulate, thickness.right.caudal.middle.frontal, thickness.right.cuneus, thickness.right.entorhinal, thickness.right.fusiform, thickness.right.inferior.parietal, thickness.right.inferior.temporal, thickness.right.isthmus.cingulate, thickness.right.lateral.occipital, thickness.right.lateral.orbitofrontal, thickness.right.lingual, thickness.right.medial.orbitofrontal, thickness.right.middle.temporal, thickness.right.parahippocampal, thickness.right.paracentral, thickness.right.pars.opercularis, thickness.right.pars.orbitalis, thickness.right.pars.triangularis, thickness.right.pericalcarine, thickness.right.postcentral, thickness.right.posterior.cingulate, thickness.right.precentral, thickness.right.precuneus, thickness.right.rostral.anterior.cingulate, thickness.right.rostral.middle.frontal, thickness.right.superior.frontal, thickness.right.superior.parietal, thickness.right.superior.temporal, thickness.right.supramarginal, thickness.right.transverse.temporal, thickness.right.insula)`
  Joining with `by = join_by(ID, site, subid, IMAGE_ID, VISIT, visit, EXAM_DATE, dateAcquired, timedays, timeyrs, AGE, baselineAge, SEX, DIAGNOSIS, MMSCORE, manufac, manufac.model, manufac.model.site, manufac.model.strength.site, manufac.model.coil.site, manufac.model.coil.strength.site, manufac.model.strength.site.indicator, manufac.model.coil.strength.site.indicator, strength, Field_Strength, Manufacturer, Mfg_Model, Weighting, Pulse_Sequence, Slice_Thickness, TE, TR, TI, Coil, Flip_Angle, Acquisition_Plane, Matrix_X, Matrix_Y, Matrix_Z, Pixel_Spacing_X, Pixel_Spacing_Y, X, thickness.left.fusiform, thickness.left.inferior.parietal, thickness.left.inferior.temporal, thickness.left.isthmus.cingulate, thickness.left.lateral.occipital, thickness.left.lateral.orbitofrontal, thickness.left.lingual, thickness.left.medial.orbitofrontal, thickness.left.middle.temporal, thickness.left.parahippocampal, thickness.left.paracentral, thickness.left.pars.opercularis, thickness.left.pars.orbitalis, thickness.left.pars.triangularis, thickness.left.pericalcarine, thickness.left.postcentral, thickness.left.posterior.cingulate, thickness.left.precentral, thickness.left.precuneus, thickness.left.rostral.anterior.cingulate, thickness.left.rostral.middle.frontal, thickness.left.superior.frontal, thickness.left.superior.parietal, thickness.left.superior.temporal, thickness.left.supramarginal, thickness.left.transverse.temporal, thickness.left.insula, thickness.right.caudal.anterior.cingulate, thickness.right.caudal.middle.frontal, thickness.right.cuneus, thickness.right.entorhinal, thickness.right.fusiform, thickness.right.inferior.parietal, thickness.right.inferior.temporal, thickness.right.isthmus.cingulate, thickness.right.lateral.occipital, thickness.right.lateral.orbitofrontal, thickness.right.lingual, thickness.right.medial.orbitofrontal, thickness.right.middle.temporal, thickness.right.parahippocampal, thickness.right.paracentral, thickness.right.pars.opercularis, thickness.right.pars.orbitalis, thickness.right.pars.triangularis, thickness.right.pericalcarine, thickness.right.postcentral, thickness.right.posterior.cingulate, thickness.right.precentral, thickness.right.precuneus, thickness.right.rostral.anterior.cingulate, thickness.right.rostral.middle.frontal, thickness.right.superior.frontal, thickness.right.superior.parietal, thickness.right.superior.temporal, thickness.right.supramarginal, thickness.right.transverse.temporal, thickness.right.insula)`
  fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
  fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
  fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
  fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
  Starting data preparation for the batch effect diagnostic and harmonization stage...
  The result from the visual prepration stage is not provided! The required parameters should be specified...
  No observation is dropped due to missing values.
  Batch levels that contain less than 3 observations are dropped: 1 level(s) are dropped, corresponding to 2 observations.
  Starting out-of-sample harmonization using the reference dataset...
  Joining with `by = join_by(ID, site, subid, IMAGE_ID, VISIT, visit, EXAM_DATE, dateAcquired, timedays, timeyrs, AGE, baselineAge, SEX, DIAGNOSIS, MMSCORE, manufac, manufac.model, manufac.model.site, manufac.model.strength.site, manufac.model.coil.site, manufac.model.coil.strength.site, manufac.model.strength.site.indicator, manufac.model.coil.strength.site.indicator, strength, Field_Strength, Manufacturer, Mfg_Model, Weighting, Pulse_Sequence, Slice_Thickness, TE, TR, TI, Coil, Flip_Angle, Acquisition_Plane, Matrix_X, Matrix_Y, Matrix_Z, Pixel_Spacing_X, Pixel_Spacing_Y, X, thickness.left.fusiform, thickness.left.inferior.parietal, thickness.left.inferior.temporal, thickness.left.isthmus.cingulate, thickness.left.lateral.occipital, thickness.left.lateral.orbitofrontal, thickness.left.lingual, thickness.left.medial.orbitofrontal, thickness.left.middle.temporal, thickness.left.parahippocampal, thickness.left.paracentral, thickness.left.pars.opercularis, thickness.left.pars.orbitalis, thickness.left.pars.triangularis, thickness.left.pericalcarine, thickness.left.postcentral, thickness.left.posterior.cingulate, thickness.left.precentral, thickness.left.precuneus, thickness.left.rostral.anterior.cingulate, thickness.left.rostral.middle.frontal, thickness.left.superior.frontal, thickness.left.superior.parietal, thickness.left.superior.temporal, thickness.left.supramarginal, thickness.left.transverse.temporal, thickness.left.insula, thickness.right.caudal.anterior.cingulate, thickness.right.caudal.middle.frontal, thickness.right.cuneus, thickness.right.entorhinal, thickness.right.fusiform, thickness.right.inferior.parietal, thickness.right.inferior.temporal, thickness.right.isthmus.cingulate, thickness.right.lateral.occipital, thickness.right.lateral.orbitofrontal, thickness.right.lingual, thickness.right.medial.orbitofrontal, thickness.right.middle.temporal, thickness.right.parahippocampal, thickness.right.paracentral, thickness.right.pars.opercularis, thickness.right.pars.orbitalis, thickness.right.pars.triangularis, thickness.right.pericalcarine, thickness.right.postcentral, thickness.right.posterior.cingulate, thickness.right.precentral, thickness.right.precuneus, thickness.right.rostral.anterior.cingulate, thickness.right.rostral.middle.frontal, thickness.right.superior.frontal, thickness.right.superior.parietal, thickness.right.superior.temporal, thickness.right.supramarginal, thickness.right.transverse.temporal, thickness.right.insula)`
  The reference data is included in the new unharmonized dataset
  Joining with `by = join_by(ID, site, subid, IMAGE_ID, VISIT, visit, EXAM_DATE, dateAcquired, timedays, timeyrs, AGE, baselineAge, SEX, DIAGNOSIS, MMSCORE, manufac, manufac.model, manufac.model.site, manufac.model.strength.site, manufac.model.coil.site, manufac.model.coil.strength.site, manufac.model.strength.site.indicator, manufac.model.coil.strength.site.indicator, strength, Field_Strength, Manufacturer, Mfg_Model, Weighting, Pulse_Sequence, Slice_Thickness, TE, TR, TI, Coil, Flip_Angle, Acquisition_Plane, Matrix_X, Matrix_Y, Matrix_Z, Pixel_Spacing_X, Pixel_Spacing_Y, X, thickness.left.fusiform, thickness.left.inferior.parietal, thickness.left.inferior.temporal, thickness.left.isthmus.cingulate, thickness.left.lateral.occipital, thickness.left.lateral.orbitofrontal, thickness.left.lingual, thickness.left.medial.orbitofrontal, thickness.left.middle.temporal, thickness.left.parahippocampal, thickness.left.paracentral, thickness.left.pars.opercularis, thickness.left.pars.orbitalis, thickness.left.pars.triangularis, thickness.left.pericalcarine, thickness.left.postcentral, thickness.left.posterior.cingulate, thickness.left.precentral, thickness.left.precuneus, thickness.left.rostral.anterior.cingulate, thickness.left.rostral.middle.frontal, thickness.left.superior.frontal, thickness.left.superior.parietal, thickness.left.superior.temporal, thickness.left.supramarginal, thickness.left.transverse.temporal, thickness.left.insula, thickness.right.caudal.anterior.cingulate, thickness.right.caudal.middle.frontal, thickness.right.cuneus, thickness.right.entorhinal, thickness.right.fusiform, thickness.right.inferior.parietal, thickness.right.inferior.temporal, thickness.right.isthmus.cingulate, thickness.right.lateral.occipital, thickness.right.lateral.orbitofrontal, thickness.right.lingual, thickness.right.medial.orbitofrontal, thickness.right.middle.temporal, thickness.right.parahippocampal, thickness.right.paracentral, thickness.right.pars.opercularis, thickness.right.pars.orbitalis, thickness.right.pars.triangularis, thickness.right.pericalcarine, thickness.right.postcentral, thickness.right.posterior.cingulate, thickness.right.precentral, thickness.right.precuneus, thickness.right.rostral.anterior.cingulate, thickness.right.rostral.middle.frontal, thickness.right.superior.frontal, thickness.right.superior.parietal, thickness.right.superior.temporal, thickness.right.supramarginal, thickness.right.transverse.temporal, thickness.right.insula)`
  Joining with `by = join_by(ID, site, subid, IMAGE_ID, VISIT, visit, EXAM_DATE, dateAcquired, timedays, timeyrs, AGE, baselineAge, SEX, DIAGNOSIS, MMSCORE, manufac, manufac.model, manufac.model.site, manufac.model.strength.site, manufac.model.coil.site, manufac.model.coil.strength.site, manufac.model.strength.site.indicator, manufac.model.coil.strength.site.indicator, strength, Field_Strength, Manufacturer, Mfg_Model, Weighting, Pulse_Sequence, Slice_Thickness, TE, TR, TI, Coil, Flip_Angle, Acquisition_Plane, Matrix_X, Matrix_Y, Matrix_Z, Pixel_Spacing_X, Pixel_Spacing_Y, X, thickness.left.fusiform, thickness.left.inferior.parietal, thickness.left.inferior.temporal, thickness.left.isthmus.cingulate, thickness.left.lateral.occipital, thickness.left.lateral.orbitofrontal, thickness.left.lingual, thickness.left.medial.orbitofrontal, thickness.left.middle.temporal, thickness.left.parahippocampal, thickness.left.paracentral, thickness.left.pars.opercularis, thickness.left.pars.orbitalis, thickness.left.pars.triangularis, thickness.left.pericalcarine, thickness.left.postcentral, thickness.left.posterior.cingulate, thickness.left.precentral, thickness.left.precuneus, thickness.left.rostral.anterior.cingulate, thickness.left.rostral.middle.frontal, thickness.left.superior.frontal, thickness.left.superior.parietal, thickness.left.superior.temporal, thickness.left.supramarginal, thickness.left.transverse.temporal, thickness.left.insula, thickness.right.caudal.anterior.cingulate, thickness.right.caudal.middle.frontal, thickness.right.cuneus, thickness.right.entorhinal, thickness.right.fusiform, thickness.right.inferior.parietal, thickness.right.inferior.temporal, thickness.right.isthmus.cingulate, thickness.right.lateral.occipital, thickness.right.lateral.orbitofrontal, thickness.right.lingual, thickness.right.medial.orbitofrontal, thickness.right.middle.temporal, thickness.right.parahippocampal, thickness.right.paracentral, thickness.right.pars.opercularis, thickness.right.pars.orbitalis, thickness.right.pars.triangularis, thickness.right.pericalcarine, thickness.right.postcentral, thickness.right.posterior.cingulate, thickness.right.precentral, thickness.right.precuneus, thickness.right.rostral.anterior.cingulate, thickness.right.rostral.middle.frontal, thickness.right.superior.frontal, thickness.right.superior.parietal, thickness.right.superior.temporal, thickness.right.supramarginal, thickness.right.transverse.temporal, thickness.right.insula)`
  fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
  fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
  fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
  fixed-effect model matrix is rank deficient so dropping 1 column / coefficient
  Starting data preparation for the batch effect diagnostic and harmonization stage...
  The result from the visual prepration stage is not provided! The required parameters should be specified...
  No observation is dropped due to missing values.
  Batch levels that contain less than 3 observations are dropped: no batch level is dropped.
  Starting data preparation for the batch effect diagnostic and harmonization stage...
  The result from the visual prepration stage is not provided! The required parameters should be specified...
  No observation is dropped due to missing values.
  Batch levels that contain less than 3 observations are dropped: no batch level is dropped.
  Starting data preparation for the batch effect diagnostic and harmonization stage...
  The result from the visual prepration stage is not provided! The required parameters should be specified...
  No observation is dropped due to missing values.
  Batch levels that contain less than 3 observations are dropped: no batch level is dropped.
  Starting data preparation for the batch effect diagnostic and harmonization stage...
  Taking the result from the visual preparation stage as input...
  No observation is dropped due to missing values.
  Starting data preparation for the post-harmonization stage...
  No existing model is provided. Fitting the regression model from scratch!
  No observation is dropped due to missing values.
  Starting data preparation for the post-harmonization stage...
  No existing model is provided. Fitting the regression model from scratch!
  No observation is dropped due to missing values.
  Starting data preparation for the post-harmonization stage...
  No existing model is provided. Fitting the regression model from scratch!
  No observation is dropped due to missing values.
  Starting data preparation for the post-harmonization stage...
  No observation is dropped due to missing values.
  Starting data preparation for the batch effect diagnostic and harmonization stage...
  The result from the visual prepration stage is not provided! The required parameters should be specified...
  No observation is dropped due to missing values.
  Batch levels that contain less than 3 observations are dropped: no batch level is dropped.
  Starting first-time harmonization...
  Starting data preparation for the post-harmonization stage...
  No existing model is provided. Fitting the regression model from scratch!
  No observation is dropped due to missing values.
  New names:
  * `residual_y` -> `residual_y...1`
  * `residual_y` -> `residual_y...2`
  * `residual_y` -> `residual_y...3`
  * `residual_y` -> `residual_y...4`
  Starting data preparation for the post-harmonization stage...
  No existing model is provided. Fitting the regression model from scratch!
  No observation is dropped due to missing values.
  New names:
  * `X3` -> `X3...1`
  * `X3` -> `X3...2`
  * `X3` -> `X3...3`
  * `X3` -> `X3...4`
  Starting data preparation for the post-harmonization stage...
  No observation is dropped due to missing values.
  Starting data preparation for the post-harmonization stage...
  No existing model is provided. Fitting the regression model from scratch!
  No observation is dropped due to missing values.
  New names:
  * `residual_y` -> `residual_y...1`
  * `residual_y` -> `residual_y...2`
  * `residual_y` -> `residual_y...3`
  * `residual_y` -> `residual_y...4`
  Starting data preparation for the post-harmonization stage...
  No observation is dropped due to missing values.
  Starting data preparation for the post-harmonization stage...
  No observation is dropped due to missing values.
  New names:
  * `residual_y` -> `residual_y...1`
  * `residual_y` -> `residual_y...2`
  * `residual_y` -> `residual_y...3`
  * `residual_y` -> `residual_y...4`
  Starting data preparation for the post-harmonization stage...
  No existing model is provided. Fitting the regression model from scratch!
  No observation is dropped due to missing values.
  New names:
  * `residual_y` -> `residual_y...1`
  * `residual_y` -> `residual_y...2`
  * `residual_y` -> `residual_y...3`
  * `residual_y` -> `residual_y...4`
  Starting data preparation for the batch effect diagnostic and harmonization stage...
  The result from the visual prepration stage is not provided! The required parameters should be specified...
  No observation is dropped due to missing values.
  Batch levels that contain less than 3 observations are dropped: no batch level is dropped.
            Statistic Numer.DF Pseudo.R2 Analytic.p.value    
  (Omnibus)    0.0495        2    0.0472          < 1e-20 ***
  manufacs     0.0495        2    0.0472          < 1e-20 ***
  ---
  Signif. codes:  0 "***" 0.001 "**" 0.01 "*" 0.05 "." 0.1 " " 1Starting data preparation for the batch effect diagnostic and harmonization stage...
  The result from the visual prepration stage is not provided! The required parameters should be specified...
  No observation is dropped due to missing values.
  Batch levels that contain less than 3 observations are dropped: no batch level is dropped.
            Statistic Numer.DF Pseudo.R2 Analytic.p.value    
  (Omnibus)     0.363        2     0.266          < 1e-20 ***
  manufacs      0.363        2     0.266          < 1e-20 ***
  ---
  Signif. codes:  0 "***" 0.001 "**" 0.01 "*" 0.05 "." 0.1 " " 1refitting model(s) with ML (instead of REML)
  refitting model(s) with ML (instead of REML)
  refitting model(s) with ML (instead of REML)
  refitting model(s) with ML (instead of REML)
  Starting data preparation for the batch effect diagnostic and harmonization stage...
  The result from the visual prepration stage is not provided! The required parameters should be specified...
  No observation is dropped due to missing values.
  Batch levels that contain less than 3 observations are dropped: no batch level is dropped.
            Statistic Numer.DF Pseudo.R2 Analytic.p.value    
  (Omnibus)    0.0534        2    0.0507          < 1e-20 ***
  manufacs     0.0534        2    0.0507          < 1e-20 ***
  ---
  Signif. codes:  0 "***" 0.001 "**" 0.01 "*" 0.05 "." 0.1 " " 1[ FAIL 1 | WARN 101 | SKIP 0 | PASS 232 ]
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-comfam_shiny.R:49:5'): comfam_shiny server logic works correctly ──
  <packageNotFoundError/error/condition>
  Error in `loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]])`: there is no package called 'systemfonts'
  Backtrace:
       ▆
    1. ├─shiny::testServer(...) at test-comfam_shiny.R:4:3
    2. │ ├─shiny:::withMockContext(...)
    3. │ │ ├─shiny::isolate(...)
    4. │ │ │ ├─shiny::..stacktraceoff..(...)
    5. │ │ │ └─ctx$run(...)
    6. │ │ │   ├─promises::with_promise_domain(...)
    7. │ │ │   │ └─domain$wrapSync(expr)
    8. │ │ │   ├─shiny::withReactiveDomain(...)
    9. │ │ │   │ └─promises::with_promise_domain(...)
   10. │ │ │   │   └─domain$wrapSync(expr)
   11. │ │ │   │     └─base::force(expr)
   12. │ │ │   ├─shiny::captureStackTraces(...)
   13. │ │ │   │ └─promises::with_promise_domain(...)
   14. │ │ │   │   └─domain$wrapSync(expr)
   15. │ │ │   │     └─base::withCallingHandlers(expr, error = doCaptureStack)
   16. │ │ │   └─env$runWith(self, func)
   17. │ │ │     └─shiny (local) contextFunc()
   18. │ │ │       └─shiny::..stacktraceon..(expr)
   19. │ │ ├─shiny::withReactiveDomain(...)
   20. │ │ │ └─promises::with_promise_domain(...)
   21. │ │ │   └─domain$wrapSync(expr)
   22. │ │ │     └─base::force(expr)
   23. │ │ └─withr::with_options(...)
   24. │ │   └─base::force(code)
   25. │ └─rlang::eval_tidy(quosure, mask, rlang::caller_env())
   26. ├─testthat::expect_true(!is.null(output$pca)) at test-comfam_shiny.R:49:5
   27. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
   28. │   └─rlang::eval_bare(expr, quo_get_env(quo))
   29. ├─output$pca
   30. ├─shiny:::`$.shinyoutput`(output, pca)
   31. │ └─.subset2(x, "impl")$getOutput(name)
   32. │   └─base::stop(v$err)
   33. └─shiny (local) `<fn>`(`<pckgNtFE>`)
  
  [ FAIL 1 | WARN 101 | SKIP 0 | PASS 232 ]
  Error: Test failures
  Execution halted