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### Running command:
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###   /home/biocbuild/bbs-3.14-bioc/R/bin/R CMD build --keep-empty-dirs --no-resave-data sparseDOSSA
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* checking for file ‘sparseDOSSA/DESCRIPTION’ ... OK
* preparing ‘sparseDOSSA’:
* checking DESCRIPTION meta-information ... OK
* installing the package to build vignettes
* creating vignettes ... ERROR
--- re-building ‘sparsedossa-vignette.Rmd’ using rmarkdown
sparseDOSSA            package:sparseDOSSA             R Documentation

_S_p_a_r_s_e _D_a_t_a _O_b_s_e_r_v_a_t_i_o_n_s _f_o_r _S_i_m_u_l_a_t_i_n_g _S_y_n_t_h_e_t_i_c _A_b_u_n_d_a_n_c_e

_D_e_s_c_r_i_p_t_i_o_n:

     Sparse Data Observations for Simulating Synthetic Abundance

_U_s_a_g_e:

     sparseDOSSA( strNormalizedFileName = "SyntheticMicrobiome.pcl",
                  strCountFileName = "SyntheticMicrobiome-Counts.pcl",
                  parameter_filename = "SyntheticMicrobiomeParameterFile.txt",
                  bugs_to_spike = 0,
                  spikeFile = NA,
                  calibrate = NA,
                  datasetCount = 1,
                  read_depth = 8030,
                  number_features = 300,
                  bugBugCorr =  "0.5",
                  spikeCount = "1",
                  lefse_file = NULL,
                  percent_spiked = 0.03,
                  minLevelPercent = 0.1,
                  number_samples = 50,
                  max_percent_outliers = 0.05,
                  number_metadata = 5,
                  spikeStrength = "1.0",
                  seed =  NA,
                  percent_outlier_spikins = 0.05,
                  minOccurence =  0,
                  verbose =  TRUE,
                  minSample =  0,
                  scalePercentZeros = 1,
                  association_type =  "linear",
                  noZeroInflate =  FALSE,
                  noRunMetadata = FALSE, 
                  runBugBug =  FALSE )
     
_A_r_g_u_m_e_n_t_s:

strNormalizedFileName: This output file records the synthetic
          microbiome data for null community (no spike-in and
          outliers), outlier-added community without spike-in and final
          spiked data. We put samples in columns and features in rows.
          The first chunk of the file is metadata, with row names
          Metadata_. The second chunk is for null community, with row
          names Feature_Lognormal_. The third chunk is for
          outlier-introduced community, with row names
          Feature_Outlier_*. The last chunk is for spiked data, with
          row names Feature_spike. This file records relative abundance
          data.

strCountFileName: This output file has the same organization as the
          file strNormalizedFileName but records raw counts data.

parameter_filename: This output file records diagnostic information and
          values of model parameters as well as the spike-in
          assignment. The most part of this file is used only for
          debugging. Users can focus on lines after Minimum Spiked-in
          Samples. Those lines record which metadata are correlated
          with which feature. The format is all metadata that are
          correlated with a specific features are listed under the name
          of the feature.

bugs_to_spike: Number of bugs to correlate with others. A non-negative
          integer value is expected.

spikeFile: The name of the file where the correlation values are
          stored. Should have fields `Domain`, `Range`, and
          `Correlation`.

calibrate: Calibration file for generating the random log normal data.
          TSV file (column = feature).

datasetCount: The number of bug-bug spiked datasets to generate.  A
          positive integer value is expected.

read_depth: Simulated read depth for counts. A positive integer value
          is expected.

number_features: The number of features per sample to create. A
          positive integer value is expected.

bugBugCorr: A vector of string separated values for the correlation
          values of the pairwise bug-bug associations. This is the
          correlation of the log-counts. Values are comma-separated;
          for example: 0.7,0.5. Default is 0.5.

spikeCount: Counts of spiked metadata used in the spike-in dataset -
          These values should be comma delimited values, in the order
          of the spikeStrength values (if given), Can be one value, in
          this case the value will be repeated to pair with the
          spikeCount values (if multiple are present). For example
          1,2,3.

lefse_file: Folder containing lefSe inputs.

percent_spiked: The percent of features spiked-in. A real number
          between 0 and 1 is expected.

minLevelPercent: Minimum percent of measurements out of the total a
          level can have in a discontinuous metadata (rounded up to the
          nearest count). A real number between 0 and 1 is expected.

number_samples: The number of samples to generate. A positive integer
          greater than 0 is expected.

max_percent_outliers: The maximum percent of outliers to spike into a
          sample. A real number between 0 and 1 is expected.

number_metadata: Indicates how many metadata are created,
          number_metadata*2 = number continuous metadata,
          number_metadata = number binary metadata, number_metadata =
          number quaternary metadata, A positive integer greater than 0
          is expected.

spikeStrength: Strength of the metadata association with the spiked-in
          feature, These values should be comma delimited and in the
          order of the spikeCount values (if given),Can be one value,
          in this case the value will be repeated to pair with the
          spikeStrength values (if multiple are present). For example
          0.2,0.3,0.4.

    seed: A seed to freeze the random generation of counts/relative
          abundance,If left as default (NA), generation is random - If
          seeded, data generation will be random within a run but
          identical if ran again under the same settings,an integer is
          expected.

percent_outlier_spikins: The percent of samples to spike in outliers. A
          real number between 0 to 1 is expected.

minOccurence: Minimum counts a bug can have for the occurrence quality
          control filter used when creating bugs (filtering minimum
          number of counts in a minimum number of samples).  A positive
          integer is expected.

 verbose: If True logging and plotting is made by the underlying
          methodology.  This is a flag, it is either included or not
          included in the command line, no value needed.

minSample: Minimum samples a bug can be in for the occurrence quality
          control filter used when creating bugs (filtering minimum
          number of counts in a minimum number of samples). A positive
          integer is expected.

scalePercentZeros: A scale used to multiply the percent zeros of all
          features across the sample after it is derived from the
          relationships with it and the feature abundance or
          calibration file. Requires a number greater than 0. A number
          greater than 1 increases sparsity, a number less than 1
          decreases sparsity, O removes sparsity, 1 (default) does not
          change the value and the value.

association_type: The type of association to generate. Options are
          'linear' or 'rounded_linear'.

noZeroInflate: If given, zero inflation is not used when generating a
          feature. This is a flag, it is either included or not
          included in the command line, no value needed.

noRunMetadata: If given, no metadata files are generated, This is a
          flag, it is either included or not included in the command
          line, no value needed.

runBugBug: If given, bug-bug interaction files are generated in
          addition to any metadata files. This is a flag, it is either
          included or not included in the command line, no value
          needed.

_V_a_l_u_e:

     A list contains the names of the output files.

_A_u_t_h_o_r(_s):

     Boyu Ren<bor158@mail.harvard.edu>, Emma
     Schwager<eschwager@hsph.harvard.edu>, Timothy
     Tickle<ttickle@hsph.harvard.edu>, Curtis Huttenhower
     <chuttenh@hsph.harvard.edu>

_E_x_a_m_p_l_e_s:

     sparseDOSSA(strNormalizedFileName = "SyntheticMicrobiome.pcl",
             strCountFileName = "SyntheticMicrobiome-Counts.pcl",
             parameter_filename = "SyntheticMicrobiomeParameterFile.txt",
             bugs_to_spike = 0,
             calibrate = NA,
             datasetCount = 1,
             read_depth = 8030,
             number_features = 300,
             spikeCount = "1",
             lefse_file = NA,
             percent_spiked = 0.03,
             minLevelPercent =  0.1,
             number_samples = 50, 
             max_percent_outliers = 0.05,
             number_metadata = 5,
             spikeStrength =  "1.0",
             seed =  1,
             percent_outlier_spikins = 0.05,
             minOccurence =  0,
             verbose =  TRUE,
             minSample =  0,
             association_type =  "linear",
             noZeroInflate =  FALSE,
             noRunMetadata = FALSE,
             runBugBug =  FALSE)
     

Warning in sparseDOSSA::sparseDOSSA() :
  number of associations = 0, and no spike file specified; no bug-bug spike-ins will be done.
Parameters BEFORE Calibration File
Length exp NA Length vdMu NA length vdSD NA length vdPercentZero NA Read depth 8030
Parameters AFTER Calibration File (if no calibration file is used, defaults are shown)
Length exp 1 Length vdMu 1 length vdSD 1 length vdPercentZero 1 Read depth 8030 Feature Count 300
func_generate_random_lognormal_matrix START
start funcGenerateFeatureParameters
funcGenerateFeatureParameters: Generating vdExp Vector.
LogMu 2.39794758911363 LogSD 1.33357395233333 Threshold 26.178898826878
funcGenerateFeatureParameters: Generating vdSD Vector.
funcGenerateFeatureParameters: Generating vdMu Vector.
funcGenerateFeatureParameters: Generating vdPercentZero Vector.
***Scale***
1
***vdPercentZero***
0.381001612630270.5110492804597510.6158370514574770.6378085929853660.7496316601717770.971922394681468
stop funcGenerateFeatureParameters
func_generate_random_lognormal_matrix: START Making features
Start funcShuffleMatrix
stop func_generate_random_lognormal_matrix
start func_generate_random_lognormal_with_outliers
Stop func_generate_random_lognormal_with_outliers
start func_generate_random_lognormal_with_multivariate_spikes
stop func_generate_random_lognormal_with_multivariate_spikes
Warning in sparseDOSSA::sparseDOSSA(number_features = n.microbes, number_samples = n.samples,  :
  number of associations = 0, and no spike file specified; no bug-bug spike-ins will be done.
Parameters BEFORE Calibration File
Length exp NA Length vdMu NA length vdSD NA length vdPercentZero NA Read depth 8030
Parameters AFTER Calibration File (if no calibration file is used, defaults are shown)
Length exp 1 Length vdMu 1 length vdSD 1 length vdPercentZero 1 Read depth 8030 Feature Count 150
func_generate_random_lognormal_matrix START
start funcGenerateFeatureParameters
funcGenerateFeatureParameters: Generating vdExp Vector.
LogMu 0.423465540160656 LogSD 2.66714790466667 Threshold 59.1226199198229
funcGenerateFeatureParameters: Generating vdSD Vector.
funcGenerateFeatureParameters: Changing low SDs to a little more than 0. # occurences =  17
funcGenerateFeatureParameters: Generating vdMu Vector.
funcGenerateFeatureParameters: Generating vdPercentZero Vector.
***Scale***
1
***vdPercentZero***
0.1578260789197270.5924273440414080.9109121177078920.7750377328571650.9975385701217940.999995440684144
stop funcGenerateFeatureParameters
func_generate_random_lognormal_matrix: START Making features
Start funcShuffleMatrix
stop func_generate_random_lognormal_matrix
start func_generate_random_lognormal_with_outliers
Stop func_generate_random_lognormal_with_outliers
start func_generate_random_lognormal_with_multivariate_spikes
stop func_generate_random_lognormal_with_multivariate_spikes
Warning in sparseDOSSA::sparseDOSSA(spikeStrength = "2.0", spikeCount = "2") :
  number of associations = 0, and no spike file specified; no bug-bug spike-ins will be done.
Parameters BEFORE Calibration File
Length exp NA Length vdMu NA length vdSD NA length vdPercentZero NA Read depth 8030
Parameters AFTER Calibration File (if no calibration file is used, defaults are shown)
Length exp 1 Length vdMu 1 length vdSD 1 length vdPercentZero 1 Read depth 8030 Feature Count 300
func_generate_random_lognormal_matrix START
start funcGenerateFeatureParameters
funcGenerateFeatureParameters: Generating vdExp Vector.
LogMu 2.39794758911363 LogSD 1.33357395233333 Threshold 26.178898826878
funcGenerateFeatureParameters: Generating vdSD Vector.
funcGenerateFeatureParameters: Generating vdMu Vector.
funcGenerateFeatureParameters: Generating vdPercentZero Vector.
***Scale***
1
***vdPercentZero***
0.3746673573278570.5048328481615370.6319034163667740.6454097383217940.7733402691990640.979238841041799
stop funcGenerateFeatureParameters
func_generate_random_lognormal_matrix: START Making features
Start funcShuffleMatrix
stop func_generate_random_lognormal_matrix
start func_generate_random_lognormal_with_outliers
Stop func_generate_random_lognormal_with_outliers
start func_generate_random_lognormal_with_multivariate_spikes
stop func_generate_random_lognormal_with_multivariate_spikes
Parameters BEFORE Calibration File
Length exp NA Length vdMu NA length vdSD NA length vdPercentZero NA Read depth 8030
Parameters AFTER Calibration File (if no calibration file is used, defaults are shown)
Length exp 1 Length vdMu 1 length vdSD 1 length vdPercentZero 1 Read depth 8030 Feature Count 300
func_generate_random_lognormal_matrix START
start funcGenerateFeatureParameters
funcGenerateFeatureParameters: Generating vdExp Vector.
LogMu 2.39794758911363 LogSD 1.33357395233333 Threshold 26.178898826878
funcGenerateFeatureParameters: Generating vdSD Vector.
funcGenerateFeatureParameters: Generating vdMu Vector.
funcGenerateFeatureParameters: Generating vdPercentZero Vector.
***Scale***
1
***vdPercentZero***
0.3633915617519070.5066083164694770.6415395150725680.6430270400425560.765606066986950.98663686342397
stop funcGenerateFeatureParameters
func_generate_random_lognormal_matrix: START Making features
Start funcShuffleMatrix
stop func_generate_random_lognormal_matrix
start func_generate_random_lognormal_with_outliers
Stop func_generate_random_lognormal_with_outliers
start func_generate_random_lognormal_with_multivariate_spikes
stop func_generate_random_lognormal_with_multivariate_spikes
Parameters for Bug-Bug spikes BEFORE Calibration File
Length exp NA Length vdMu NA length vdSD NA length vdPercentZero NA Read depth 8030
Parameters for Bug-Bug spikes AFTER Calibration File (if no calibration file is used, defaults are shown)
Length exp 1 Length vdMu 1 length vdSD 1 length vdPercentZero 1 Read depth 8030 Feature Count 300
start funcGenerateFeatureParameters
funcGenerateFeatureParameters: Generating vdExp Vector.
LogMu 2.39794758911363 LogSD 1.33357395233333 Threshold 26.178898826878
funcGenerateFeatureParameters: Generating vdSD Vector.
funcGenerateFeatureParameters: Generating vdMu Vector.
funcGenerateFeatureParameters: Generating vdPercentZero Vector.
***Scale***
1
***vdPercentZero***
0.3829492145282010.5109190497750480.6404247633832220.6420645335775940.765839028655390.974570579534246
stop funcGenerateFeatureParameters
start func_get_corr_mat_from_num
start func_generate_spike_structure
end func_generate_spike_structure
end func_get_corr_mat_from_num
func_generate_random_lognormal_matrix START
start funcGenerateFeatureParameters
stop funcGenerateFeatureParameters
func_generate_random_lognormal_matrix: START Making features
stop func_generate_random_lognormal_matrix
Warning in sparseDOSSA::sparseDOSSA(strNormalizedFileName = "my_Microbiome.pcl",  :
  number of associations = 0, and no spike file specified; no bug-bug spike-ins will be done.
Parameters BEFORE Calibration File
Length exp NA Length vdMu NA length vdSD NA length vdPercentZero NA Read depth 8030
Parameters AFTER Calibration File (if no calibration file is used, defaults are shown)
Length exp 1 Length vdMu 1 length vdSD 1 length vdPercentZero 1 Read depth 8030 Feature Count 300
func_generate_random_lognormal_matrix START
start funcGenerateFeatureParameters
funcGenerateFeatureParameters: Generating vdExp Vector.
LogMu 2.39794758911363 LogSD 1.33357395233333 Threshold 26.178898826878
funcGenerateFeatureParameters: Generating vdSD Vector.
funcGenerateFeatureParameters: Generating vdMu Vector.
funcGenerateFeatureParameters: Generating vdPercentZero Vector.
***Scale***
1
***vdPercentZero***
0.3727369629860380.519256938589810.629850979173920.6415346803430020.7665530005976420.994435891635014
stop funcGenerateFeatureParameters
func_generate_random_lognormal_matrix: START Making features
Start funcShuffleMatrix
stop func_generate_random_lognormal_matrix
start func_generate_random_lognormal_with_outliers
Stop func_generate_random_lognormal_with_outliers
start func_generate_random_lognormal_with_multivariate_spikes
stop func_generate_random_lognormal_with_multivariate_spikes
[WARNING] Could not parse YAML metadata at line 17 column 1: :2:75: Unexpected '
  '
pandoc-citeproc: reference paulson not found
pandoc-citeproc: reference xochi2012 not found
pandoc-citeproc: reference paulson not found
pandoc-citeproc: reference mice not found
Could not fetch https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;mice.txt
HttpExceptionRequest Request {
  host                 = "latex.codecogs.com"
  port                 = 443
  secure               = True
  requestHeaders       = []
  path                 = "/png.image"
  queryString          = "?%5Cdpi%7B110%7D&space;%5Cbg_white&space;mice.txt"
  method               = "GET"
  proxy                = Nothing
  rawBody              = False
  redirectCount        = 10
  responseTimeout      = ResponseTimeoutDefault
  requestVersion       = HTTP/1.1
}
 (InternalException (HandshakeFailed Error_EOF))
Error: processing vignette 'sparsedossa-vignette.Rmd' failed with diagnostics:
pandoc document conversion failed with error 61
--- failed re-building ‘sparsedossa-vignette.Rmd’

SUMMARY: processing the following file failed:
  ‘sparsedossa-vignette.Rmd’

Error: Vignette re-building failed.
Execution halted