Last updated: 2021-07-04

Checks: 5 2

Knit directory: Embryoid_Body_Pilot_Workflowr/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


The R Markdown is untracked by Git. To know which version of the R Markdown file created these results, you'll want to first commit it to the Git repo. If you're still working on the analysis, you can ignore this warning. When you're finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it's best to always run the code in an empty environment.

The command set.seed(20200804) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Using absolute paths to the files within your workflowr project makes it difficult for you and others to run your code on a different machine. Change the absolute path(s) below to the suggested relative path(s) to make your code more reproducible.

absolute relative
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/mergedObjects/Harmony.Batchindividual.rds ../output/mergedObjects/Harmony.Batchindividual.rds
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/data/dge/hESC1.rawdge.txt.gz ../data/dge/hESC1.rawdge.txt.gz
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/data/dge/iPS-to-EB_20Day_dge.txt.gz ../data/dge/iPS-to-EB_20Day_dge.txt.gz
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/CaoEtAl.Obj.CellsOfAllClusters.ProteinCodingGenes.rds ../output/CaoEtAl.Obj.CellsOfAllClusters.ProteinCodingGenes.rds
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/TranferredAnnotations_ReferenceInt_JustEarlyEcto.csv ../output/TranferredAnnotations_ReferenceInt_JustEarlyEcto.csv

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 56c37c9. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/.Rhistory
    Ignored:    output/.Rhistory

Untracked files:
    Untracked:  GSE122380_raw_counts.txt.gz
    Untracked:  UTF1_plots.Rmd
    Untracked:  analysis/CompiledFits_BatchvInd.Rmd
    Untracked:  analysis/DownSamp_NoiseRatio.Rmd
    Untracked:  analysis/Fig1.Rmd
    Untracked:  analysis/Fig2.Rmd
    Untracked:  analysis/IntegrateReference_SCTregressCaoPlusScHCL.Rmd
    Untracked:  analysis/IntegrateReference_SCTregressCaoPlusScHCL_JustEarlyEcto.Rmd
    Untracked:  analysis/IntegrateReference_SCTregressCaoPlusScHCL_JustEndo.Rmd
    Untracked:  analysis/IntegrateReference_SCTregressCaoPlusScHCL_JustMeso.Rmd
    Untracked:  analysis/IntegrateReference_SCTregressCaoPlusScHCL_JustNeuralCrest.Rmd
    Untracked:  analysis/IntegrateReference_SCTregressCaoPlusScHCL_JustNeuron.Rmd
    Untracked:  analysis/IntegrateReference_SCTregressCaoPlusScHCL_JustPluri.Rmd
    Untracked:  analysis/OLD/
    Untracked:  analysis/Pseudobulk_Limma_Harmony.BatchIndividual_ClusterRes0.8_minPCT0.2.Rmd
    Untracked:  analysis/Pseudobulk_Limma_Harmony.BatchIndividual_ClusterRes1_minPCT0.2.Rmd
    Untracked:  analysis/Pseudobulk_VariancePartition_Harmony.Batchindividual_ClusterRes0.1_byCluster.Rmd
    Untracked:  analysis/RefInt_ComparingFulltoPartialIntegrationAnnotations.Rmd
    Untracked:  analysis/ReferenceAnn_DE.Rmd
    Untracked:  analysis/SingleCell_HierarchicalClustering_NoGeneFilter.Rmd
    Untracked:  analysis/SingleCell_VariancePartitionByCluster_Harmony.Batchindividual_ClusterRes0.1_minPCT0.2.Rmd
    Untracked:  analysis/VarPartPlots_res0.1_SCT.Rmd
    Untracked:  analysis/VarPart_SC_res0.1_SCT.Rmd
    Untracked:  analysis/child/
    Untracked:  analysis/k10topics_Explore.Rmd
    Untracked:  analysis/k6topics_Explore.Rmd
    Untracked:  build_refint_scale.R
    Untracked:  build_refint_sct.R
    Untracked:  build_stuff.R
    Untracked:  build_varpart_sc.R
    Untracked:  code/.ipynb_checkpoints/
    Untracked:  code/CellRangerPreprocess.Rmd
    Untracked:  code/ConvertToDGE.Rmd
    Untracked:  code/ConvertToDGE_PseudoBulk.Rmd
    Untracked:  code/ConvertToDGE_SingleCellRes_minPCT0.2.Rmd
    Untracked:  code/EB.getHumanMetadata.Rmd
    Untracked:  code/GEO_processed_data.Rmd
    Untracked:  code/PowerAnalysis_NoiseRatio.ipynb
    Untracked:  code/Untitled.ipynb
    Untracked:  code/Untitled1.ipynb
    Untracked:  code/compile_fits.Rmd
    Untracked:  code/fit_all_models.sh
    Untracked:  code/fit_poisson_nmf.R
    Untracked:  code/fit_poisson_nmf.sbatch
    Untracked:  code/functions_for_fit_comparison.Rmd
    Untracked:  code/get_genelist_byPCTthresh.Rmd
    Untracked:  code/prefit_poisson_nmf.R
    Untracked:  code/prefit_poisson_nmf.sbatch
    Untracked:  code/prepare_data_for_fastTopics.Rmd
    Untracked:  data/HCL_Fig1_adata.h5ad
    Untracked:  data/HCL_Fig1_adata.h5seurat
    Untracked:  data/dge/
    Untracked:  data/dge_raw_data.tar.gz
    Untracked:  data/ref.expr.rda
    Untracked:  figure/
    Untracked:  output/CR_sampleQCrds/
    Untracked:  output/CaoEtAl.Obj.CellsOfAllClusters.ProteinCodingGenes.rds
    Untracked:  output/CaoEtAl.Obj.rds
    Untracked:  output/ClusterInfo_res0.1.csv
    Untracked:  output/DGELists/
    Untracked:  output/DownSampleVarPart.rds
    Untracked:  output/Frequency.MostCommonAnnotation.FiveNearestRefCells.csv
    Untracked:  output/GEOsubmissionProcessedFiles/
    Untracked:  output/GeneLists_by_minPCT/
    Untracked:  output/MostCommonAnnotation.FiveNearestRefCells.csv
    Untracked:  output/NearestReferenceCell.Cao.hESC.EuclideanDistanceinHarmonySpace.csv
    Untracked:  output/NearestReferenceCell.Cao.hESC.FrequencyofEachAnnotation.csv
    Untracked:  output/NearestReferenceCell.SCTregressRNAassay.Cao.hESC.EuclideanDistanceinHarmonySpace.csv
    Untracked:  output/NearestReferenceCell.SCTregressRNAassay.Cao.hESC.FrequencyofEachAnnotation.csv
    Untracked:  output/Pseudobulk_Limma_res0.1_OnevAllTopTables.csv
    Untracked:  output/Pseudobulk_Limma_res0.1_OnevAll_top10Upregby_adjP.csv
    Untracked:  output/Pseudobulk_Limma_res0.1_OnevAll_top10Upregby_logFC.csv
    Untracked:  output/Pseudobulk_Limma_res0.5_OnevAllTopTables.csv
    Untracked:  output/Pseudobulk_Limma_res0.8_OnevAllTopTables.csv
    Untracked:  output/Pseudobulk_Limma_res1_OnevAllTopTables.csv
    Untracked:  output/Pseudobulk_VarPart.ByCluster.Res0.1.rds
    Untracked:  output/ResidualVariances_fromDownSampAnalysis.csv
    Untracked:  output/SingleCell_VariancePartition_RNA_Res0.1_minPCT0.2.rds
    Untracked:  output/SingleCell_VariancePartition_Res0.1_minPCT0.2.rds
    Untracked:  output/SingleCell_VariancePartition_SCT_Res0.1_minPCT0.2.rds
    Untracked:  output/TopicModelling_k10_top10drivergenes.byBeta.csv
    Untracked:  output/TopicModelling_k6_top10drivergenes.byBeta.csv
    Untracked:  output/TopicModelling_k6_top15drivergenes.byZ.csv
    Untracked:  output/TranferredAnnotations_ReferenceInt_JustEarlyEcto.csv
    Untracked:  output/TranferredAnnotations_ReferenceInt_JustEndoderm.csv
    Untracked:  output/TranferredAnnotations_ReferenceInt_JustMeso.csv
    Untracked:  output/TranferredAnnotations_ReferenceInt_JustNeuralCrest.csv
    Untracked:  output/TranferredAnnotations_ReferenceInt_JustNeuron.csv
    Untracked:  output/TranferredAnnotations_ReferenceInt_JustPluripotent.csv
    Untracked:  output/VarPart.ByCluster.Res0.1.rds
    Untracked:  output/azimuth/
    Untracked:  output/downsamp_10800cells_10subreps_medianexplainedbyresiduals_varpart_PsB.rds
    Untracked:  output/downsamp_16200cells_10subreps_medianexplainedbyresiduals_varpart_PsB.rds
    Untracked:  output/downsamp_21600cells_10subreps_medianexplainedbyresiduals_varpart_PsB.rds
    Untracked:  output/downsamp_2700cells_10subreps_medianexplainedbyresiduals_varpart_PsB.rds
    Untracked:  output/downsamp_2700cells_10subreps_medianexplainedbyresiduals_varpart_scres.rds
    Untracked:  output/downsamp_5400cells_10subreps_medianexplainedbyresiduals_varpart_PsB.rds
    Untracked:  output/downsamp_7200cells_10subreps_medianexplainedbyresiduals_varpart_PsB.rds
    Untracked:  output/fasttopics/
    Untracked:  output/figs/
    Untracked:  output/merge.Cao.SCTwRegressOrigIdent.rds
    Untracked:  output/merge.all.SCTwRegressOrigIdent.Harmony.rds
    Untracked:  output/merged.SCT.counts.matrix.rds
    Untracked:  output/merged.raw.counts.matrix.rds
    Untracked:  output/mergedObjects/
    Untracked:  output/pdfs/
    Untracked:  output/sampleQCrds/
    Untracked:  output/splitgpm_gsea_results/
    Untracked:  slurm-12005914.out
    Untracked:  slurm-12005923.out

Unstaged changes:
    Deleted:    analysis/IntegrateAnalysis.afterFilter.HarmonyBatch.Rmd
    Deleted:    analysis/IntegrateAnalysis.afterFilter.HarmonyBatchSampleIDindividual.Rmd
    Modified:   analysis/IntegrateAnalysis.afterFilter.HarmonyBatchindividual.Rmd
    Deleted:    analysis/IntegrateAnalysis.afterFilter.NOHARMONYjustmerge.Rmd
    Deleted:    analysis/IntegrateAnalysis.afterFilter.SCTregressBatchIndividual.Rmd
    Deleted:    analysis/IntegrateAnalysis.afterFilter.SCTregressBatchIndividualHarmonyBatchindividual.Rmd
    Modified:   analysis/Pseudobulk_HierarchicalClustering_Harmony.Batchindividual_ClusterRes0.1_minPCT0.2.Rmd
    Modified:   analysis/Pseudobulk_HierarchicalClustering_Harmony.Batchindividual_ClusterRes0.5_minPCT0.2.Rmd
    Modified:   analysis/Pseudobulk_HierarchicalClustering_Harmony.Batchindividual_ClusterRes0.8_minPCT0.2.Rmd
    Modified:   analysis/Pseudobulk_HierarchicalClustering_Harmony.Batchindividual_ClusterRes1_minPCT0.2.Rmd
    Modified:   analysis/Pseudobulk_Limma_Harmony.BatchIndividual_ClusterRes0.1_minPCT0.2.Rmd
    Modified:   analysis/Pseudobulk_Limma_Harmony.BatchIndividual_ClusterRes0.5_minPCT0.2.Rmd
    Modified:   analysis/Pseudobulk_VariancePartition_Harmony.Batchindividual_ClusterRes0.1_minPCT0.2.Rmd
    Modified:   analysis/Pseudobulk_VariancePartition_Harmony.Batchindividual_ClusterRes0.5_minPCT0.2.Rmd
    Modified:   analysis/Pseudobulk_VariancePartition_Harmony.Batchindividual_ClusterRes0.8_minPCT0.2.Rmd
    Modified:   analysis/Pseudobulk_VariancePartition_Harmony.Batchindividual_ClusterRes1_minPCT0.2.Rmd
    Deleted:    analysis/RunscHCL_HarmonyBatchInd.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.


library(Seurat)
library(edgeR)
Loading required package: limma
library(dplyr)

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
library(tidyr)
library(tibble)
library(purrr)
library(harmony)
Loading required package: Rcpp
library(ggplot2)

loading data

first, my data

merged<- readRDS("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/mergedObjects/Harmony.Batchindividual.rds")

subset to only early ectoderm cells (cluster 1, res 0.1)

merged<- subset(merged, idents = 1)
DimPlot(merged)

loading in hESC and iPS-to-EB raw dges from scHCL reference set

hESC<- read.table("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/data/dge/hESC1.rawdge.txt.gz", header=T, row.names = 1)

iPStoEBday20<- read.table("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/data/dge/iPS-to-EB_20Day_dge.txt.gz", header=T, row.names = 1)

Note: there is no available metadata for these iPS to EB differentiations (no cell annotations online)

make a seurat objects with all of the data

hESC.obj<- CreateSeuratObject(hESC)
Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')
EB20.obj<- CreateSeuratObject(iPStoEBday20)
Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')
#normalizing each dataset
hESC.obj<- suppressWarnings(SCTransform(hESC.obj, variable.features.n=5000,verbose=F))

EB20.obj<-suppressWarnings(SCTransform(EB20.obj, variable.features.n=5000,verbose=F))
Cao.obj<-readRDS("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/CaoEtAl.Obj.CellsOfAllClusters.ProteinCodingGenes.rds")
Cao.obj<- RunPCA(Cao.obj, npcs= 100, verbose = F)
Cao.obj<- FindNeighbors(Cao.obj, dims = 1:30, verbose = F)
Cao.obj<- RunUMAP(Cao.obj, dims=1:30, verbose = F)
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
#rename Cao metadata so none match with EB (just need to replace Batch)
colnames(Cao.obj@meta.data)
 [1] "orig.ident"        "nCount_RNA"        "nFeature_RNA"     
 [4] "Assay"             "Batch"             "Experiment_batch" 
 [7] "Main_cluster_name" "Fetus_id"          "Sex"              
[10] "nCount_SCT"        "nFeature_SCT"     
colnames(Cao.obj@meta.data)[5]<-"Batch_week"
#rename orig.idents
hESC.obj$orig.ident<- "scHCL.hESC"
EB20.obj$orig.ident<- "scHCL.EB20"
merged$orig.ident<- "EB.Pilot"
Cao.obj$orig.ident<- "Cao.EtAl"
#merge objects
obj.list<- list(Cao.obj, merged, hESC.obj, EB20.obj)
merge.all<- merge(x=obj.list[[1]], y=c(obj.list[[2]], obj.list[[3]], obj.list[[4]]), merge.data=T)
merge.all<- SCTransform(merge.all, variable.features.n = 5000, vars.to.regress = c("orig.ident"), assay= "SCT")

  |                                                                            
  |                                                                      |   0%
Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

  |                                                                            
  |=========                                                             |  12%
Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

  |                                                                            
  |==================                                                    |  25%
Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached
Warning in sqrt(1/i): NaNs produced
Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

  |                                                                            
  |==========================                                            |  38%
Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

  |                                                                            
  |===================================                                   |  50%
Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

  |                                                                            
  |============================================                          |  62%
Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

  |                                                                            
  |====================================================                  |  75%
Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

  |                                                                            
  |=============================================================         |  88%
Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached
Warning: glm.fit: fitted rates numerically 0 occurred
Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

  |                                                                            
  |======================================================================| 100%

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |=                                                                     |   1%
  |                                                                            
  |==                                                                    |   2%
  |                                                                            
  |==                                                                    |   3%
  |                                                                            
  |===                                                                   |   4%
  |                                                                            
  |====                                                                  |   6%
  |                                                                            
  |=====                                                                 |   7%
  |                                                                            
  |======                                                                |   8%
  |                                                                            
  |======                                                                |   9%
  |                                                                            
  |=======                                                               |  10%
  |                                                                            
  |========                                                              |  11%
  |                                                                            
  |=========                                                             |  12%
  |                                                                            
  |=========                                                             |  13%
  |                                                                            
  |==========                                                            |  15%
  |                                                                            
  |===========                                                           |  16%
  |                                                                            
  |============                                                          |  17%
  |                                                                            
  |=============                                                         |  18%
  |                                                                            
  |=============                                                         |  19%
  |                                                                            
  |==============                                                        |  20%
  |                                                                            
  |===============                                                       |  21%
  |                                                                            
  |================                                                      |  22%
  |                                                                            
  |=================                                                     |  24%
  |                                                                            
  |=================                                                     |  25%
  |                                                                            
  |==================                                                    |  26%
  |                                                                            
  |===================                                                   |  27%
  |                                                                            
  |====================                                                  |  28%
  |                                                                            
  |====================                                                  |  29%
  |                                                                            
  |=====================                                                 |  30%
  |                                                                            
  |======================                                                |  31%
  |                                                                            
  |=======================                                               |  33%
  |                                                                            
  |========================                                              |  34%
  |                                                                            
  |========================                                              |  35%
  |                                                                            
  |=========================                                             |  36%
  |                                                                            
  |==========================                                            |  37%
  |                                                                            
  |===========================                                           |  38%
  |                                                                            
  |============================                                          |  39%
  |                                                                            
  |============================                                          |  40%
  |                                                                            
  |=============================                                         |  42%
  |                                                                            
  |==============================                                        |  43%
  |                                                                            
  |===============================                                       |  44%
  |                                                                            
  |===============================                                       |  45%
  |                                                                            
  |================================                                      |  46%
  |                                                                            
  |=================================                                     |  47%
  |                                                                            
  |==================================                                    |  48%
  |                                                                            
  |===================================                                   |  49%
  |                                                                            
  |===================================                                   |  51%
  |                                                                            
  |====================================                                  |  52%
  |                                                                            
  |=====================================                                 |  53%
  |                                                                            
  |======================================                                |  54%
  |                                                                            
  |=======================================                               |  55%
  |                                                                            
  |=======================================                               |  56%
  |                                                                            
  |========================================                              |  57%
  |                                                                            
  |=========================================                             |  58%
  |                                                                            
  |==========================================                            |  60%
  |                                                                            
  |==========================================                            |  61%
  |                                                                            
  |===========================================                           |  62%
  |                                                                            
  |============================================                          |  63%
  |                                                                            
  |=============================================                         |  64%
  |                                                                            
  |==============================================                        |  65%
  |                                                                            
  |==============================================                        |  66%
  |                                                                            
  |===============================================                       |  67%
  |                                                                            
  |================================================                      |  69%
  |                                                                            
  |=================================================                     |  70%
  |                                                                            
  |==================================================                    |  71%
  |                                                                            
  |==================================================                    |  72%
  |                                                                            
  |===================================================                   |  73%
  |                                                                            
  |====================================================                  |  74%
  |                                                                            
  |=====================================================                 |  75%
  |                                                                            
  |=====================================================                 |  76%
  |                                                                            
  |======================================================                |  78%
  |                                                                            
  |=======================================================               |  79%
  |                                                                            
  |========================================================              |  80%
  |                                                                            
  |=========================================================             |  81%
  |                                                                            
  |=========================================================             |  82%
  |                                                                            
  |==========================================================            |  83%
  |                                                                            
  |===========================================================           |  84%
  |                                                                            
  |============================================================          |  85%
  |                                                                            
  |=============================================================         |  87%
  |                                                                            
  |=============================================================         |  88%
  |                                                                            
  |==============================================================        |  89%
  |                                                                            
  |===============================================================       |  90%
  |                                                                            
  |================================================================      |  91%
  |                                                                            
  |================================================================      |  92%
  |                                                                            
  |=================================================================     |  93%
  |                                                                            
  |==================================================================    |  94%
  |                                                                            
  |===================================================================   |  96%
  |                                                                            
  |====================================================================  |  97%
  |                                                                            
  |====================================================================  |  98%
  |                                                                            
  |===================================================================== |  99%
  |                                                                            
  |======================================================================| 100%

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |=                                                                     |   1%
  |                                                                            
  |==                                                                    |   2%
  |                                                                            
  |==                                                                    |   3%
  |                                                                            
  |===                                                                   |   4%
  |                                                                            
  |====                                                                  |   6%
  |                                                                            
  |=====                                                                 |   7%
  |                                                                            
  |======                                                                |   8%
  |                                                                            
  |======                                                                |   9%
  |                                                                            
  |=======                                                               |  10%
  |                                                                            
  |========                                                              |  11%
  |                                                                            
  |=========                                                             |  12%
  |                                                                            
  |=========                                                             |  13%
  |                                                                            
  |==========                                                            |  15%
  |                                                                            
  |===========                                                           |  16%
  |                                                                            
  |============                                                          |  17%
  |                                                                            
  |=============                                                         |  18%
  |                                                                            
  |=============                                                         |  19%
  |                                                                            
  |==============                                                        |  20%
  |                                                                            
  |===============                                                       |  21%
  |                                                                            
  |================                                                      |  22%
  |                                                                            
  |=================                                                     |  24%
  |                                                                            
  |=================                                                     |  25%
  |                                                                            
  |==================                                                    |  26%
  |                                                                            
  |===================                                                   |  27%
  |                                                                            
  |====================                                                  |  28%
  |                                                                            
  |====================                                                  |  29%
  |                                                                            
  |=====================                                                 |  30%
  |                                                                            
  |======================                                                |  31%
  |                                                                            
  |=======================                                               |  33%
  |                                                                            
  |========================                                              |  34%
  |                                                                            
  |========================                                              |  35%
  |                                                                            
  |=========================                                             |  36%
  |                                                                            
  |==========================                                            |  37%
  |                                                                            
  |===========================                                           |  38%
  |                                                                            
  |============================                                          |  39%
  |                                                                            
  |============================                                          |  40%
  |                                                                            
  |=============================                                         |  42%
  |                                                                            
  |==============================                                        |  43%
  |                                                                            
  |===============================                                       |  44%
  |                                                                            
  |===============================                                       |  45%
  |                                                                            
  |================================                                      |  46%
  |                                                                            
  |=================================                                     |  47%
  |                                                                            
  |==================================                                    |  48%
  |                                                                            
  |===================================                                   |  49%
  |                                                                            
  |===================================                                   |  51%
  |                                                                            
  |====================================                                  |  52%
  |                                                                            
  |=====================================                                 |  53%
  |                                                                            
  |======================================                                |  54%
  |                                                                            
  |=======================================                               |  55%
  |                                                                            
  |=======================================                               |  56%
  |                                                                            
  |========================================                              |  57%
  |                                                                            
  |=========================================                             |  58%
  |                                                                            
  |==========================================                            |  60%
  |                                                                            
  |==========================================                            |  61%
  |                                                                            
  |===========================================                           |  62%
  |                                                                            
  |============================================                          |  63%
  |                                                                            
  |=============================================                         |  64%
  |                                                                            
  |==============================================                        |  65%
  |                                                                            
  |==============================================                        |  66%
  |                                                                            
  |===============================================                       |  67%
  |                                                                            
  |================================================                      |  69%
  |                                                                            
  |=================================================                     |  70%
  |                                                                            
  |==================================================                    |  71%
  |                                                                            
  |==================================================                    |  72%
  |                                                                            
  |===================================================                   |  73%
  |                                                                            
  |====================================================                  |  74%
  |                                                                            
  |=====================================================                 |  75%
  |                                                                            
  |=====================================================                 |  76%
  |                                                                            
  |======================================================                |  78%
  |                                                                            
  |=======================================================               |  79%
  |                                                                            
  |========================================================              |  80%
  |                                                                            
  |=========================================================             |  81%
  |                                                                            
  |=========================================================             |  82%
  |                                                                            
  |==========================================================            |  83%
  |                                                                            
  |===========================================================           |  84%
  |                                                                            
  |============================================================          |  85%
  |                                                                            
  |=============================================================         |  87%
  |                                                                            
  |=============================================================         |  88%
  |                                                                            
  |==============================================================        |  89%
  |                                                                            
  |===============================================================       |  90%
  |                                                                            
  |================================================================      |  91%
  |                                                                            
  |================================================================      |  92%
  |                                                                            
  |=================================================================     |  93%
  |                                                                            
  |==================================================================    |  94%
  |                                                                            
  |===================================================================   |  96%
  |                                                                            
  |====================================================================  |  97%
  |                                                                            
  |====================================================================  |  98%
  |                                                                            
  |===================================================================== |  99%
  |                                                                            
  |======================================================================| 100%
all.genes= rownames(merge.all)
merge.all<-FindVariableFeatures(merge.all,selection.method="vst", nfeatures = 5000)
merge.all<- ScaleData(merge.all, features = all.genes, assay = "SCT")
Centering and scaling data matrix
merge.all<-RunPCA(merge.all, npcs = 100, verbose=F, Assay="SCT")
DimPlot(merge.all, reduction = "pca", group.by = "orig.ident")

merge.all<- RunHarmony(merge.all, c("orig.ident", "individual", "Batch"), theta = c(3,1,1), plot_convergence = T, assay.use = "SCT")
Harmony 1/10
Harmony 2/10
Harmony 3/10
Harmony 4/10
Harmony 5/10
Harmony converged after 5 iterations
Warning: Invalid name supplied, making object name syntactically valid. New
object name is Seurat..ProjectDim.SCT.harmony; see ?make.names for more details
on syntax validity

DimPlot(merge.all, group.by= "orig.ident", reduction= "harmony")

merge.all<- RunUMAP(merge.all,dims=1:100, reduction="harmony")
13:46:29 UMAP embedding parameters a = 0.9922 b = 1.112
13:46:29 Read 60631 rows and found 100 numeric columns
13:46:29 Using Annoy for neighbor search, n_neighbors = 30
13:46:29 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
13:46:47 Writing NN index file to temp file /tmp/jobs/12028895/RtmpnS9Nbs/file35c964afb56d3
13:46:47 Searching Annoy index using 1 thread, search_k = 3000
13:47:26 Annoy recall = 100%
13:47:28 Commencing smooth kNN distance calibration using 1 thread
13:47:31 Initializing from normalized Laplacian + noise
13:47:37 Commencing optimization for 200 epochs, with 2893712 positive edges
13:48:57 Optimization finished
V<- DimPlot(merge.all, group.by = "Main_cluster_name", label = T, label.size = 2.5, repel = T)+NoLegend()
Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
Please use `as_label()` or `as_name()` instead.
This warning is displayed once per session.
V
Warning: ggrepel: 33 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

Add new metadata to include Main_cluster_name as well as cluster labels from EB Pilot DE results, and orig ident of the HCL data

merge.all<- AddMetaData(merge.all, col.name = "all.labels", metadata = merge.all@meta.data$Main_cluster_name)

#for cells with NA for main_cluster_name, replace label from SCT_snn_res 0.1
for (i in (1:nrow(merge.all@meta.data))){
if (is.na(merge.all@meta.data$all.labels[i]) == T){
  merge.all@meta.data$all.labels[i]<- merge.all@meta.data$SCT_snn_res.0.1[i]
}
}

#and now replace remaining NAs with orig.ident
for (i in (1:nrow(merge.all@meta.data))){
if (is.na(merge.all@meta.data$all.labels[i]) == T){
  merge.all@meta.data$all.labels[i]<- merge.all@meta.data$orig.ident[i]
}
}
V<- DimPlot(merge.all, group.by = "all.labels", label = T, label.size = 2.5, repel = T)+NoLegend()

V
Warning: ggrepel: 34 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

V<- DimPlot(merge.all, group.by = "orig.ident", label = F, order= c("scHCL.hESC","scHCL.EB20", "EB.Pilot","Cao.EtAl"))

V

DimPlot(merge.all, split.by = "orig.ident", label = F, order= c("scHCL.hESC","scHCL.EB20", "EB.Pilot","Cao.EtAl"))

options(ggrepel.max.overlaps = Inf)
#subset object to just my data and Cao reference, plot UMAP
Idents(merge.all)<- "orig.ident"
sub<- subset(merge.all, idents= c("Cao.EtAl", "EB.Pilot"))
V<- DimPlot(sub, group.by = "all.labels", label = T, repel = T, label.size = 2.5)+NoLegend()
V

V<- DimPlot(sub, group.by = "Main_cluster_name", label = T, repel = T, label.size = 2.5)+NoLegend()
V

V<- DimPlot(sub, group.by = "SCT_snn_res.0.1", label = T, repel = T, label.size = 2.5)+NoLegend()
V

DimPlot(sub, group.by = "orig.ident")

DimPlot(sub, group.by = "orig.ident", order="Cao.EtAl")

#Subset object to just my data and HCL references, plot UMAP
sub<- subset(merge.all, idents= c("scHCL.EB20", "EB.Pilot"))
V<- DimPlot(sub, group.by = "orig.ident", label = F)
V

sub<- subset(merge.all, idents= c("scHCL.EB20", "EB.Pilot", "Cao.EtAl"))
DimPlot(sub, group.by="orig.ident", pt.size = 0.2, label=F) 

DimPlot(merge.all, split.by="orig.ident",group.by = "all.labels", pt.size = 0.2, label=F) +NoLegend()

Now, will transfer labels for Cao Et Al and hESC onto my data.

#subset to remove scHCL.EB20
sub<- subset(merge.all, idents= c("Cao.EtAl", "EB.Pilot", "scHCL.hESC"))
#compute distance matrix based on harmony embeddings, dims 1:100
har_embeds<- sub@reductions$harmony@cell.embeddings

har_distmat<- as.matrix(dist(har_embeds, method="euclidean", upper=TRUE))
#vectors with cell ids from Cao.EtAl, EB.pilot, and scHCL.hESC
EB.pilot.ids<-rownames(merge.all@meta.data[merge.all@meta.data$orig.ident == "EB.Pilot",])

#subset rows to only cells in EB.pilot
sub_har_distmat<- har_distmat[rownames(har_distmat) %in% EB.pilot.ids,]

#subset cols to only cells not in EB.pilot
'%notin%'<- Negate('%in%')
sub_har_distmat<- sub_har_distmat[,colnames(sub_har_distmat) %notin% EB.pilot.ids]
nearest.ref.cell.id<- NULL
nearest.ref.cell.dist<- NULL
#for loop, loop through each row
for (i in 1:nrow(sub_har_distmat)){
  nearest.ref.cell.dist[i]<- min(sub_har_distmat[i,])
  nearest.ref.cell.id[i]<- names(which.min(sub_har_distmat[i,]))
}

nearest.ref.table<- cbind(rownames(sub_har_distmat), nearest.ref.cell.id,nearest.ref.cell.dist)

colnames(nearest.ref.table)<- c("EB.cell.id", "nearest.ref.cell.id", "harmony.dist.to.nearest.ref.cell")
#add annotation
ann<- as.data.frame(merge.all@meta.data$all.labels)
ann<- cbind(rownames(merge.all@meta.data), ann)
colnames(ann)<- c("nearest.ref.cell.id", "annotation")

nearest.ref.table<- as.data.frame(nearest.ref.table)
nearest.ann<- left_join(nearest.ref.table, ann, by=c("nearest.ref.cell.id"))
a<- as.data.frame(table(nearest.ann$annotation))
a<- a[a$Var1 != "scHCL.EB20",]
a<- a[a$Var1 != "0",]
a<- a[a$Var1 != "1",]
a<- a[a$Var1 != "2",]
a<- a[a$Var1 != "3",]
a<- a[a$Var1 != "4",]
a<- a[a$Var1 != "5",]
a<- a[a$Var1 != "6",]
a<- a[a$Var1 != "7",]
colnames(a)<- c("reference.cell.type", "Frequency")

a
                                 reference.cell.type Frequency
2                             AFP_ALB positive cells         0
3                                       Acinar cells        80
4                               Adrenocortical cells         4
5                                     Amacrine cells       134
6                           Antigen presenting cells        11
7                                         Astrocytes         8
8                                      Bipolar cells        18
9          Bronchiolar and alveolar epithelial cells         0
10                        CCL19_CCL21 positive cells         0
11                          CLC_IL5RA positive cells         0
12                          CSH1_CSH2 positive cells         0
13                                    Cardiomyocytes         1
14                                  Chromaffin cells         1
15                         Ciliated epithelial cells       302
16         Corneal and conjunctival epithelial cells         2
17                                      Ductal cells        16
18                         ELF3_AGBL2 positive cells         1
19                                          ENS glia         4
20                                       ENS neurons         4
21                                 Endocardial cells        11
22                              Epicardial fat cells         0
23                                     Erythroblasts        14
24                                Excitatory neurons         0
25                         Extravillous trophoblasts         2
26                                    Ganglion cells         0
27                                      Goblet cells         1
28                                   Granule neurons         4
29                          Hematopoietic stem cells         1
30                                      Hepatoblasts         8
31                                  Horizontal cells        68
32                        IGFBP1_DKK1 positive cells         9
33                           Inhibitory interneurons         9
34                                Inhibitory neurons         4
35                       Intestinal epithelial cells         0
36                             Islet endocrine cells        12
37                                  Lens fibre cells         7
38                             Limbic system neurons         1
39                       Lymphatic endothelial cells         2
40                                    Lymphoid cells         8
41                        MUC13_DMBT1 positive cells         2
42                                    Megakaryocytes         2
43                                   Mesangial cells         2
44                                 Mesothelial cells         1
45                                 Metanephric cells        25
46                                         Microglia        27
47                                     Myeloid cells         6
48                              Neuroendocrine cells         0
49                                  Oligodendrocytes        12
50                         PAEP_MECOM positive cells         0
51                     PDE11A_FAM19A2 positive cells         4
52                        PDE1C_ACSM3 positive cells         0
53                          Parietal and chief cells        13
54                               Photoreceptor cells        43
55                                  Purkinje neurons        26
56                             Retinal pigment cells       220
57               Retinal progenitors and Muller glia        10
58                        SATB2_LRRC7 positive cells         0
59                        SKOR2_NPSR1 positive cells         3
60                      SLC24A4_PEX5L positive cells         9
61                       SLC26A4_PAEP positive cells         0
62                          STC2_TLX1 positive cells         0
63                                   Satellite cells         0
64                                     Schwann cells         5
65                             Skeletal muscle cells         3
66                               Smooth muscle cells         2
67                         Squamous epithelial cells        13
68                                    Stellate cells         2
69                                     Stromal cells         1
70                                    Sympathoblasts         0
71 Syncytiotrophoblasts and villous cytotrophoblasts         2
72                           Thymic epithelial cells         0
73                                        Thymocytes        47
74                           Trophoblast giant cells         7
75                              Unipolar brush cells         2
76                                Ureteric bud cells         2
77                        Vascular endothelial cells         0
78                                  Visceral neurons         1
80                                        scHCL.hESC     13144
sub<- subset(merge.all, idents= c("EB.Pilot"))
EB.cell.id<- rownames(sub@meta.data)
sub@meta.data<- cbind(sub@meta.data, EB.cell.id)
sub@meta.data<- full_join(sub@meta.data, nearest.ann, by= c("EB.cell.id"))
rownames(sub@meta.data)<- EB.cell.id

V<- DimPlot(sub, group.by="annotation", pt.size = 0.2, label.size = 2.5,label=T, repel=T) +NoLegend()

V

Instead of matching to just one nearest cell, we will get a more robust annotation if we check a group of nearest neighbors for common annotations

mostcommon.ann<- NULL
maxann.FIVEnearest<- NULL

#for loop, loop through each row
for (i in 1:nrow(sub_har_distmat)){
  cell<- sub_har_distmat[i,]
  cell<- cell[order(cell)]
  topfive<- names(cell[1:5])
  
  #get the annotations of the nearest 5 reference cells
  topfiveann<- merge.all@meta.data$all.labels[rownames(merge.all@meta.data) %in% topfive]
  
  #if/else at least 3/5 match annotations
  maxann<- max(table(topfiveann))
  finalann<- names(which.max(table(topfiveann)))
  
  maxann.FIVEnearest[i]<- maxann
  
  if(maxann >= 3){
    mostcommon.ann[i]<- finalann
  } else {
    mostcommon.ann[i]<- "uncertain"
  }
  
}

CommonAnnDF<- as.data.frame(cbind(rownames(sub_har_distmat), mostcommon.ann, maxann.FIVEnearest))
colnames(CommonAnnDF)<- c("EB.cell.id", "Annotation", "NoutofFIVErefneighborsWithSameAnnotation")
write.csv(CommonAnnDF,"/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/TranferredAnnotations_ReferenceInt_JustEarlyEcto.csv")
b<- table(CommonAnnDF$Annotation)

b

                                     Acinar cells 
                                               24 
                                   Amacrine cells 
                                              133 
                                       Astrocytes 
                                                4 
                                    Bipolar cells 
                                                3 
                                 Chromaffin cells 
                                                1 
                        Ciliated epithelial cells 
                                              291 
                                     Ductal cells 
                                                3 
                                         ENS glia 
                                                3 
                                Endocardial cells 
                                                6 
                                    Erythroblasts 
                                                4 
                                     Hepatoblasts 
                                                7 
                                 Horizontal cells 
                                               69 
                       IGFBP1_DKK1 positive cells 
                                                3 
                               Inhibitory neurons 
                                                1 
                            Islet endocrine cells 
                                                3 
                                 Lens fibre cells 
                                                6 
                                   Megakaryocytes 
                                                2 
                                  Mesangial cells 
                                                1 
                                Metanephric cells 
                                                1 
                                        Microglia 
                                               21 
                                 Oligodendrocytes 
                                                7 
                         Parietal and chief cells 
                                                2 
                              Photoreceptor cells 
                                                5 
                                 Purkinje neurons 
                                               14 
                            Retinal pigment cells 
                                              375 
              Retinal progenitors and Muller glia 
                                                4 
                       SKOR2_NPSR1 positive cells 
                                                2 
                     SLC24A4_PEX5L positive cells 
                                                7 
                                    Schwann cells 
                                                2 
                            Skeletal muscle cells 
                                                3 
                              Smooth muscle cells 
                                                3 
                        Squamous epithelial cells 
                                                9 
                                   Stellate cells 
                                                1 
                                    Stromal cells 
                                                1 
Syncytiotrophoblasts and villous cytotrophoblasts 
                                                1 
                                       Thymocytes 
                                               12 
                          Trophoblast giant cells 
                                                2 
                                       scHCL.hESC 
                                            13064 
                                        uncertain 
                                              283 
#print fetal celltypes not present in EB data
unique(merge.all@meta.data$Main_cluster_name)[unique(merge.all@meta.data$Main_cluster_name) %notin% names(b)] 
 [1] "Ganglion cells"                           
 [2] "Corneal and conjunctival epithelial cells"
 [3] "PDE11A_FAM19A2 positive cells"            
 [4] "Vascular endothelial cells"               
 [5] "Adrenocortical cells"                     
 [6] "Myeloid cells"                            
 [7] "Lymphoid cells"                           
 [8] "Sympathoblasts"                           
 [9] "SLC26A4_PAEP positive cells"              
[10] "CSH1_CSH2 positive cells"                 
[11] "Inhibitory interneurons"                  
[12] "Granule neurons"                          
[13] "Unipolar brush cells"                     
[14] "Excitatory neurons"                       
[15] "Limbic system neurons"                    
[16] "CLC_IL5RA positive cells"                 
[17] "SATB2_LRRC7 positive cells"               
[18] "Epicardial fat cells"                     
[19] "Cardiomyocytes"                           
[20] "ELF3_AGBL2 positive cells"                
[21] "Lymphatic endothelial cells"              
[22] "Visceral neurons"                         
[23] "Mesothelial cells"                        
[24] "ENS neurons"                              
[25] "Intestinal epithelial cells"              
[26] "Ureteric bud cells"                       
[27] "Hematopoietic stem cells"                 
[28] "Neuroendocrine cells"                     
[29] "Bronchiolar and alveolar epithelial cells"
[30] "Satellite cells"                          
[31] "CCL19_CCL21 positive cells"               
[32] "AFP_ALB positive cells"                   
[33] "Extravillous trophoblasts"                
[34] "PAEP_MECOM positive cells"                
[35] "STC2_TLX1 positive cells"                 
[36] "PDE1C_ACSM3 positive cells"               
[37] "MUC13_DMBT1 positive cells"               
[38] "Goblet cells"                             
[39] "Antigen presenting cells"                 
[40] "Thymic epithelial cells"                  
[41] NA                                         
sub<- subset(merge.all, idents= c("EB.Pilot"))
EB.cell.id<- rownames(sub@meta.data)
sub@meta.data<- cbind(sub@meta.data, EB.cell.id)
sub@meta.data<- full_join(sub@meta.data, CommonAnnDF, by= c("EB.cell.id"))
rownames(sub@meta.data)<- EB.cell.id

V<- DimPlot(sub, group.by="Annotation", pt.size = 0.2, label.size = 2.5,label=T, repel=T) +NoLegend()

V

sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
[1] C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggplot2_3.3.3   harmony_1.0     Rcpp_1.0.6      purrr_0.3.4    
 [5] tibble_3.0.4    tidyr_1.1.0     dplyr_1.0.2     edgeR_3.28.1   
 [9] limma_3.42.2    Seurat_3.2.0    workflowr_1.6.2

loaded via a namespace (and not attached):
  [1] Rtsne_0.15            colorspace_2.0-0      deldir_0.1-28        
  [4] ellipsis_0.3.1        ggridges_0.5.2        rprojroot_2.0.2      
  [7] fs_1.4.2              spatstat.data_1.4-3   farver_2.0.3         
 [10] leiden_0.3.3          listenv_0.8.0         npsurv_0.4-0         
 [13] ggrepel_0.9.0         RSpectra_0.16-0       codetools_0.2-16     
 [16] splines_3.6.1         lsei_1.2-0            knitr_1.29           
 [19] polyclip_1.10-0       jsonlite_1.7.2        ica_1.0-2            
 [22] cluster_2.1.0         png_0.1-7             uwot_0.1.10          
 [25] shiny_1.5.0           sctransform_0.2.1     compiler_3.6.1       
 [28] httr_1.4.2            Matrix_1.2-18         fastmap_1.0.1        
 [31] lazyeval_0.2.2        later_1.1.0.1         htmltools_0.5.0      
 [34] tools_3.6.1           rsvd_1.0.3            igraph_1.2.6         
 [37] gtable_0.3.0          glue_1.4.2            RANN_2.6.1           
 [40] reshape2_1.4.4        rappdirs_0.3.3        spatstat_1.64-1      
 [43] vctrs_0.3.6           gdata_2.18.0          ape_5.4-1            
 [46] nlme_3.1-140          lmtest_0.9-37         xfun_0.16            
 [49] stringr_1.4.0         globals_0.12.5        mime_0.9             
 [52] miniUI_0.1.1.1        lifecycle_0.2.0       irlba_2.3.3          
 [55] gtools_3.8.2          goftest_1.2-2         future_1.18.0        
 [58] MASS_7.3-51.4         zoo_1.8-8             scales_1.1.1         
 [61] promises_1.1.1        spatstat.utils_1.17-0 parallel_3.6.1       
 [64] RColorBrewer_1.1-2    yaml_2.2.1            reticulate_1.20      
 [67] pbapply_1.4-2         gridExtra_2.3         rpart_4.1-15         
 [70] stringi_1.5.3         highr_0.8             caTools_1.18.0       
 [73] rlang_0.4.10          pkgconfig_2.0.3       bitops_1.0-6         
 [76] evaluate_0.14         lattice_0.20-38       ROCR_1.0-7           
 [79] tensor_1.5            labeling_0.4.2        patchwork_1.1.1      
 [82] htmlwidgets_1.5.1     cowplot_1.1.1         tidyselect_1.1.0     
 [85] RcppAnnoy_0.0.18      plyr_1.8.6            magrittr_2.0.1       
 [88] R6_2.5.0              gplots_3.0.4          generics_0.1.0       
 [91] withr_2.4.2           pillar_1.4.7          mgcv_1.8-28          
 [94] fitdistrplus_1.0-14   survival_3.2-3        abind_1.4-5          
 [97] future.apply_1.6.0    crayon_1.3.4          KernSmooth_2.23-15   
[100] plotly_4.9.2.1        rmarkdown_2.3         locfit_1.5-9.4       
[103] grid_3.6.1            data.table_1.13.4     git2r_0.26.1         
[106] digest_0.6.27         xtable_1.8-4          httpuv_1.5.4         
[109] munsell_0.5.0         viridisLite_0.3.0    

sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
[1] C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggplot2_3.3.3   harmony_1.0     Rcpp_1.0.6      purrr_0.3.4    
 [5] tibble_3.0.4    tidyr_1.1.0     dplyr_1.0.2     edgeR_3.28.1   
 [9] limma_3.42.2    Seurat_3.2.0    workflowr_1.6.2

loaded via a namespace (and not attached):
  [1] Rtsne_0.15            colorspace_2.0-0      deldir_0.1-28        
  [4] ellipsis_0.3.1        ggridges_0.5.2        rprojroot_2.0.2      
  [7] fs_1.4.2              spatstat.data_1.4-3   farver_2.0.3         
 [10] leiden_0.3.3          listenv_0.8.0         npsurv_0.4-0         
 [13] ggrepel_0.9.0         RSpectra_0.16-0       codetools_0.2-16     
 [16] splines_3.6.1         lsei_1.2-0            knitr_1.29           
 [19] polyclip_1.10-0       jsonlite_1.7.2        ica_1.0-2            
 [22] cluster_2.1.0         png_0.1-7             uwot_0.1.10          
 [25] shiny_1.5.0           sctransform_0.2.1     compiler_3.6.1       
 [28] httr_1.4.2            Matrix_1.2-18         fastmap_1.0.1        
 [31] lazyeval_0.2.2        later_1.1.0.1         htmltools_0.5.0      
 [34] tools_3.6.1           rsvd_1.0.3            igraph_1.2.6         
 [37] gtable_0.3.0          glue_1.4.2            RANN_2.6.1           
 [40] reshape2_1.4.4        rappdirs_0.3.3        spatstat_1.64-1      
 [43] vctrs_0.3.6           gdata_2.18.0          ape_5.4-1            
 [46] nlme_3.1-140          lmtest_0.9-37         xfun_0.16            
 [49] stringr_1.4.0         globals_0.12.5        mime_0.9             
 [52] miniUI_0.1.1.1        lifecycle_0.2.0       irlba_2.3.3          
 [55] gtools_3.8.2          goftest_1.2-2         future_1.18.0        
 [58] MASS_7.3-51.4         zoo_1.8-8             scales_1.1.1         
 [61] promises_1.1.1        spatstat.utils_1.17-0 parallel_3.6.1       
 [64] RColorBrewer_1.1-2    yaml_2.2.1            reticulate_1.20      
 [67] pbapply_1.4-2         gridExtra_2.3         rpart_4.1-15         
 [70] stringi_1.5.3         highr_0.8             caTools_1.18.0       
 [73] rlang_0.4.10          pkgconfig_2.0.3       bitops_1.0-6         
 [76] evaluate_0.14         lattice_0.20-38       ROCR_1.0-7           
 [79] tensor_1.5            labeling_0.4.2        patchwork_1.1.1      
 [82] htmlwidgets_1.5.1     cowplot_1.1.1         tidyselect_1.1.0     
 [85] RcppAnnoy_0.0.18      plyr_1.8.6            magrittr_2.0.1       
 [88] R6_2.5.0              gplots_3.0.4          generics_0.1.0       
 [91] withr_2.4.2           pillar_1.4.7          mgcv_1.8-28          
 [94] fitdistrplus_1.0-14   survival_3.2-3        abind_1.4-5          
 [97] future.apply_1.6.0    crayon_1.3.4          KernSmooth_2.23-15   
[100] plotly_4.9.2.1        rmarkdown_2.3         locfit_1.5-9.4       
[103] grid_3.6.1            data.table_1.13.4     git2r_0.26.1         
[106] digest_0.6.27         xtable_1.8-4          httpuv_1.5.4         
[109] munsell_0.5.0         viridisLite_0.3.0