Integrate lanes from all batches of EB pilot (Human data only, no doublets

workflowr

• Summary
• Checks
• Past versions

Last updated: 2020-08-10

Checks: 7 0

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.

---

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

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.

Seed: set.seed(20200804)

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.

Session information: recorded

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

Cache: none

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

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: f50ebd3

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 f50ebd3. 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:    output/.Rhistory

Untracked files:
    Untracked:  analysis/child/
    Untracked:  code/EB.getHumanMetadata.Rmd
    Untracked:  figure/
    Untracked:  output/mergedObjects/
    Untracked:  output/sampleQCrds/

Unstaged changes:
    Modified:   .Rprofile
    Modified:   .gitattributes
    Modified:   .gitignore
    Modified:   Embryoid_Body_Pilot_Workflowr.Rproj
    Modified:   README.md
    Modified:   _workflowr.yml
    Modified:   analysis/_site.yml
    Modified:   analysis/about.Rmd
    Modified:   analysis/index.Rmd
    Modified:   analysis/license.Rmd
    Modified:   code/README.md
    Modified:   data/README.md
    Modified:   output/README.md

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.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/IntegrateAnalysis.afterFilter.SCTregressBatchIndividualHarmonyBatchindividual.Rmd) and HTML (docs/IntegrateAnalysis.afterFilter.SCTregressBatchIndividualHarmonyBatchindividual.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	f50ebd3	KLRhodes	2020-08-10	wflow_publish("analysis/Integrate*")

---

library(Seurat)
library(harmony)
library(ggplot2)
library(DataCombine)
library(here)
library(RColorBrewer)
options(future.globals.maxSize= 15000*1024^2) #allow global exceeding 4Gb

Read in the files, add metadata, and create an object list

filelist<-list.files(here::here('output/sampleQCrds/'), full.names = T)
objectlist<- list()
for (i in 1:length(filelist)){
  rds<- readRDS(filelist[i])
  objectlist[i]<- rds
  
}

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

create a merged seurat object

ids<-substr(basename(filelist),1,12)
merged<- merge(objectlist[[1]], c(objectlist[[2]], objectlist[[3]],objectlist[[4]],objectlist[[5]],objectlist[[6]],objectlist[[7]],objectlist[[8]],objectlist[[9]],objectlist[[10]],objectlist[[11]],objectlist[[12]],objectlist[[13]],objectlist[[14]],objectlist[[15]],objectlist[[16]]),add.cell.ids=ids, merge.data=T)

#need to fix the individual names because they are slightly different from batch1
replacements<- data.frame(from= c("SNG-NA18511.variant2", "SNG-NA18858.variant2", "SNG-NA19160.variant2"), to=c("SNG-NA18511", "SNG-NA18858", "SNG-NA19160"))
merged@meta.data<-FindReplace(merged@meta.data, "individual", replacements, from = "from", to= "to", exact=T, vector=F )

Only exact matches will be replaced.

merged <- SCTransform(merged, variable.features.n = 5000, vars.to.regress = c("Batch","individual"))

Calculating cell attributes for input UMI matrix

Variance stabilizing transformation of count matrix of size 18065 by 42488

Model formula is y ~ log_umi

Get Negative Binomial regression parameters per gene

Using 2000 genes, 42488 cells

  |                                                                            
  |                                                                      |   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

  |                                                                            
  |=========                                                             |  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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  |                                                                            
  |===================================                                   |  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

  |                                                                            
  |============================================                          |  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

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

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

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

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

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

  |                                                                            
  |======================================================================| 100%

Second step: Get residuals using fitted parameters for 18065 genes

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |=                                                                     |   1%
  |                                                                            
  |==                                                                    |   3%
  |                                                                            
  |===                                                                   |   4%
  |                                                                            
  |====                                                                  |   6%
  |                                                                            
  |=====                                                                 |   7%
  |                                                                            
  |======                                                                |   8%
  |                                                                            
  |=======                                                               |  10%
  |                                                                            
  |========                                                              |  11%
  |                                                                            
  |=========                                                             |  13%
  |                                                                            
  |==========                                                            |  14%
  |                                                                            
  |===========                                                           |  15%
  |                                                                            
  |============                                                          |  17%
  |                                                                            
  |=============                                                         |  18%
  |                                                                            
  |==============                                                        |  20%
  |                                                                            
  |===============                                                       |  21%
  |                                                                            
  |================                                                      |  23%
  |                                                                            
  |=================                                                     |  24%
  |                                                                            
  |==================                                                    |  25%
  |                                                                            
  |===================                                                   |  27%
  |                                                                            
  |====================                                                  |  28%
  |                                                                            
  |=====================                                                 |  30%
  |                                                                            
  |======================                                                |  31%
  |                                                                            
  |=======================                                               |  32%
  |                                                                            
  |========================                                              |  34%
  |                                                                            
  |=========================                                             |  35%
  |                                                                            
  |==========================                                            |  37%
  |                                                                            
  |===========================                                           |  38%
  |                                                                            
  |============================                                          |  39%
  |                                                                            
  |=============================                                         |  41%
  |                                                                            
  |==============================                                        |  42%
  |                                                                            
  |===============================                                       |  44%
  |                                                                            
  |================================                                      |  45%
  |                                                                            
  |=================================                                     |  46%
  |                                                                            
  |==================================                                    |  48%
  |                                                                            
  |===================================                                   |  49%
  |                                                                            
  |===================================                                   |  51%
  |                                                                            
  |====================================                                  |  52%
  |                                                                            
  |=====================================                                 |  54%
  |                                                                            
  |======================================                                |  55%
  |                                                                            
  |=======================================                               |  56%
  |                                                                            
  |========================================                              |  58%
  |                                                                            
  |=========================================                             |  59%
  |                                                                            
  |==========================================                            |  61%
  |                                                                            
  |===========================================                           |  62%
  |                                                                            
  |============================================                          |  63%
  |                                                                            
  |=============================================                         |  65%
  |                                                                            
  |==============================================                        |  66%
  |                                                                            
  |===============================================                       |  68%
  |                                                                            
  |================================================                      |  69%
  |                                                                            
  |=================================================                     |  70%
  |                                                                            
  |==================================================                    |  72%
  |                                                                            
  |===================================================                   |  73%
  |                                                                            
  |====================================================                  |  75%
  |                                                                            
  |=====================================================                 |  76%
  |                                                                            
  |======================================================                |  77%
  |                                                                            
  |=======================================================               |  79%
  |                                                                            
  |========================================================              |  80%
  |                                                                            
  |=========================================================             |  82%
  |                                                                            
  |==========================================================            |  83%
  |                                                                            
  |===========================================================           |  85%
  |                                                                            
  |============================================================          |  86%
  |                                                                            
  |=============================================================         |  87%
  |                                                                            
  |==============================================================        |  89%
  |                                                                            
  |===============================================================       |  90%
  |                                                                            
  |================================================================      |  92%
  |                                                                            
  |=================================================================     |  93%
  |                                                                            
  |==================================================================    |  94%
  |                                                                            
  |===================================================================   |  96%
  |                                                                            
  |====================================================================  |  97%
  |                                                                            
  |===================================================================== |  99%
  |                                                                            
  |======================================================================| 100%

Computing corrected count matrix for 18065 genes

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |=                                                                     |   1%
  |                                                                            
  |==                                                                    |   3%
  |                                                                            
  |===                                                                   |   4%
  |                                                                            
  |====                                                                  |   6%
  |                                                                            
  |=====                                                                 |   7%
  |                                                                            
  |======                                                                |   8%
  |                                                                            
  |=======                                                               |  10%
  |                                                                            
  |========                                                              |  11%
  |                                                                            
  |=========                                                             |  13%
  |                                                                            
  |==========                                                            |  14%
  |                                                                            
  |===========                                                           |  15%
  |                                                                            
  |============                                                          |  17%
  |                                                                            
  |=============                                                         |  18%
  |                                                                            
  |==============                                                        |  20%
  |                                                                            
  |===============                                                       |  21%
  |                                                                            
  |================                                                      |  23%
  |                                                                            
  |=================                                                     |  24%
  |                                                                            
  |==================                                                    |  25%
  |                                                                            
  |===================                                                   |  27%
  |                                                                            
  |====================                                                  |  28%
  |                                                                            
  |=====================                                                 |  30%
  |                                                                            
  |======================                                                |  31%
  |                                                                            
  |=======================                                               |  32%
  |                                                                            
  |========================                                              |  34%
  |                                                                            
  |=========================                                             |  35%
  |                                                                            
  |==========================                                            |  37%
  |                                                                            
  |===========================                                           |  38%
  |                                                                            
  |============================                                          |  39%
  |                                                                            
  |=============================                                         |  41%
  |                                                                            
  |==============================                                        |  42%
  |                                                                            
  |===============================                                       |  44%
  |                                                                            
  |================================                                      |  45%
  |                                                                            
  |=================================                                     |  46%
  |                                                                            
  |==================================                                    |  48%
  |                                                                            
  |===================================                                   |  49%
  |                                                                            
  |===================================                                   |  51%
  |                                                                            
  |====================================                                  |  52%
  |                                                                            
  |=====================================                                 |  54%
  |                                                                            
  |======================================                                |  55%
  |                                                                            
  |=======================================                               |  56%
  |                                                                            
  |========================================                              |  58%
  |                                                                            
  |=========================================                             |  59%
  |                                                                            
  |==========================================                            |  61%
  |                                                                            
  |===========================================                           |  62%
  |                                                                            
  |============================================                          |  63%
  |                                                                            
  |=============================================                         |  65%
  |                                                                            
  |==============================================                        |  66%
  |                                                                            
  |===============================================                       |  68%
  |                                                                            
  |================================================                      |  69%
  |                                                                            
  |=================================================                     |  70%
  |                                                                            
  |==================================================                    |  72%
  |                                                                            
  |===================================================                   |  73%
  |                                                                            
  |====================================================                  |  75%
  |                                                                            
  |=====================================================                 |  76%
  |                                                                            
  |======================================================                |  77%
  |                                                                            
  |=======================================================               |  79%
  |                                                                            
  |========================================================              |  80%
  |                                                                            
  |=========================================================             |  82%
  |                                                                            
  |==========================================================            |  83%
  |                                                                            
  |===========================================================           |  85%
  |                                                                            
  |============================================================          |  86%
  |                                                                            
  |=============================================================         |  87%
  |                                                                            
  |==============================================================        |  89%
  |                                                                            
  |===============================================================       |  90%
  |                                                                            
  |================================================================      |  92%
  |                                                                            
  |=================================================================     |  93%
  |                                                                            
  |==================================================================    |  94%
  |                                                                            
  |===================================================================   |  96%
  |                                                                            
  |====================================================================  |  97%
  |                                                                            
  |===================================================================== |  99%
  |                                                                            
  |======================================================================| 100%

Calculating gene attributes

Wall clock passed: Time difference of 16.86706 mins

Determine variable features

Set 5000 variable features

Place corrected count matrix in counts slot

Regressing out Batch, individual

Centering data matrix

Set default assay to SCT

#run PCA on full dataset pre-alignment
all.genes= rownames(merged)
merged<-FindVariableFeatures(merged,selection.method="vst", nfeatures = 5000)
#have previously used all genes (nfeatures=25000) and clustering by individual rather than batch (based on proportion of cells per cluster) was still observed downstream. Now using 5000 because it is the upper bound of what has been recommended in the literature.
merged<- ScaleData(merged, features = all.genes)

Centering and scaling data matrix

merged<-RunPCA(merged, npcs = 100, verbose=F)

DimPlot(merged, reduction = "pca", group.by = "Batch")

Now, running harmony to integrate. Here, using Batch, SampleID(10x Lane), and individual to integrate. Since Batch and Lane are confounded, this may over correct for Batch.

merged<- RunHarmony(merged, c("Batch", "individual"), plot_convergence = T, assay.use = "SCT")

Harmony 1/10

Harmony 2/10

Harmony 3/10

Harmony converged after 3 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

Visualize Harmony embeddings

DimPlot(merged, reduction="harmony", group.by= c("individual", "Batch"), combine=F)

[[1]]

[[2]]

Now Running UMAP and identifying clusters, etc

merged<- RunUMAP(merged, reduction = "harmony", dims = 1:100, 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

merged<- FindNeighbors(merged, reduction="harmony", dims = 1:100, verbose = F)
merged<- FindClusters(merged, resolution=1, verbose = F)
merged<- FindClusters(merged, resolution=0.8, verbose = F)
merged<- FindClusters(merged, resolution=0.5, verbose = F)
merged<- FindClusters(merged, resolution=0.1, verbose = F)

SAVING merged/aligned/reclustered object

path<- here::here("output/mergedObjects/")
saveRDS(merged, file=paste0(path,'SCTregress.Batchindividual.Harmony.Batchindividual.rds'))

#reassign idents
Idents(merged)<- 'SCT_snn_res.1'

VizDimLoadings(merged, dims = 1:2, reduction = "harmony")

VizDimLoadings(merged, dims = 3:4, reduction = "harmony")

VizDimLoadings(merged, dims = 5:6, reduction = "harmony")

xlim <- c(min(merged@reductions$harmony@cell.embeddings[,'harmony_1']),
          max(merged@reductions$harmony@cell.embeddings[,'harmony_1']))
ylim <- c(min(merged@reductions$harmony@cell.embeddings[,'harmony_2']),
          max(merged@reductions$harmony@cell.embeddings[,'harmony_2']))

individuals <- table(merged$individual)
individuals <- individuals[individuals>50]
individuals <- names(individuals)
for (i in individuals)
{
  print(DimPlot(merged, reduction = "harmony", group.by = c("Batch"), pt.size = 0.01,
                cells = WhichCells(merged, expression = individual == i)) +
          xlim(xlim) + ylim(ylim) + ggtitle(i))
}

DimPlot(merged, reduction = "umap")

DimPlot(merged, reduction = "umap", group.by = "Batch")

DimPlot(merged, reduction = "umap", group.by = "individual")

xlim <- c(min(merged@reductions$umap@cell.embeddings[,'UMAP_1']),
          max(merged@reductions$umap@cell.embeddings[,'UMAP_1']))
ylim <- c(min(merged@reductions$umap@cell.embeddings[,'UMAP_2']),
          max(merged@reductions$umap@cell.embeddings[,'UMAP_2']))
for (i in individuals)
{
  print(DimPlot(merged, reduction = "umap", 
                cells = WhichCells(merged, expression = individual == i)) +
          xlim(xlim) + ylim(ylim) + ggtitle(i))
}

plots2<- DimPlot(merged, group.by = "individual", split.by = "Batch")
plots2

DimPlot(merged, group.by = "Batch", split.by = c("individual"))

DimPlot(merged, group.by = "SCT_snn_res.1", split.by = c("Batch"), label=T)

DimPlot(merged, reduction = "harmony", group.by = "SCT_snn_res.1", split.by = "Batch", combine = F)

[[1]]

VlnPlot(merged, features = c("POU5F1", "PAX6", "TNNT2", "SOX17", "HAND1", "LUM"), ncol=2)

#pluripotent markers
FeaturePlot(merged, features = c("POU5F1", "SOX2", "NANOG"), pt.size = 0.2, ncol=3)

#Endoderm markers (first 3 definitive endo, 4-6 liver markers, )
FeaturePlot(merged, features = c("SOX17","CLDN6","FOXA2", "TTR", "AFP", "FGB"), pt.size = 0.2, combine = F)

[[1]]

[[2]]

[[3]]

[[4]]

[[5]]

[[6]]

#Mesoderm Markers (first 3 early meso markers, 4-6 heart markers, 7-9 endothelial markers (which comes from mesoderm), then some other general muscle markers)
FeaturePlot(merged, features = c("HAND1", "BMP4", "TNNT2","KDR", "GNG11", "ECSCR", "COL3A1", "ACTC1"), pt.size = 0.2, combine=F)

[[1]]

[[2]]

[[3]]

[[4]]

[[5]]

[[6]]

[[7]]

[[8]]

#Ectoderm Markers (3-1 early ectoderm markers, 4-6schwann cell (myelinating, non myelinating, or precursor), 7-8 oligodendrocytes, 9-10 radial glia)
FeaturePlot(merged, features = c("PAX6", "GBX2",  "NES", "MPZ", "SOX10","GAP43", "OLIG1", "OLIG2", "VIM", "HES5"), pt.size = 0.2, ncol=3, combine=F)

[[1]]

[[2]]

[[3]]

[[4]]

[[5]]

[[6]]

[[7]]

[[8]]

[[9]]

[[10]]

#More ectoderm, specifically neurons
#immature neurons: NEUROD1
#Mature Neurons: MAP2, SYP
#dopaminergic: TH, FOXA2,
FeaturePlot(merged, features = c("MAP2", "SYP","NEUROD1", "TH" ), pt.size = 0.2, ncol=3)

Identify cluster markers

#how many cells per cluster?
t1<-table(merged@meta.data$SCT_snn_res.1, merged@meta.data$Batch)
t1

    
     Batch1 Batch2 Batch3
  0    3386   1805   2758
  1    2178   1180   1055
  10    533    332    342
  11    471    142    551
  12    613    215    321
  13    165    260    496
  14    334    207    321
  15    384    138    280
  16     17     49    667
  17    264    244    211
  18     37     81    594
  19    104    120    371
  2    2017    896    935
  20    289    169     39
  21     97     75    266
  22    233     62     98
  23    104     59    135
  24    107     36    111
  25    100     10     25
  3    1248    928   1566
  4    1359    437    858
  5    1206    407    936
  6     472    954    882
  7     378    322    693
  8     247    279    855
  9     530    458    384

#percent of cells in each cluster per batch
t1colsum<- colSums(t1)
percT1<-t1/t1colsum
percT1

    
           Batch1       Batch2       Batch3
  0  0.2006756356 0.1146031746 0.2795742524
  1  0.2207805373 0.0699342144 0.0669841270
  10 0.0338412698 0.0336543335 0.0202690689
  11 0.0279144195 0.0090158730 0.0558540294
  12 0.0621388748 0.0127422509 0.0203809524
  13 0.0104761905 0.0263558033 0.0293960766
  14 0.0197949387 0.0131428571 0.0325392803
  15 0.0389254942 0.0081787471 0.0177777778
  16 0.0010793651 0.0049670552 0.0395306110
  17 0.0156462988 0.0154920635 0.0213887481
  18 0.0037506336 0.0048005690 0.0377142857
  19 0.0066031746 0.0121642169 0.0219877911
  2  0.1195400936 0.0568888889 0.0947795236
  20 0.0292954891 0.0100160019 0.0024761905
  21 0.0061587302 0.0076026356 0.0157648314
  22 0.0138090440 0.0039365079 0.0099341105
  23 0.0105423213 0.0034967107 0.0085714286
  24 0.0067936508 0.0036492651 0.0065785575
  25 0.0059266283 0.0006349206 0.0025342119
  3  0.1265078561 0.0549991110 0.0994285714
  4  0.0862857143 0.0442980233 0.0508504712
  5  0.0714751378 0.0258412698 0.0948808920
  6  0.0478459199 0.0565400344 0.0560000000
  7  0.0240000000 0.0326406488 0.0410715344
  8  0.0146387720 0.0177142857 0.0866700456
  9  0.0537252914 0.0271439578 0.0243809524

heatmap(t(percT1))

#how many cells per cluster from each individual?
t2<-table(merged@meta.data$SCT_snn_res.1, merged@meta.data$individual)
t2

    
     SNG-NA18511 SNG-NA18858 SNG-NA19160
  0          282        7549         118
  1         2985         166        1262
  10         113         999          95
  11          89          10        1065
  12         139          21         989
  13         403          99         419
  14         591          36         235
  15          61         714          27
  16         480           7         246
  17         440          59         220
  18         406           6         300
  19         155          14         426
  2         1825         316        1707
  20         127         356          14
  21         123           8         307
  22          82         133         178
  23          90           4         204
  24          23         220          11
  25           0         135           0
  3          239        3393         110
  4          820          36        1798
  5          281          37        2231
  6          126        2020         162
  7          651         201         541
  8          525          39         817
  9          893          92         387

t2colsums<-colSums(t2)

percT2<- t2/t2colsums

percT2

    
      SNG-NA18511  SNG-NA18858  SNG-NA19160
  0  0.0236003013 0.5443074483 0.0070785843
  1  0.1790641872 0.0138923759 0.0909943038
  10 0.0081476675 0.0599280144 0.0079504561
  11 0.0074483220 0.0007210325 0.0638872226
  12 0.0083383323 0.0017574692 0.0713101161
  13 0.0290576105 0.0059388122 0.0350656959
  14 0.0494602059 0.0025957171 0.0140971806
  15 0.0036592681 0.0597539543 0.0019467878
  16 0.0346095609 0.0004199160 0.0205874969
  17 0.0368231651 0.0042540919 0.0131973605
  18 0.0243551290 0.0005021341 0.0216309756
  19 0.0111760040 0.0008398320 0.0356515190
  2  0.1527324462 0.0227846276 0.1023995201
  20 0.0076184763 0.0297932881 0.0010094455
  21 0.0088687000 0.0004799040 0.0256925266
  22 0.0068624990 0.0095897325 0.0106778644
  23 0.0053989202 0.0003347560 0.0147090634
  24 0.0016583748 0.0131973605 0.0009205791
  25 0.0000000000 0.0097339390 0.0000000000
  3  0.0143371326 0.2839568165 0.0079313577
  4  0.0591246665 0.0021595681 0.1504728429
  5  0.0235166123 0.0026678203 0.1338332334
  6  0.0075584883 0.1690518035 0.0116807268
  7  0.0469392170 0.0120575885 0.0452757553
  8  0.0439367311 0.0028120268 0.0490101980
  9  0.0535692861 0.0076993891 0.0279039585

heatmap(t(percT2))

cormat<-round(cor(percT2),2)
library(reshape2)
melted_cormat<-melt(cormat)
ugly<-ggplot(data= melted_cormat, aes(x=Var1, y=Var2, fill=value)) +
  geom_tile() +
  ggtitle("Pairwise Pearson Correlation of the percent of cells from \neach cell line assigned to each Seurat Cluster")

get_lower_tri<- function(cormat){
  cormat[upper.tri(cormat)]<-NA
  return(cormat)
}

lower_tri<- get_lower_tri(cormat)
melted_tri<- melt(lower_tri)
pretty<-ggplot(data= melted_tri, aes(x=Var1, y=Var2, fill=value)) +
  geom_tile(color="white") +
  scale_fill_gradient2(low="blue", high="red", mid="white", midpoint = 0, limit= c(-1,1), space= "Lab", name="Pearson\nCorrelation") +
  theme_minimal() +
  ggtitle("Pairwise Pearson Correlation of the percent of cells from \neach cell line assigned to each Seurat Cluster")

pretty

#exploring similarity in the number of cells per individual between batches
merged.Batch1<- (subset(merged, Batch == "Batch1"))
b1t<- table(merged.Batch1$SCT_snn_res.1, merged.Batch1$individual)
remove("merged.Batch1")
b1tcolsums<- colSums(b1t)
percb1t<- b1t/b1tcolsums                

merged.Batch2<- (subset(merged, Batch == "Batch2"))
b2t<- table(merged.Batch2$SCT_snn_res.1, merged.Batch2$individual)
remove("merged.Batch2")
b2tcolsums<- colSums(b2t)
percb2t<- b2t/b2tcolsums

merged.Batch3<- (subset(merged, Batch == "Batch3"))
b3t<- table(merged.Batch3$SCT_snn_res.1, merged.Batch3$individual)
remove("merged.Batch3")
b3tcolsums<- colSums(b3t)
percb3t<- b3t/b3tcolsums

cols1<- c("Batch1_18511","Batch1_18858","Batch1_19160", "Batch2_18511", "Batch2_18858","Batch2_19160",
         "Batch3_18511","Batch3_18858", "Batch3_19160")

cols2<- c("Batch1_18511", "Batch2_18511", "Batch3_18511","Batch1_18858", "Batch2_18858", "Batch3_18858","Batch1_19160", "Batch2_19160", "Batch3_19160")

fullpercs<- as.data.frame(cbind(percb1t[,1:3], percb2t,percb3t))
colnames(fullpercs)<-cols1
fullpercs<- cbind(fullpercs$Batch1_18511, fullpercs$Batch2_18511, fullpercs$Batch3_18511,
                  fullpercs$Batch1_18858, fullpercs$Batch2_18858, fullpercs$Batch3_18858,
                  fullpercs$Batch1_19160, fullpercs$Batch2_19160, fullpercs$Batch3_19160)
colnames(fullpercs)<-cols2
fullpercs_cor<- round(cor(fullpercs),2)
fullpercs_melt<- melt(fullpercs_cor)
ggplot(data= fullpercs_melt, aes(x=Var1, y=Var2, fill=value)) +
  geom_tile(color="white") +
  scale_fill_gradient2(low="blue", high="red", mid="white", midpoint = 0, limit= c(-1,1), space= "Lab", name="Pearson\nCorrelation") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 30, hjust=1))+
  ggtitle("Pairwise Pearson Correlation of the percent of cells from \neach cell line assigned to each Seurat Cluster (res 1)")

#now clustering individual_Batch samples with hierarchical clustering/they will get reordered based on similarity

beauty<- colorRampPalette(brewer.pal(9,"Purples"))(200)

rownames(fullpercs)<- c(0:(nrow(fullpercs)-1))

heatmap(as.matrix(fullpercs), scale="none", col=beauty, cexCol = .7, cexRow=.6)
text(1:ncol(fullpercs),labels=names(fullpercs),srt=30)

#generate a heatmap of the proportion of cells from each individual_batch in each seurat cluster. dendrograms based on similarity of the vectors. should be colored by the value(proportion), but some of the cluster/sample values to seem to match with the color

Reclustering with less resolution, check if everything is robust

#reassign idents
Idents(merged)<- 'SCT_snn_res.0.5'

DimPlot(merged, reduction = "umap")

DimPlot(merged, reduction = "umap", group.by = "Batch")

DimPlot(merged, reduction = "umap", group.by = "individual")

xlim <- c(min(merged@reductions$umap@cell.embeddings[,'UMAP_1']),
          max(merged@reductions$umap@cell.embeddings[,'UMAP_1']))
ylim <- c(min(merged@reductions$umap@cell.embeddings[,'UMAP_2']),
          max(merged@reductions$umap@cell.embeddings[,'UMAP_2']))
for (i in individuals)
{
  print(DimPlot(merged, reduction = "umap", 
                cells = WhichCells(merged, expression = individual == i)) +
          xlim(xlim) + ylim(ylim) + ggtitle(i))
}

#exploring similarity in the number of cells per individual between batches
merged.Batch1<- (subset(merged, Batch == "Batch1"))
b1t<- table(merged.Batch1$SCT_snn_res.0.5, merged.Batch1$individual)
remove("merged.Batch1")
b1tcolsums<- colSums(b1t)
percb1t<- b1t/b1tcolsums                

merged.Batch2<- (subset(merged, Batch == "Batch2"))
b2t<- table(merged.Batch2$SCT_snn_res.0.5, merged.Batch2$individual)
remove("merged.Batch2")
b2tcolsums<- colSums(b2t)
percb2t<- b2t/b2tcolsums

merged.Batch3<- (subset(merged, Batch == "Batch3"))
b3t<- table(merged.Batch3$SCT_snn_res.0.5, merged.Batch3$individual)
remove("merged.Batch3")
b3tcolsums<- colSums(b3t)
percb3t<- b3t/b3tcolsums

cols1<- c("Batch1_18511","Batch1_18858","Batch1_19160", "Batch2_18511", "Batch2_18858","Batch2_19160",
         "Batch3_18511","Batch3_18858", "Batch3_19160")

cols2<- c("Batch1_18511", "Batch2_18511", "Batch3_18511","Batch1_18858", "Batch2_18858", "Batch3_18858","Batch1_19160", "Batch2_19160", "Batch3_19160")

fullpercs<- as.data.frame(cbind(percb1t[,1:3], percb2t,percb3t))
colnames(fullpercs)<-cols1
fullpercs<- cbind(fullpercs$Batch1_18511, fullpercs$Batch2_18511, fullpercs$Batch3_18511,
                  fullpercs$Batch1_18858, fullpercs$Batch2_18858, fullpercs$Batch3_18858,
                  fullpercs$Batch1_19160, fullpercs$Batch2_19160, fullpercs$Batch3_19160)
colnames(fullpercs)<-cols2
fullpercs_cor<- round(cor(fullpercs),2)
fullpercs_melt<- melt(fullpercs_cor)
ggplot(data= fullpercs_melt, aes(x=Var1, y=Var2, fill=value)) +
  geom_tile(color="white") +
  scale_fill_gradient2(low="blue", high="red", mid="white", midpoint = 0, limit= c(-1,1), space= "Lab", name="Pearson\nCorrelation") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 30, hjust=1))+
  ggtitle("Pairwise Pearson Correlation of the percent of cells from \neach cell line assigned to each Seurat Cluster\ncluster res. 0.5")

#now clustering individual_Batch samples with hierarchical clustering/they will get reordered based on similarity

beauty<- colorRampPalette(brewer.pal(9,"Purples"))(200)

rownames(fullpercs)<- c(0:(nrow(fullpercs)-1))

heatmap(as.matrix(fullpercs), scale="none", col=beauty, cexCol = .7, cexRow=.6)
text(1:ncol(fullpercs),labels=names(fullpercs),srt=30)

#generate a heatmap of the raw proportion of cells from each individual_batch in each seurat cluster. dendrograms based on similarity of the vectors. should be colored by the value(proportion), but some of the cluster/sample values to seem to match with the color

#reassign idents
Idents(merged)<- 'SCT_snn_res.0.1'

DimPlot(merged, reduction = "umap")

DimPlot(merged, reduction = "umap", group.by = "Batch")

DimPlot(merged, reduction = "umap", group.by = "individual")

xlim <- c(min(merged@reductions$umap@cell.embeddings[,'UMAP_1']),
          max(merged@reductions$umap@cell.embeddings[,'UMAP_1']))
ylim <- c(min(merged@reductions$umap@cell.embeddings[,'UMAP_2']),
          max(merged@reductions$umap@cell.embeddings[,'UMAP_2']))
for (i in individuals)
{
  print(DimPlot(merged, reduction = "umap", 
                cells = WhichCells(merged, expression = individual == i)) +
          xlim(xlim) + ylim(ylim) + ggtitle(i))
}

#exploring similarity in the number of cells per individual between batches
merged.Batch1<- (subset(merged, Batch == "Batch1"))
b1t<- table(merged.Batch1$SCT_snn_res.0.1, merged.Batch1$individual)
remove("merged.Batch1")
b1tcolsums<- colSums(b1t)
percb1t<- b1t/b1tcolsums                

merged.Batch2<- (subset(merged, Batch == "Batch2"))
b2t<- table(merged.Batch2$SCT_snn_res.0.1, merged.Batch2$individual)
remove("merged.Batch2")
b2tcolsums<- colSums(b2t)
percb2t<- b2t/b2tcolsums

merged.Batch3<- (subset(merged, Batch == "Batch3"))
b3t<- table(merged.Batch3$SCT_snn_res.0.1, merged.Batch3$individual)
remove("merged.Batch3")
b3tcolsums<- colSums(b3t)
percb3t<- b3t/b3tcolsums

cols1<- c("Batch1_18511","Batch1_18858","Batch1_19160", "Batch2_18511", "Batch2_18858","Batch2_19160",
         "Batch3_18511","Batch3_18858", "Batch3_19160")

cols2<- c("Batch1_18511", "Batch2_18511", "Batch3_18511","Batch1_18858", "Batch2_18858", "Batch3_18858","Batch1_19160", "Batch2_19160", "Batch3_19160")

fullpercs<- as.data.frame(cbind(percb1t[,1:3], percb2t,percb3t))
colnames(fullpercs)<-cols1
fullpercs<- cbind(fullpercs$Batch1_18511, fullpercs$Batch2_18511, fullpercs$Batch3_18511,
                  fullpercs$Batch1_18858, fullpercs$Batch2_18858, fullpercs$Batch3_18858,
                  fullpercs$Batch1_19160, fullpercs$Batch2_19160, fullpercs$Batch3_19160)
colnames(fullpercs)<-cols2
fullpercs_cor<- round(cor(fullpercs),2)
fullpercs_melt<- melt(fullpercs_cor)
ggplot(data= fullpercs_melt, aes(x=Var1, y=Var2, fill=value)) +
  geom_tile(color="white") +
  scale_fill_gradient2(low="blue", high="red", mid="white", midpoint = 0, limit= c(-1,1), space= "Lab", name="Pearson\nCorrelation") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 30, hjust=1))+
  ggtitle("Pairwise Pearson Correlation of the percent of cells from \neach cell line assigned to each Seurat Cluster\ncluster res. 0.1")

#now clustering individual_Batch samples with hierarchical clustering/they will get reordered based on similarity

beauty<- colorRampPalette(brewer.pal(9,"Purples"))(200)
#fullnonorm<- as.data.frame(cbind(b1t[,1:3], b2t,b3t))
#colnames(fullnonorm)<-cols1
#heatmap((as.matrix(fullnonorm)), scale="column", col= beauty)

rownames(fullpercs)<- c(0:(nrow(fullpercs)-1))

heatmap(as.matrix(fullpercs), scale="none", col=beauty, cexCol = .7, cexRow=.6)
text(1:ncol(fullpercs),labels=names(fullpercs),srt=30)

#generate a heatmap of the raw proportion of cells from each individual_batch in each seurat cluster. dendrograms based on similarity of the vectors. should be colored by the value(proportion), but some of the cluster/sample values to seem to match with the color

#reassign idents
Idents(merged)<- 'SCT_snn_res.0.8'

DimPlot(merged, reduction = "umap")

DimPlot(merged, reduction = "umap", group.by = "Batch")

DimPlot(merged, reduction = "umap", group.by = "individual")

xlim <- c(min(merged@reductions$umap@cell.embeddings[,'UMAP_1']),
          max(merged@reductions$umap@cell.embeddings[,'UMAP_1']))
ylim <- c(min(merged@reductions$umap@cell.embeddings[,'UMAP_2']),
          max(merged@reductions$umap@cell.embeddings[,'UMAP_2']))
for (i in individuals)
{
  print(DimPlot(merged, reduction = "umap", 
                cells = WhichCells(merged, expression = individual == i)) +
          xlim(xlim) + ylim(ylim) + ggtitle(i))
}

#exploring similarity in the number of cells per individual between batches
merged.Batch1<- (subset(merged, Batch == "Batch1"))
b1t<- table(merged.Batch1$SCT_snn_res.0.8, merged.Batch1$individual)
remove("merged.Batch1")
b1tcolsums<- colSums(b1t)
percb1t<- b1t/b1tcolsums                

merged.Batch2<- (subset(merged, Batch == "Batch2"))
b2t<- table(merged.Batch2$SCT_snn_res.0.8, merged.Batch2$individual)
remove("merged.Batch2")
b2tcolsums<- colSums(b2t)
percb2t<- b2t/b2tcolsums

merged.Batch3<- (subset(merged, Batch == "Batch3"))
b3t<- table(merged.Batch3$SCT_snn_res.0.8, merged.Batch3$individual)
remove("merged.Batch3")
b3tcolsums<- colSums(b3t)
percb3t<- b3t/b3tcolsums

cols1<- c("Batch1_18511","Batch1_18858","Batch1_19160", "Batch2_18511", "Batch2_18858","Batch2_19160",
         "Batch3_18511","Batch3_18858", "Batch3_19160")

cols2<- c("Batch1_18511", "Batch2_18511", "Batch3_18511","Batch1_18858", "Batch2_18858", "Batch3_18858","Batch1_19160", "Batch2_19160", "Batch3_19160")

fullpercs<- as.data.frame(cbind(percb1t[,1:3], percb2t,percb3t))
colnames(fullpercs)<-cols1
fullpercs<- cbind(fullpercs$Batch1_18511, fullpercs$Batch2_18511, fullpercs$Batch3_18511,
                  fullpercs$Batch1_18858, fullpercs$Batch2_18858, fullpercs$Batch3_18858,
                  fullpercs$Batch1_19160, fullpercs$Batch2_19160, fullpercs$Batch3_19160)
colnames(fullpercs)<-cols2
fullpercs_cor<- round(cor(fullpercs),2)
fullpercs_melt<- melt(fullpercs_cor)
ggplot(data= fullpercs_melt, aes(x=Var1, y=Var2, fill=value)) +
  geom_tile(color="white") +
  scale_fill_gradient2(low="blue", high="red", mid="white", midpoint = 0, limit= c(-1,1), space= "Lab", name="Pearson\nCorrelation") +
  theme_minimal() +
  ggtitle("Pairwise Pearson Correlation of the percent of cells from \neach cell line assigned to each Seurat Cluster\ncluster res. 0.8")

#now clustering individual_Batch samples with hierarchical clustering/they will get reordered based on similarity

beauty<- colorRampPalette(brewer.pal(9,"Purples"))(200)

rownames(fullpercs)<- c(0:(nrow(fullpercs)-1))

heatmap(as.matrix(fullpercs), scale="none", col=beauty, cexCol = .7, cexRow=.6)
text(1:ncol(fullpercs),labels=names(fullpercs),srt=30)

#generate a heatmap of the raw proportion of cells from each individual_batch in each seurat cluster. dendrograms based on similarity of the vectors. should be colored by the value(proportion), but some of the cluster/sample values to seem to match with the color

VlnPlot(merged, features= "percent.mt", group.by = "SCT_snn_res.1", pt.size = 0)

merged[["percent.rps"]]<- PercentageFeatureSet(merged, pattern = "^RPS")
merged[["percent.rpl"]]<- PercentageFeatureSet(merged, pattern = "^RPL")
merged[["percent.rp"]]<- merged[["percent.rps"]]+merged[["percent.rpl"]]
VlnPlot(merged, features= "percent.rp", group.by = "SCT_snn_res.1", pt.size=0)

FeaturePlot(merged, features = "nFeature_RNA")

head(merged)

An object of class Seurat 
2 features across 42488 samples within 2 assays 
Active assay: SCT (1 features, 1 variable features)
 1 other assay present: RNA
 3 dimensional reductions calculated: pca, harmony, umap

VlnPlot(merged, features= "nFeature_RNA", group.by = "SCT_snn_res.1", pt.size=0)

FeaturePlot(merged, features = c("POU5F1", "SOX17",  "HAND1", "PAX6"), pt.size = 0.2, ncol=2, combine=T)

FeaturePlot(merged, features = c("FGB", "ECSCR",  "NEUROD1", "SOX10"), pt.size = 0.2, ncol=2)

Session information

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] reshape2_1.4.4     RColorBrewer_1.1-2 here_0.1-11        DataCombine_0.2.21
[5] ggplot2_3.3.2      harmony_1.0        Rcpp_1.0.5         Seurat_3.2.0      
[9] workflowr_1.6.2   

loaded via a namespace (and not attached):
  [1] Rtsne_0.15            colorspace_1.4-1      deldir_0.1-28        
  [4] ellipsis_0.3.1        ggridges_0.5.2        rprojroot_1.3-2      
  [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_0.8.2         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_1.7.0        ica_1.0-2            
 [22] cluster_2.1.0         png_0.1-7             uwot_0.1.8           
 [25] shiny_1.5.0           sctransform_0.2.1     compiler_3.6.1       
 [28] httr_1.4.2            backports_1.1.8       Matrix_1.2-18        
 [31] fastmap_1.0.1         lazyeval_0.2.2        later_1.1.0.1        
 [34] htmltools_0.5.0       tools_3.6.1           rsvd_1.0.3           
 [37] igraph_1.2.5          gtable_0.3.0          glue_1.4.1           
 [40] RANN_2.6.1            dplyr_1.0.0           rappdirs_0.3.1       
 [43] spatstat_1.64-1       vctrs_0.3.2           gdata_2.18.0         
 [46] ape_5.3               nlme_3.1-140          lmtest_0.9-37        
 [49] xfun_0.16             stringr_1.4.0         globals_0.12.5       
 [52] mime_0.9              miniUI_0.1.1.1        lifecycle_0.2.0      
 [55] irlba_2.3.3           gtools_3.8.2          goftest_1.2-2        
 [58] future_1.18.0         MASS_7.3-51.4         zoo_1.8-8            
 [61] scales_1.1.1          promises_1.1.1        spatstat.utils_1.17-0
 [64] parallel_3.6.1        yaml_2.2.1            reticulate_1.16      
 [67] pbapply_1.4-2         gridExtra_2.3         rpart_4.1-15         
 [70] stringi_1.4.6         caTools_1.18.0        rlang_0.4.7          
 [73] pkgconfig_2.0.3       bitops_1.0-6          evaluate_0.14        
 [76] lattice_0.20-38       ROCR_1.0-7            purrr_0.3.4          
 [79] tensor_1.5            labeling_0.3          patchwork_1.0.1      
 [82] htmlwidgets_1.5.1     cowplot_1.0.0         tidyselect_1.1.0     
 [85] RcppAnnoy_0.0.16      plyr_1.8.6            magrittr_1.5         
 [88] R6_2.4.1              gplots_3.0.4          generics_0.0.2       
 [91] withr_2.2.0           pillar_1.4.6          whisker_0.4          
 [94] mgcv_1.8-28           fitdistrplus_1.0-14   survival_3.2-3       
 [97] abind_1.4-5           tibble_3.0.3          future.apply_1.6.0   
[100] crayon_1.3.4          KernSmooth_2.23-15    plotly_4.9.2.1       
[103] rmarkdown_2.3         grid_3.6.1            data.table_1.13.0    
[106] git2r_0.26.1          digest_0.6.25         xtable_1.8-4         
[109] tidyr_1.1.0           httpuv_1.5.4          munsell_0.5.0        
[112] viridisLite_0.3.0