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### Running command:
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###   /home/biocbuild/bbs-3.11-bioc/R/bin/R CMD check --install=check:EBarrays.install-out.txt --library=/home/biocbuild/bbs-3.11-bioc/R/library --no-vignettes --timings EBarrays_2.52.0.tar.gz
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* using log directory ‘/home/biocbuild/bbs-3.11-bioc/meat/EBarrays.Rcheck’
* using R version 4.0.3 (2020-10-10)
* using platform: x86_64-pc-linux-gnu (64-bit)
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘EBarrays/DESCRIPTION’ ... OK
* this is package ‘EBarrays’ version ‘2.52.0’
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘EBarrays’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... NOTE
Packages listed in more than one of Depends, Imports, Suggests, Enhances:
  ‘Biobase’ ‘lattice’ ‘methods’
A package should be listed in only one of these fields.
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... NOTE
'library' or 'require' call to ‘lattice’ which was already attached by Depends.
  Please remove these calls from your code.
* checking S3 generic/method consistency ... NOTE
Found the following apparent S3 methods exported but not registered:
  plot.ebarraysEMfit
See section ‘Registering S3 methods’ in the ‘Writing R Extensions’
manual.
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... NOTE
prepare_Rd: crit.fun.Rd:51: Dropping empty section \keyword
prepare_Rd: ebplots.Rd:116-117: Dropping empty section \examples
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking compiled code ... NOTE
Note: information on .o files is not available
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘test.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes in ‘inst/doc’ ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 5 NOTEs
See
  ‘/home/biocbuild/bbs-3.11-bioc/meat/EBarrays.Rcheck/00check.log’
for details.

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### Running command:
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###   /home/biocbuild/bbs-3.11-bioc/R/bin/R CMD INSTALL EBarrays
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* installing to library ‘/home/biocbuild/bbs-3.11-bioc/R/library’
* installing *source* package ‘EBarrays’ ...
** using staged installation
** libs
gcc -I"/home/biocbuild/bbs-3.11-bioc/R/include" -DNDEBUG   -I/usr/local/include   -fpic  -g -O2  -Wall -c ebarrays.c -o ebarrays.o
gcc -shared -L/home/biocbuild/bbs-3.11-bioc/R/lib -L/usr/local/lib -o EBarrays.so ebarrays.o -L/home/biocbuild/bbs-3.11-bioc/R/lib -lR
installing to /home/biocbuild/bbs-3.11-bioc/R/library/00LOCK-EBarrays/00new/EBarrays/libs
** R
** data
** demo
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** checking absolute paths in shared objects and dynamic libraries
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (EBarrays)

R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out"
Copyright (C) 2020 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(EBarrays)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

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

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
    tapply, union, unique, unsplit, which, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: lattice
> demo(ebarrays)


	demo(ebarrays)
	---- ~~~~~~~~

> library(EBarrays)

> ## EM algorithm 
> ## Lognormal-Normal Demo
> 
> ## mu10,sigma2,tau are parameters in the LNNB model; pde is the
> ## proportion of differentially expressed genes; n is the
> ## total number of genes; nr1 and nr2 are the number of replicate
> ## arrays in each group.
> 
> lnnb.sim <- function(mu10, sigmasq, tausq, pde, n, nr1, nr2)
+ {
+     de <- sample(c(TRUE, FALSE), size = n, replace = TRUE, prob = c(pde, 1 - pde))
+     x <- matrix(NA, n, nr1)
+     y <- matrix(NA, n, nr2)
+     mu1 <- rnorm(n, mu10, sqrt(tausq))
+     mu2.de <- rnorm(n, mu10, sqrt(tausq))
+     mu2 <- mu1
+     mu2[de] <- mu2.de[de]
+     for(j in 1:nr1) {
+         x[, j] <- rnorm(n, mu1, sqrt(sigmasq))
+     }
+     for(j in 1:nr2) {
+         y[, j] <- rnorm(n, mu2, sqrt(sigmasq))
+     }
+     outmat <- exp(cbind(x, y))
+     list(mu1 = mu1, mu2 = mu2, outmat = outmat, de = de)
+ }

> ## simulating data with
> ##  mu_0 = 2.33, sigma^2 = 0.1, tau^2 = 2
> ##  P(DE) = 0.2
> 
> sim.data1 <- lnnb.sim(2.33, 0.1, 2, 0.2, 2000, nr1 = 3, nr2 = 3)

> de.true1 <- sim.data1$de ## true indicators of differential expression

> sim.data2 <- lnnb.sim(1.33, 0.01, 2, 0.2, 2000, nr1 = 3, nr2 = 3)

> de.true2 <- sim.data2$de ## true indicators of differential expression

> testdata <- rbind(sim.data1$outmat,sim.data2$outmat)

> hypotheses <- ebPatterns(c("1 1 1 1 1 1", "1 1 1 2 2 2")) 

> em.out <- emfit(testdata, family = "LNN", hypotheses,
+                 cluster = 1:5,
+                 type = 2,
+                 verbose = TRUE,
+                 num.iter = 10)

 Checking for negative entries...
 Checking for negative entries...
 Generating summary statistics for patterns. 
 This may take a few seconds...

 Starting EM iterations (total 10 ).
 This may take a while

	 Starting iteration 1 ...
	 Starting iteration 2 ...
	 Starting iteration 3 ...
	 Starting iteration 4 ...
	 Starting iteration 5 ...
	 Starting iteration 6 ...
	 Starting iteration 7 ...
	 Starting iteration 8 ...
	 Starting iteration 9 ...
	 Starting iteration 10 ...


 Fit used  0.61 seconds user time

 Checking for negative entries...
 Generating summary statistics for patterns. 
 This may take a few seconds...

 Starting EM iterations (total 10 ).
 This may take a while

	 Starting iteration 1 ...
	 Starting iteration 2 ...
	 Starting iteration 3 ...
	 Starting iteration 4 ...
	 Starting iteration 5 ...
	 Starting iteration 6 ...
	 Starting iteration 7 ...
	 Starting iteration 8 ...
	 Starting iteration 9 ...
	 Starting iteration 10 ...


 Fit used  1.50 seconds user time

 Checking for negative entries...
 Generating summary statistics for patterns. 
 This may take a few seconds...

 Starting EM iterations (total 10 ).
 This may take a while

	 Starting iteration 1 ...
	 Starting iteration 2 ...
	 Starting iteration 3 ...
	 Starting iteration 4 ...
	 Starting iteration 5 ...
	 Starting iteration 6 ...
	 Starting iteration 7 ...
	 Starting iteration 8 ...
	 Starting iteration 9 ...
	 Starting iteration 10 ...


 Fit used  1.85 seconds user time

 Checking for negative entries...
 Generating summary statistics for patterns. 
 This may take a few seconds...

 Starting EM iterations (total 10 ).
 This may take a while

	 Starting iteration 1 ...
	 Starting iteration 2 ...
	 Starting iteration 3 ...
	 Starting iteration 4 ...
	 Starting iteration 5 ...
	 Starting iteration 6 ...
	 Starting iteration 7 ...
	 Starting iteration 8 ...
	 Starting iteration 9 ...
	 Starting iteration 10 ...


 Fit used  2.22 seconds user time

 Checking for negative entries...
 Generating summary statistics for patterns. 
 This may take a few seconds...

 Starting EM iterations (total 10 ).
 This may take a while

	 Starting iteration 1 ...
	 Starting iteration 2 ...
	 Starting iteration 3 ...
	 Starting iteration 4 ...
	 Starting iteration 5 ...
	 Starting iteration 6 ...
	 Starting iteration 7 ...
	 Starting iteration 8 ...
	 Starting iteration 9 ...
	 Starting iteration 10 ...


 Fit used  2.47 seconds user time

> em.out

 EB model fit 
	 Family: LNN ( Lognormal-Normal )

 Model parameter estimates:

              mu_0     sigma.2  tao_0.2
Cluster 1 2.345963 0.098858356 1.960199
Cluster 2 1.315349 0.009781337 1.957951

 Estimated mixing proportions:

          Pattern.1  Pattern.2
Cluster 1 0.3963502 0.10289343
Cluster 2 0.4024952 0.09826116

> post.out <- postprob(em.out, testdata)

> table(post.out$pattern[, 2] > .5, c(de.true1,de.true2))
       
        FALSE TRUE
  FALSE  3178  139
  TRUE     41  642

> table((post.out$cluster[, 2] > .5)+1, c(rep("Cluster 1",2000),rep("Cluster 2",2000)))
   
    Cluster 1 Cluster 2
  1      1856        73
  2       144      1927

> plotMarginal(em.out,testdata)

> par(ask=TRUE)

> plotCluster(em.out,testdata)

> par(ask=FALSE)

> lnnmv.em.out <- emfit(testdata, family = "LNNMV", hypotheses, groupid=c(1,1,1,2,2,2),
+                 verbose = TRUE,
+                 num.iter = 10,
+                 p.init = c(0.95, 0.05))

 Checking for negative entries...
 Generating summary statistics for patterns. 
 This may take a few seconds...

 Starting EM iterations (total 10 ).
 This may take a while

	 Starting iteration 1 ...
	 Starting iteration 2 ...
	 Starting iteration 3 ...
	 Starting iteration 4 ...
	 Starting iteration 5 ...
	 Starting iteration 6 ...
	 Starting iteration 7 ...
	 Starting iteration 8 ...
	 Starting iteration 9 ...
	 Starting iteration 10 ...


 Fit used  0.59 seconds user time

> lnnmv.em.out

 EB model fit 
	 Family: LNNMV ( Lognormal-Normal with modified variances )

 Model parameter estimates:

      mu_0  tao_0.2
1 1.837928 2.227111

 Estimated mixing proportions:

       Pattern.1 Pattern.2
p.temp 0.7839558 0.2160442

> post.out <- postprob(lnnmv.em.out, testdata, groupid=c(1,1,1,2,2,2))

> table(post.out$pattern[, 2] > .5, c(de.true1,de.true2))
       
        FALSE TRUE
  FALSE  3124  133
  TRUE     95  648
There were 50 or more warnings (use warnings() to see the first 50)
> 
> 
> 
> proc.time()
   user  system elapsed 
 11.504   0.121  11.665