Running 'exprSet-example.R' [5s]
  Comparing 'exprSet-example.Rout' to 'exprSet-example.Rout.save' ... OK
  Running 'test00-color.R' [0s]
  Comparing 'test00-color.Rout' to 'test00-color.Rout.save' ... OK
  Running 'test01-distance.R' [0s]
  Comparing 'test01-distance.Rout' to 'test01-distance.Rout.save' ... OK
  Running 'test02-cluster.R' [169s]
  Running 'test02.1-just.R' [157s]
  Running 'test03-boot.R' [2s]
  Comparing 'test03-boot.Rout' to 'test03-boot.Rout.save' ... OK
  Running 'test04-perturb.R' [2s]
  Comparing 'test04-perturb.Rout' to 'test04-perturb.Rout.save' ... OK
  Running 'test04.1-gpca.R' [165s]
  Running 'test05-spca.R' [1s]
  Comparing 'test05-spca.Rout' to 'test05-spca.Rout.save' ... OK
  Running 'test06-mosaic.R' [1s]
  Comparing 'test06-mosaic.Rout' to 'test06-mosaic.Rout.save' ... OK
  Running 'test07-pcanova.R' [161s]
Running the tests in 'tests/test02-cluster.R' failed.
Complete output:
  > library(ClassDiscovery)
  Loading required package: cluster
  Loading required package: oompaBase
  > suppressWarnings( RNGversion("3.5.3") )
  > set.seed(320085)
  > # simulate data from two different classes
  > d1 <- matrix(rnorm(100*30, rnorm(100, 0.5)), nrow=100, ncol=30, byrow=FALSE)
  > d2 <- matrix(rnorm(100*20, rnorm(100, 0.5)), nrow=100, ncol=20, byrow=FALSE)
  > dd <- cbind(d1, d2)
  > 
  > # cluster the data
  > hc <- hclust(distanceMatrix(dd, 'pearson'), method='average')
  > 
  > # make a fake reproducibility matrix
  > fraud <- function(x) {
  +   new('ClusterTest', result=abs(cor(x)), call=match.call())
  + }
  > 
  > fake <- fraud(dd)
  > summary(fake)
  A ClusterTest object.
  
  Call:
  	 fraud(x = dd) 
  
  Agreement levels:
       Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
  0.0000367 0.0643374 0.3528907 0.2975618 0.5153581 0.6697706 
  > 
  > hist(fake)
  > 
  > image(fake) # let heatmap compute a new dendrogram from the agreements
  > 
  > image(fake, dendrogram=hc) # use the actual dendrogram from the data
  > 
  > image(fake, dendrogram=hc, col=blueyellow(64)) # change the colors
  > 
  > #cleanup
  > rm(fake, fraud, hc, dd, d1, d2)
  > 
  > proc.time()
     user  system elapsed 
     0.59    0.10    0.68 
Running the tests in 'tests/test02.1-just.R' failed.
Complete output:
  > library(ClassDiscovery)
  Loading required package: cluster
  Loading required package: oompaBase
  > suppressWarnings( RNGversion("3.5.3") )
  > set.seed(547422)
  > # simulate data from three different groups
  > d1 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
  > d2 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
  > d3 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
  > dd <- cbind(d1, d2, d3)
  > 
  > cutKmeans(dd, k=3)
   [1] 3 3 3 3 3 3 3 3 3 3 1 2 2 2 1 2 2 1 1 1 3 3 3 3 3 3 3 3 3 3
  > cutKmeans(dd, k=4)
   [1] 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 4 2 2 4 4 2 2 4 4 2
  > 
  > cutHclust(dd, k=3)
  > cutHclust(dd, k=4)
  > 
  > cutPam(dd, k=3)
   [1] 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3
  > cutPam(dd, k=4)
   [1] 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 4 3 4 4 4 3 3 3 3
  > 
  > cutRepeatedKmeans(dd, k=3, nTimes=10)
   [1] 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3
  > crk <- cutRepeatedKmeans(dd, k=4, nTimes=10)
  > sort(as.vector(table(crk))) # sme numbers, different labels on some machines
  [1]  5  5 10 10
  > 
  > # cleanup
  > rm(d1, d2, d3, dd)
  > 
  > proc.time()
     user  system elapsed 
     0.37    0.12    0.48 
Running the tests in 'tests/test04.1-gpca.R' failed.
Complete output:
  > library(ClassDiscovery)
  Loading required package: cluster
  Loading required package: oompaBase
  > suppressWarnings( RNGversion("3.5.3") )
  > set.seed(341392)
  > # simulate samples from thre different groups, with generic genes
  > d1 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
  > d2 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
  > d3 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
  > dd <- cbind(d1, d2, d3)
  > 
  > # perform PCA in gene space
  > gpc <- GenePCA(dd)
  > 
  > # plot the results
  > plot(gpc)
  > 
  > # cleanup
  > rm(d1, d2, d3, dd, gpc)
  > 
  > proc.time()
     user  system elapsed 
     0.37    0.06    0.42 
Running the tests in 'tests/test07-pcanova.R' failed.
Complete output:
  > library(ClassDiscovery)
  Loading required package: cluster
  Loading required package: oompaBase
  > suppressWarnings( RNGversion("3.5.3") )
  > set.seed(581492)
  > # simulate data from three groups
  > d1 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
  > d2 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
  > d3 <- matrix(rnorm(100*10, rnorm(100, 0.5)), nrow=100, ncol=10, byrow=FALSE)
  > dd <- cbind(d1, d2, d3)
  > # colors that match the groups
  > cols <- rep(c('red', 'green', 'blue'), each=10)
  > 
  > # compute teh PCanova object
  > pan <- PCanova(dd, c('red', 'green', 'blue'), cols, cols)
  > summary(pan)
  An object of the PCanova class with 3 groups.
  Call:
  	PCanova(data = dd, classes = c("red", "green", "blue"), labels = cols, colors = cols)
  > 
  > # view the PC plots
  > plot(pan)
  > 
  > # view the dendrograms
  > pltree(pan, line=-0.5)
  > 
  > # compare teh results when there is no underlying group structure
  > dd <- matrix(rnorm(100*50, rnorm(100, 0.5)), nrow=100, ncol=50, byrow=FALSE)
  > cols <- rep(c('red', 'green', 'blue', 'orange', 'cyan'), each=10)
  > pan <- PCanova(dd, unique(cols), cols, cols)
  > plot(pan, mscale=1/sqrt(10))
  > 
  > pltree(pan, line=-0.5)
  > 
  > # cleanup
  > rm(d1, d2, d3, dd, cols, pan)
  > 
  > proc.time()
     user  system elapsed 
     0.42    0.12    0.53