biclust (version 2.0.3.1)

diagnosticPlot2: Diagnostics F Statistiics Visualization

Description

Plots distributions of bootstrap replicates of F-statistics for row, column and multiplicative effects obtained from diagnosticTest (when save_F=TRUE). Contains an option to highlight the observed statistics.

Usage

diagnosticPlot2(diagnosticTest, number = 1, StatVal = TRUE,
  binwidth = NULL)

Value

Returns a ggplot object.

Arguments

diagnosticTest

output of diagnosticTest with save_F=TRUE which contains the F-statistics and sampling replicates.

number

Number of which BC to plot. This needs to be one of the Biclusters requested in in diagnosticTest.

StatVal

Boolean value to draw the observed statistic on the distribution plots.

binwidth

The width of the bins.

Author

Ewoud De Troyer

Examples

# \dontshow{
test <- matrix(rnorm(5000),100,50)
roweff <- sample(1:5,10,replace=TRUE)
coleff <- sample(1:5,10,replace=TRUE)
test[11:20,11:20] <- test[11:20,11:20] +
  matrix(coleff,nrow=10,ncol=10,byrow=TRUE) +
  matrix(roweff,nrow=10,ncol=10) +
  roweff %*% t(coleff)


#Apply Plaid Biclustering
res <- biclust(test, method=BCPlaid())

#Apply default diagnosticTest
out <- diagnosticTest(BCresult=res, data=test, save_F=TRUE, number=1,
                      statistics=c("F"),
                      samplingtypes=c("Permutation"))
diagnosticPlot2(out,number=1)
# }

if (FALSE) {
#Random matrix with embedded bicluster (with multiplicative effect)
test <- matrix(rnorm(5000),100,50)
roweff <- sample(1:5,10,replace=TRUE)
coleff <- sample(1:5,10,replace=TRUE)
test[11:20,11:20] <- test[11:20,11:20] +
  matrix(coleff,nrow=10,ncol=10,byrow=TRUE) +
  matrix(roweff,nrow=10,ncol=10) +
  roweff %*% t(coleff)


#Apply Plaid Biclustering
res <- biclust(test, method=BCPlaid())

#Apply default diagnosticTest
out <- diagnosticTest(BCresult=res, data=test, save_F=TRUE, number=1,
                      statistics=c("F","Tukey","ModTukey","Tusell","Mandel","LBI","JandG"),
                      samplingtypes=c("Permutation","SemiparPerm","SemiparBoot",
                      "PermutationCor","SamplingCor","NormSim"))

#Plot Distributions
diagnosticPlot2(out,number=1)
}