computeObservedFstat {biclust}  R Documentation 
Functions for obtaining F statistics within bicluster and the significance levels. The main effects considered are row, column and interaction effect.
computeObservedFstat(x, bicResult, number)
x 
Data Matrix 
bicResult 

number 
Number of bicluster in the output for computing observed statistics 
Fstatistics are calculated from the twoway ANOVA mode with row anc column effect. The full model with interaction is unidentifiable, thus, Tukey's test for nonadditivity is used to detect an interaction within a bicluster. pvalues are obtained from assymptotic F distributions.
Data frame with three rows ("Row Effect", "Column Effect", "Tukey test") and 2 columns for corresponding statistics (Fstat) and their pvalues (PValue). 2
Tatsiana KHAMIAKOVA tatsiana.khamiakova@uhasselt.be
diagnosticTest
, diagnosticPlot2
, ChiaKaruturi
, diagnoseColRow
#simulate dataset with 1 bicluster # xmat<matrix(rnorm(50*50,0,0.25),50,50) # background noise only rowSize < 20 #number of rows in a bicluster colSize < 10 #number of columns in a bicluster a1<rnorm(rowSize,1,0.1) #sample row effect from N(0,0.1) #adding a coherent values bicluster: b1<rnorm((colSize),2,0.25) #sample column effect from N(0,0.05) mu<0.01 #constant value signal for ( i in 1 : rowSize){ for(j in 1: (colSize)){ xmat[i,j] < xmat[i,j] + mu + a1[i] + b1[j] } } #obtain a bicluster by running an algorithm# plaidmab < biclust(x=xmat, method=BCPlaid(), cluster="b", fit.model = y ~ m + a+ b, background = TRUE, row.release = 0.6, col.release = 0.7, shuffle = 50, back.fit = 5, max.layers = 1, iter.startup = 100, iter.layer = 100, verbose = TRUE) #Calculate statistics and their pvalues to infer about the structure within bicluster: Structure < computeObservedFstat(x=xmat, bicResult = plaidmab, number = 1)