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 |
F-statistics are calculated from the two-way ANOVA mode with row anc column effect. The full model with interaction is undentifiable, thus, Tukey's test for non-additivity is used to detect an interaction within a bicluster. p-values 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 p-values (PValue). 2
Tatsiana KHAMIAKOVA tatsiana.khamiakova@uhasselt.be
#---simulate dataset with 1 bicluster ---# xmat<-matrix(rnorm(20*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 p-values to infer about the structure within bicluster: Structure <- computeObservedFstat(x=xmat, bicResult = plaidmab, number = 1)