R: Diagnostic F Statistic Calculation

computeObservedFstat {biclust} | R Documentation

## Diagnostic F Statistic Calculation

### Description

Functions for obtaining F statistics within bicluster and the significance levels. The main effects considered are row, column and interaction effect.

### Usage

    computeObservedFstat(x, bicResult, number)

### Arguments

`x` | Data Matrix
`bicResult` | `Biclust` object from `biclust` package
`number` | Number of bicluster in the output for computing observed statistics

### Details

F-statistics are calculated from the two-way ANOVA mode with row anc column effect. The full model with interaction is unidentifiable, thus, Tukey's test for non-additivity is used to detect an interaction within a bicluster. p-values are obtained from assymptotic F distributions.

### Value

Data frame with three rows ("Row Effect", "Column Effect", "Tukey test") and 2 columns for corresponding statistics (Fstat) and their p-values (PValue). 2

### Author(s)

Tatsiana KHAMIAKOVA tatsiana.khamiakova@uhasselt.be

### See Also

`diagnosticTest`, `diagnosticPlot2`, `ChiaKaruturi`, `diagnoseColRow`

### Examples

    #---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)

[Package biclust version 2.0.2 Index]