balliFit {BALLI} R Documentation

## balliFit

### Description

Estimates likelihood and Bartlett correction factor using BALLI algorithm of each gene

### Usage

balliFit(y_mat, x_mat, tecVar, intVar = 2, full = T, cfault = 0,
miter = 200, conv = 1e-06)


### Arguments

 y_mat numeric vector containing log-cpm values of each gene and each sample x_mat design matrix with samples in row and covariable(s) to be estimated in column tecVar numeric vector containing estimated technical variance of a gene of each sample intVar numeric vector designating interest variable(s) which is(are) column number(s) of x_mat full logical value designating full model (TRUE) or reduced model (FALSE). cfault initial value of index showing whether converged (0) or not (1). miter maximum number of iteration to converge. conv threshold for convergence

### Value

following components are estimated

 ll log-likelihoods beta coefficients of interested variable(s) alpha coefficients of nuisance variable(s) BCF Bartlett's correction factor cfault index whether converged or not

### Examples

expr <- data.frame(t(sapply(1:1000,function(x)rnbinom(20,mu=500,size=50))))
group <- c(rep("A",10),rep("B",10))
design <- model.matrix(~group, data = expr)
dge <- DGEList(counts=expr, group=group)
dge <- calcNormFactors(dge)
tV <- tecVarEstim(dge,design)
gtv <- tV$tecVar[1,] gdat <- data.frame(logcpm=tV$logcpm[1,],design,tecVar=gtv)
gy <- matrix(unlist(gdat[,1]),ncol=1)
gx <- matrix(unlist(gdat[,2:(ncol(gdat)-1)]),ncol=ncol(gdat)-2)
balliFit(y_mat=gy,x_mat=gx,tecVar=gtv,intVar=2,full=TRUE,cfault=0,miter=200,conv=1e-6)


[Package BALLI version 0.2.0 Index]