BALLI (version 0.2.0)

balliFit: 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

# NOT RUN {
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)
# }