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]