fitLogNormal: Computes a log-normal linear model and permutation based...
In metagenomeSeq: Statistical analysis for sparse high-throughput sequencing

Description Usage Arguments Value Examples

Wrapper to perform the permutation test on the t-statistic. This is the original method employed by metastats (for non-sparse large samples). We include CSS normalization though (optional) and log2 transform the data. In this method the null distribution is not assumed to be a t-dist.

1	fitLogNormal(obj, mod, useCSSoffset = TRUE, B = 1000, coef = 2, sl = 1000)

`obj`	A MRexperiment object with count data.
`mod`	The model for the count distribution.
`useCSSoffset`	Boolean, whether to include the default scaling parameters in the model or not.
`B`	Number of permutations.
`coef`	The coefficient of interest.
`sl`	The value to scale by (default=1000).

Call made, fit object from lmFit, t-statistics and p-values for each feature.

# This is a simple demonstration
data(lungData)
k = grep("Extraction.Control",pData(lungData)$SampleType)
lungTrim = lungData[,-k]
k = which(rowSums(MRcounts(lungTrim)>0)<30)
lungTrim = cumNorm(lungTrim)
lungTrim = lungTrim[-k,]
smokingStatus = pData(lungTrim)$SmokingStatus
mod = model.matrix(~smokingStatus)
fit = fitLogNormal(obj = lungTrim,mod=mod,B=1)