fitLogNormal {metagenomeSeq} | R Documentation |
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.
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)