fitFeatureModel: Computes differential abundance analysis using a...
In metagenomeSeq: Statistical analysis for sparse high-throughput sequencing

Description Usage Arguments Value See Also Examples

Wrapper to actually run zero-inflated log-normal model given a MRexperiment object and model matrix. User can decide to shrink parameter estimates.

1	fitFeatureModel(obj, mod, coef = 2, B = 1, szero = FALSE, spos = TRUE)

`obj`	A MRexperiment object with count data.
`mod`	The model for the count distribution.
`coef`	Coefficient of interest to grab log fold-changes.
`B`	Number of bootstraps to perform if >1. If >1 performs permutation test.
`szero`	TRUE/FALSE, shrink zero component parameters.
`spos`	TRUE/FALSE, shrink positive component parameters.

A list of objects including:

call - the call made to fitFeatureModel
fitZeroLogNormal - list of parameter estimates for the zero-inflated log normal model
design - model matrix
taxa - taxa names
counts - count matrix
pvalues - calculated p-values
permuttedfits - permutted z-score estimates under the null

cumNorm

data(lungData)
lungData = lungData[,-which(is.na(pData(lungData)$SmokingStatus))]
lungData=filterData(lungData,present=30,depth=1)
lungData <- cumNorm(lungData, p=.5)
s <- normFactors(lungData)
pd <- pData(lungData)
mod <- model.matrix(~1+SmokingStatus, data=pd)
lungres1 = fitFeatureModel(lungData,mod)