fitZig: Computes the weighted fold-change estimates and t-statistics.

Description Usage Arguments Value See Also Examples

View source: R/fitZig.R

Description

Wrapper to actually run the Expectation-maximization algorithm and estimate $f_count$ fits. Maximum-likelihood estimates are approximated using the EM algorithm where we treat mixture membership $delta_ij = 1$ if $y_ij$ is generated from the zero point mass as latent indicator variables. The density is defined as $f_zig(y_ij = pi_j(S_j)*f_0(y_ij) +(1-pi_j (S_j)) * f_count(y_ij; mu_i, sigma_i^2)$. The log-likelihood in this extended model is: $(1-delta_ij) log f_count(y;mu_i,sigma_i^2 )+delta_ij log pi_j(s_j)+(1-delta_ij) log (1-pi_j (s_j))$. The responsibilities are defined as $z_ij = pr(delta_ij=1 | data)$.

Usage

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fitZig(obj, mod, zeroMod = NULL, useCSSoffset = TRUE,
  control = zigControl(), useMixedModel = FALSE, ...)

Arguments

obj

A MRexperiment object with count data.

mod

The model for the count distribution.

zeroMod

The zero model, the model to account for the change in the number of OTUs observed as a linear effect of the depth of coverage.

useCSSoffset

Boolean, whether to include the default scaling parameters in the model or not.

control

The settings for fitZig.

useMixedModel

Estimate the correlation between duplicate features or replicates using duplicateCorrelation.

...

Additional parameters for duplicateCorrelation.

Value

A list of objects including:

See Also

cumNorm zigControl

Examples

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# 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)
# The maxit is not meant to be 1 - this is for demonstration/speed
settings = zigControl(maxit=1,verbose=FALSE)
fit = fitZig(obj = lungTrim,mod=mod,control=settings)

metagenomeSeq documentation built on May 31, 2017, 11:53 a.m.