MArrayLM-class: Microarray Linear Model Fit - class

Description

A list-based S4 class for storing the results of fitting gene-wise linear models to a set of microarrays. Objects are normally created by lmFit, and additional components are added by eBayes.

Arguments

Components

MArrayLM objects do not contain any slots (apart from .Data) but they should contain the following list components: ll{ {coefficients} matrix containing fitted coefficients or contrasts} stdev.unscaled

tab

matrix containing unscaled standard deviations of the coefficients or contrasts
numeric vector containing residual standard deviations for each gene
numeric vector containing residual degrees of freedom for each gene
numeric vector containing the average log-intensity for each probe over all the arrays in the original linear model fit. Note this vector does not change when a contrast is applied to the fit using contrasts.fit.
data.frame containing probe annotation.
design matrix.
numeric matrix giving the unscaled covariance matrix of the estimable coefficients
integer vector giving the order of coefficients in cov.coefficients. Is computed by the QR-decomposition of the design matrix.
numeric matrix defining contrasts of coefficients for which results are desired.
numeric value giving empirical Bayes estimated prior value for residual variances
numeric vector giving empirical Bayes estimated degrees of freedom associated with s2.prior for each gene
numeric vector giving total degrees of freedom used for each gene, usually equal to df.prior + df.residual.
numeric vector giving posterior residual variances
numeric vector giving empirical Bayes estimated prior variance for each true coefficient
numeric vector giving moderated F-statistics for testing all contrasts equal to zero
numeric vector giving p-value corresponding to F.stat
numeric matrix containing empirical Bayes t-statistics

cr

sigma
df.residual
genes
design
cov.coefficients
pivot
qr QR-decomposition of the design matrix (if the fit involved no weights or missing values). ... other components returned by lm.fit (if the fit involved no weights or missing values).
df.prior
df.total
s2.post
var.prior
F
F.p.value
t

code

s2.prior

tabular

Methods

MArrayLM objects will return dimensions and hence functions such as dim, nrow and ncol are defined. MArrayLM objects inherit a show method from the virtual class LargeDataObject. The functions eBayes, decideTests and classifyTestsF accept MArrayLM objects as arguments.