marraylm: Microarray Linear Model Fit - class

In limma: Linear Models for Microarray Data

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.

Components

MArrayLM objects do not contain any slots (apart from .Data) but they should contain the following list components:

coefficients	matrix containing fitted coefficients or contrasts
stdev.unscaled	matrix containing unscaled standard deviations of the coefficients or contrasts
sigma	numeric vector containing residual standard deviations for each gene
df.residual	numeric vector containing residual degrees of freedom for each gene

The following additional components may be created by lmFit:

Amean	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.
genes	data.frame containing probe annotation.
design	design matrix.
cov.coefficients	numeric matrix giving the unscaled covariance matrix of the estimable coefficients
pivot	integer vector giving the order of coefficients in cov.coefficients. Is computed by the QR-decomposition of the design matrix.
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).

The following component may be added by contrasts.fit:

contrasts

numeric matrix defining contrasts of coefficients for which results are desired.

The following components may be added by eBayes:

s2.prior	numeric value or vector giving empirical Bayes estimated prior value for residual variances
df.prior	numeric value or vector giving empirical Bayes estimated degrees of freedom associated with s2.prior for each gene
df.total	numeric vector giving total degrees of freedom used for each gene, usually equal to df.prior + df.residual.
s2.post	numeric vector giving posterior residual variances
var.prior	numeric vector giving empirical Bayes estimated prior variance for each true coefficient
F	numeric vector giving moderated F-statistics for testing all contrasts equal to zero
F.p.value	numeric vector giving p-value corresponding to F.stat
t	numeric matrix containing empirical Bayes t-statistics

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.

Author(s)

Gordon Smyth

See Also

02.Classes gives an overview of all the classes defined by this package.

limma documentation built on Nov. 8, 2020, 8:28 p.m.