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marraylm

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.

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

Linear Models for Microarray Data

v3.46.0
GPL (>=2)
Authors
Gordon Smyth [cre,aut], Yifang Hu [ctb], Matthew Ritchie [ctb], Jeremy Silver [ctb], James Wettenhall [ctb], Davis McCarthy [ctb], Di Wu [ctb], Wei Shi [ctb], Belinda Phipson [ctb], Aaron Lun [ctb], Natalie Thorne [ctb], Alicia Oshlack [ctb], Carolyn de Graaf [ctb], Yunshun Chen [ctb], Mette Langaas [ctb], Egil Ferkingstad [ctb], Marcus Davy [ctb], Francois Pepin [ctb], Dongseok Choi [ctb]
Initial release
2020-10-19

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