Description Components Methods Author(s) See Also
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
.
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 |
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.
Gordon Smyth
02.Classes gives an overview of all the classes defined by this package.
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