| MArrayLM-class {limma} | R Documentation |
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 giving empirical Bayes estimated prior value for residual variances |
df.prior | numeric 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.