MArrayLM-class {limma} | R Documentation

## 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 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

### 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.

[Package limma version 3.42.2 Index]