MArrayLM-class {limma} | R Documentation

## Microarray Linear Model Fit - class

### Description

A list-based class for storing the results of fitting gene-wise linear models to a batch of microarrays. Objects are normally created by `lmFit`.

### Slots/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

Objects may also contain the following optional components:

`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 gene names and annotation
`design`:
  design `matrix` of full column rank
`contrasts`:
  `matrix` defining contrasts of coefficients for which results are desired
`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`
`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
`s2.post`:
  `numeric` vector giving posterior residual variances
`t`:
  `matrix` containing empirical Bayes t-statistics
`var.prior`:
  `numeric` vector giving empirical Bayes estimated prior variance for each true coefficient
`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.

If there are no weights and no missing values, then the `MArrayLM` objects returned by `lmFit` will also contain the QR-decomposition of the design matrix, and any other components returned by `lm.fit`.

### Methods

`RGList` 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` 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 2.16.5 Index]