getResidPerGene {GSEAlm} | R Documentation

## Row-by-Row Linear-Model Residuals for Gene Expression (or similar) Data Structures

### Description

This produces residuals of an identical linear model applied to each row of a gene expression matrix (or similar dataset). Computation speed is achieved via straightforward matrix algebra. Most commonly-used residual types are available.

### Usage

    getResidPerGene(lmobj, type = "extStudent")

### Arguments

`lmobj` | An object produced by function `lmPerGene`.
`type` | A string indicating the type of residual requeseted (defaults to externally-Studentized).

### Details

Types of residuals now available:

"response"
  Response residuals, observed minus fitted
"normalized"
  Response residuals divided by the estimated residual S.E.
"intStudent"
  Internally Studentized residuals, often referred to as "Standardized"
default
  Externally Studentized residuals, which can be used directly for outlier identification

### Value

Returns a instance of `ExpressionSet` where the expression matrix contains the residuals. The `phenoData` are inherited from `lmobj$eS`.

### Author(s)

Robert Gentleman, Assaf Oron

### See Also

`lmPerGene`, `resplot`,`dfbetasPerGene`,`influence.measures`

### Examples

    data(sample.ExpressionSet)
    lm1 = lmPerGene(sample.ExpressionSet,~sex)
    r1 = getResidPerGene(lm1)
    ### now a boxplot of all residuals by sample
    resplot(resmat=exprs(r1),fac=sample.ExpressionSet$sex)
    ### This plot is not very informative because of some gross outliers;
    ### try this instead
    resplot(resmat=exprs(r1),fac=sample.ExpressionSet$sex,lims=c(-5,5))

[Package GSEAlm version 1.0.0 Index]