swlm {PSEA} | R Documentation

## Stepwise model selection

### Description

Simple wrapper around stepAIC() (package MASS) to repeatedly perform stepwise model selection by AIC on several dependent variables (or responses, taken as rows of a matrix).

### Usage

    swlm(y,subset=NULL,upper,lower=formula(~1),direction='both',trace=FALSE,keep=NULL,verbose=FALSE)

### Arguments

`y` | Numeric matrix (with responses as rows and samples as columns) or ExpressionSet. Typically the expression data with transcripts (i.e. for a microarray, probes) as rows and samples as columns. If an ExpressionSet is provided the expression data is extracted with the function exprs.
`subset` | Integer vector. Represents a subset of samples (specified as column indices in y) to use for model fitting. By default all samples are used.
`verbose` | logical. If TRUE (default) the response number being fitted is printed.
`upper` | see ?stepAIC
`lower` | see ?stepAIC
`direction` | see ?stepAIC
`trace` | see ?stepAIC
`keep` | see ?stepAIC

### Details

The initial model for the stepwise approach only contains an intercept term.

### Value

`swft` | List of stepwise-selected models (see ?stepAIC)

### Author(s)

Alexandre Kuhn alexandre.m.kuhn@gmail.com

### References

Kuhn A, Kumar A, Beilina A, Dillman A, Cookson MR, Singleton AB. Cell population-specific expression analysis of human cerebellum. BMC Genomics 2012, 13:610.

### See Also

`marker`,`lmfitst`.

### Examples

    ## Load example expression data (variable "expression")
    ## and phenotype data (variable "groups")
    data("example")

    ## Four cell population-specific reference signals
    neuron_probesets <- list(c("221805_at", "221801_x_at", "221916_at"),
    		"201313_at", "210040_at", "205737_at", "210432_s_at")
    neuron_reference <- marker(expression, neuron_probesets)

    astro_probesets <- list("203540_at",c("210068_s_at","210906_x_at"),
    		"201667_at")
    astro_reference <- marker(expression, astro_probesets)

    oligo_probesets <- list(c("211836_s_at","214650_x_at"),"216617_s_at",
    		"207659_s_at",c("207323_s_at","209072_at"))
    oligo_reference <- marker(expression, oligo_probesets)

    micro_probesets <- list("204192_at", "203416_at")
    micro_reference <- marker(expression, micro_probesets)

    ## Stepwise model selection for 2 transcripts (202429_s_at and 200850_s_at)
    ## and focusing on control samples (i.e. groups == 0)
    swlm(expression[c("202429_s_at", "200850_s_at"),],
    	subset = which(groups == 0),
    	upper = formula(~neuron_reference + astro_reference +
    			oligo_reference + micro_reference))

[Package PSEA version 1.18.0 Index]