swlm {PSEA} | R Documentation |
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).
swlm(y,subset=NULL,upper,lower=formula(~1),direction='both',trace=FALSE,keep=NULL,verbose=FALSE)
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 |
The initial model for the stepwise approach only contains an intercept term.
swft |
List of stepwise-selected models (see ?stepAIC) |
Alexandre Kuhn alexandre.m.kuhn@gmail.com
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.
## 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))