interactionResult-class: Class to Contain the Regression Result Based on An...
In ArrayTools: geneChip Analysis Package

Description Creating Objects Object Components Extends Methods Author(s) See Also Examples

Class to Contain the Regression Result Based on An Interaction Model. Interaction is a statistical term refering to a situation when the relationship between the outcome and the variable of the main interest differs at different levels of the extraneous variable

interactionResult object is generally created from the postInteraction function See postInteraction

A list of four or more components. Each component is a reggressResult class. The first component contains all the genes. The second component contains genes with the interaction effect The rest components contains genes with the interaction effect across different levels. Each component contains the result for each level.

Class "list", from data part. Class "vector", by class "list", distance 2.

adjustment: signature(object = "regressResult")

: access the adjustment slot

getAdjP: signature(object = "regressResult")

: access the adjPVal slot

getAnnotation: signature(object = "regressResult")

: access the annotation slot

getContrast: signature(object = "regressResult")

: access the contrast slot

getF: signature(object = "regressResult")

: access the FValue slot

getFC: signature(object = "regressResult")

: access the foldChange slot

getFCCutoff: signature(object = "regressResult")

: access the significantFCCutoff slot

getFileName: signature(object = "regressResult")

: access the fileName slot

getFilterMethod: signature(object = "regressResult")

: access the filterMethod slot

getID: signature(object = "regressResult")

: access the ID slot

getIndex: signature(object = "regressResult")

: access the significantIndex slot

getNormalizationMethod: signature(object = "regressResult")

: access the normalizationMethod slot

getP: signature(object = "regressResult")

: access the pValue slot

getPCutoff: signature(object = "regressResult")

: access the significantPvalueCutoff slot

Output2HTML: signature(object = "regressResult")

: create HTML file for sigificant genes in regressionResult

regressionMethod: signature(object = "regressResult")

: access the regressionMethod slot

selectSigGene: signature(object = "regressResult")

: select significant genes for regressionResult class

show: signature(object = "regressResult")

: print regressResult

Sort: signature(x = "regressResult")

: sort regressResult

summary: signature(object = "regressResult")

: print the summary for regressResult

getLength: signature(object = "interactionResult")

: calculate the length of the interactionResult class

Xiwei Wu, Arthur Li

regressResult

## Creating the interactionREsult takes a few steps:
data(eSetExample)
design.int<- new("designMatrix", target=pData(eSetExample), covariates = c("Treatment", "Group"),
    intIndex = c(1, 2))
contrast.int<- new("contrastMatrix", design.matrix = design.int, interaction=TRUE)
result.int<- regress(eSetExample, contrast.int)
sigResult.int <- selectSigGene(result.int)
intResult <- postInteraction(eSetExample, sigResult.int, mainVar ="Treatment",
   compare1 = "Treated", compare2 = "Control")