interactionResult-class {ArrayTools} | R Documentation |
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.
signature(object = "regressResult")
adjustment
slot }
signature(object = "regressResult")
adjPVal
slot }
signature(object = "regressResult")
annotation
slot }
signature(object = "regressResult")
contrast
slot }
signature(object = "regressResult")
FValue
slot}
signature(object = "regressResult")
foldChange
slot }
signature(object = "regressResult")
significantFCCutoff
slot}
signature(object = "regressResult")
fileName
slot }
signature(object = "regressResult")
filterMethod
slot }
signature(object = "regressResult")
ID
slot }
signature(object = "regressResult")
significantIndex
slot}
signature(object = "regressResult")
normalizationMethod
slot}
signature(object = "regressResult")
pValue
slot }
signature(object = "regressResult")
significantPvalueCutoff
slot }
signature(object = "regressResult")
signature(object = "regressResult")
regressionMethod
slot}
signature(object = "regressResult")
regressionResult
class}
signature(object = "regressResult")
regressResult
}
signature(x = "regressResult")
regressResult
}
signature(object = "regressResult")
regressResult
}
signature(object = "interactionResult")
Xiwei Wu, Arthur Li
## 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")