classificationTag: Method "classificationTag"

Description

Returns a classification tag to assign a sexual dimorphism assessment of the phenotypic change.

Usage

classificationTag(phenTestResult, userMode = "summaryOutput",  phenotypeThreshold = 0.01,outputMessages=TRUE)

Arguments

phenTestResult

instance of the PhenTestResult class that comes from the function testDataset; mandatory argument

userMode

flag: "vectorOuput" a sexual dimorphic classification is assigned with a proviso of later assessing the overall statistical significance; "summaryOutput" the phenotypeThreshold is used to assess the overall statistical significance and then if significant the sexual dimorphic classification determined; defaults to summaryOutput

phenotypeThreshold

a numerical value defining the threshold to use in classificationTag in determining whether the genotype effect is classed as significant or not; default value 0.01

outputMessages

flag: "FALSE" value to suppress output messages; "TRUE" value to show output messages ; default value TRUE

Value

Returns a classification tag to assign a sexual dimorphism assessment of the phenotypic change.If you are working interactively with the data, the argument "userMode" set to the value "summaryOutput" will use the "phenotypeThreshold" argument's value to assess statistical significance of the genotype effect and if significant then assign a sexual dimorphic classification. Alternatively, if the "userMode" set to the value "vectorMode", a sexual dimorphic classification will be returned with the MM framework where later you can globally assess whether the variable had a significant genotype effect. With the FE framework and the vectorMode, a NA is returned as the type of the effect cannot be assessed without assessing the statistical significance of the genotype effect.

References

Karp N, Melvin D, Sanger Mouse Genetics Project, Mott R (2012): Robust and Sensitive Analysis of Mouse Knockout Phenotypes. PLoS ONE 7(12): e52410. doi:10.1371/journal.pone.0052410 West B, Welch K, Galecki A (2007): Linear Mixed Models: A practical guide using statistical software New York: Chapman & Hall/CRC 353 p.

Examples

Run this code

    file <- system.file("extdata", "test1.csv", package="PhenStat")
    test <- PhenList(dataset=read.csv(file),
            testGenotype="Sparc/Sparc")
    result <- testDataset(test,
            depVariable="Lean.Mass")
    classificationTag(result, 
            userMode="summaryOutput", 
            phenotypeThreshold=0.001)
    classificationTag(result, 
            userMode="vectorOutput")