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PhenStat (version 2.6.0)

finalModel: Method "finalModel"

Description

This is an internal function run within MM framework. It completes the final stage of the MM framework, which builds the final model and estimates effects. As an internal function, it doesn't include extensive error testing of inputs. Please use cautiously if calling directly. Works with PhenTestResult object created by startModel function. The creation of MM final model is based on the significance of different fixed effects, depVariable and equation values stored in PhenTestResult object.

Usage

finalModel(phenTestResult, outputMessages = TRUE)

Arguments

phenTestResult
instance of the PhenTestResult class that comes from the function testDataset; mandatory argument
outputMessages
flag: "FALSE" value to suppress output messages; "TRUE" value to show output messages; default value TRUE

Value

Returns results stored in instance of the PhenTestResult class

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.

See Also

PhenTestResult and testDataset

Examples

Run this code
    file <- system.file("extdata", "test1.csv", package="PhenStat")
    test <- PhenList(dataset=read.csv(file),
            testGenotype="Sparc/Sparc")
    # when "testDataset" function's argument "callAll" is set to FALSE 
    # only "startModel" function is called - the first step of MM framework
    result <- testDataset(test,
            depVariable="Lean.Mass",
            equation="withoutWeight",
            callAll=FALSE) 
    # print out formula that has been created
    # result$model.formula.genotype
    # print out batch effect's significance 
    # result$model.effect.batch
    # change the model
    # result <- testDataset(test,
    #        depVariable="Lean.Mass",
    #        equation="withWeight",
    #        callAll=FALSE) 
    # print out new formula
    #result$model.formula.genotype
    # run the final model fitting when statisfied with the model
    result <- finalModel(result)

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