testDataset: Method "testDataset"

Description

Statistical analysis manager function in PhenStat package. Firstly, it performs the checks for dependent variable in the dataset. Some checks are generic, some depends on selected statistical framework/method. Secondly, if checks are passed it runs the stages of selected framework to analyse dependent variable. For instance, runs startModel and finalModel for the MM framework if the argument "callAll" is set to TRUE. If framework contains only one stage like in Fisher Exact Test case then runs that one stage regardless "callAll" value.

Usage

testDataset(phenList=NULL, depVariable=NULL,  equation="withWeight", outputMessages=TRUE,  pThreshold=0.05, method="MM", callAll=TRUE, keepList=NULL, dataPointsThreshold=4, RR_naturalVariation=95,RR_controlPointsThreshold=60,transformValues=FALSE,useUnfiltered=FALSE)

Arguments

phenList

instance of the PhenList class; mandatory argument

depVariable

a character string defining the dependent variable of interest; mandatory argument

equation

a character string defining the equation to use. Possible values "withWeight" (default), "withoutWeight"

outputMessages

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

pThreshold

a numerical value for the p-value threshold used to determine which fixed effects to keep in the model, default value 0.05

method

a character string ("MM", "FE" or "RR") defining the method to use for model building; default value "MM" for mixed model

callAll

flag: "FALSE" value to run only the first stage of the selected framework; "TRUE" value (default) to run all stages of framework one after another

keepList

a logical vector defining the significance of different model effects: keep_batch, keep_equalvar, keep_weight, keep_sex, keep_interaction; default value NULL

dataPointsThreshold

threshold for the number of data points in the MM framework; default value is 4 ; minimal value is 2

RR_naturalVariation

threshold for the variation ranges in the RR framework; default value is 95 ; minimal value is 60

RR_controlPointsThreshold

threshold for the number of control data points in the RR framework ; default value is 60; minimal value is 40

transformValues

flag: "FALSE" value to suppress transformation; "TRUE" value to perform transformation if needed; default value FALSE

useUnfiltered

flag: "FALSE" value to use filtered dataset; "TRUE" value to use unfiltered dataset; default value FALSE

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.

Examples

Run this code

    # Mixed Model framework
    file <- system.file("extdata", "test1.csv", package="PhenStat")
    test <- PhenList(dataset=read.csv(file),
            testGenotype="Sparc/Sparc")
    result <- testDataset(test,
            depVariable="Lean.Mass")
    # print out formula that has been created
    analysisResults(result)$model.formula.genotype
    summaryOutput(result)
    
    # Mixed Model framework with user defined effects
    user_defined_effects <- c(keep_batch=TRUE,
            keep_equalvar=TRUE,
            keep_weight=TRUE,
            keep_sex=TRUE,
            keep_interaction=TRUE)
    result3 <- testDataset(test,
            depVariable="Lean.Mass",
            keepList=user_defined_effects)
    # print out formula that has been created
    analysisResults(result3)$model.formula.genotype
    summaryOutput(result3)
    
    # Fisher Exact Test framework
    file <- system.file("extdata", "test_categorical.csv", package="PhenStat")
    test2 <- PhenList(dataset=read.csv(file),
            testGenotype="Aff3/Aff3")
    result2 <- testDataset(test2,
            depVariable="Thoracic.Processes",
            method="FE") 
    summaryOutput(result2)