mutoss (version 0.1-10)

gatherStatistics: Gathering statistics from simulation()-Object

Description

This function facilitates gathering statistics from the object returned by simulation().

Usage

gatherStatistics(simObject, listOfStatisticFunctions, listOfAvgFunctions)

Arguments

simObject

An object returned by simulation()

listOfStatisticFunctions

List of statistics that shall be calculated for the elements in simObject

listOfAvgFunctions

List of functions that will be used to summarize/average the calculated statistics for all elements in simObject$results with the same parameter constellation. If this is argument is missing no averaging will be done. Instead the resulting data.frame will keep one row for every object in simObject$results.

Value

Details

For every simulation()-Object in simObject$results all statistics in listOfStatisticFunctions are calculated. If in addition listOfAvgFunctions is provided then the statistics of the objects that have the same parameter constellation are averaged by the functions in listOfAvgFunctions. The resulting data.frame will then keep only one row for every parameter constellation.

Examples

myGen <- function(n, n0) {
  list(procInput=list(pValues = c(runif(n-n0, 0, 0.01), runif(n0))), 
       groundTruth = c(rep(FALSE, times=n-n0), rep(TRUE, times=n0)))
}

# need some simulation()-Object I can work with 
sim <- simulation(replications = 10, list(funName="myGen", fun=myGen, n=200, n0=c(50,100)), 
list(list(funName="BH", fun=function(pValues, alpha) BH(pValues, alpha, silent=TRUE), 
  alpha=c(0.25, 0.5)),
list(funName="holm", fun=holm, alpha=c(0.25, 0.5),silent=TRUE)))

# Make my own statistic function
NumberOfType1Error <- function(data, result) sum(data$groundTruth * result$rejected)

# Get now for every object in sim$results one row with the number of Type 1 Errors
result.all <- gatherStatistics(sim, list(NumOfType1Err = NumberOfType1Error))

# Average over all sim$results-Objects with common parameters 
result1 <- gatherStatistics(sim, list(NumOfType1Err = NumberOfType1Error), list(MEAN = mean))
print(result1)  
result2 <- gatherStatistics(sim, list(NumOfType1Err = NumberOfType1Error), 
             list(q05 = function(x) quantile(x, probs=0.05), 
             MEAN = mean, q95 = function(x) quantile(x, probs=0.95)))
print(result2)

# create some plots
require(lattice)
histogram(~NumOfType1Err | method*alpha, data = result.all$statisticDF)
barchart(NumOfType1Err.MEAN ~ method | alpha, data = result2$statisticDF)