aggregate-methods {simFrame}

Method for aggregating simulation results

Description

Aggregate simulation results, i.e, split the data into subsets if applicable and compute summary statistics.

Usage

## S4 method for signature 'SimResults'
aggregate(x, select = NULL, FUN = mean, ...)

Arguments

x

the simulation results to be aggregated, i.e., an object of class "SimResults".

select

a character vector specifying the columns to be aggregated. It must be a subset of the colnames slot of x, which is the default.

FUN

a scalar function to compute the summary statistics (defaults to mean).

...

additional arguments to be passed down to aggregate or apply.

Value

If contamination or missing values have been inserted or the simulations have been split into different domains, a data.frame is returned, otherwise a vector.

Details

If contamination or missing values have been inserted or the simulations have been split into different domains, aggregate is called to compute the summary statistics for the respective subsets.

Otherwise, apply is called to compute the summary statistics for each column specified by select.

Methods

x = "SimResults"

aggregate simulation results.

Author(s)

Andreas Alfons

References

Alfons, A., Templ, M. and Filzmoser, P. (2010) An Object-Oriented Framework for Statistical Simulation: The R Package simFrame. Journal of Statistical Software, 37(3), 1–36. doi: 10.18637/jss.v037.i03.

See Also

aggregate, apply, "SimResults"

Examples

#### design-based simulation
set.seed(12345)  # for reproducibility
data(eusilcP)    # load data

## control objects for sampling and contamination
sc <- SampleControl(size = 500, k = 50)
cc <- DARContControl(target = "eqIncome", epsilon = 0.02,
    fun = function(x) x * 25)

## function for simulation runs
sim <- function(x) {
    c(mean = mean(x$eqIncome), trimmed = mean(x$eqIncome, 0.02))
}

## run simulation
results <- runSimulation(eusilcP,
    sc, contControl = cc, fun = sim)

## aggregate
aggregate(results)  # means of results
aggregate(results, FUN = sd)  # standard deviations of results


#### model-based simulation
set.seed(12345)  # for reproducibility

## function for generating data
rgnorm <- function(n, means) {
    group <- sample(1:2, n, replace=TRUE)
    data.frame(group=group, value=rnorm(n) + means[group])
}

## control objects for data generation and contamination
means <- c(0, 0.25)
dc <- DataControl(size = 500, distribution = rgnorm,
    dots = list(means = means))
cc <- DCARContControl(target = "value",
    epsilon = 0.02, dots = list(mean = 15))

## function for simulation runs
sim <- function(x) {
    c(mean = mean(x$value),
        trimmed = mean(x$value, trim = 0.02),
        median = median(x$value))
}

## run simulation
results <- runSimulation(dc, nrep = 50,
    contControl = cc, design = "group", fun = sim)

## aggregate
aggregate(results)  # means of results
aggregate(results, FUN = sd)  # standard deviations of results