simFrame (version 0.5.0)

plot-methods: Plot simulation results

Description

Plot simulation results. A suitable plot function is selected automatically, depending on the structure of the results.

Usage

## S3 method for class 'SimResults,missing':
plot(x, y , \dots)

Arguments

x

the simulation results.

y

not used.

...

further arguments to be passed to the selected plot function.

Value

• An object of class "trellis". The update method can be used to update components of the object and the print method (usually called by default) will plot it on an appropriate plotting device.

Details

The results of simulation experiments with at most one contamination level and at most one missing value rate are visualized by (conditional) box-and-whisker plots. For simulations involving different contamination levels or missing value rates, the average results are plotted against the contamination levels or missing value rates.

References

Alfons, A., Templ, M. and Filzmoser, P. (2010) An Object-Oriented Framework for Statistical Simulation: The RPackage simFrame. Journal of Statistical Software, 37(3), 1--36. URL http://www.jstatsoft.org/v37/i03/.

See Also

simBwplot, simDensityplot, simXyplot, "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)

## plot results
tv <- mean(eusilcP$eqIncome)  # true population mean
plot(results, true = tv)



#### 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)

## plot results
plot(results, true = means)