simXyplot {simFrame}

X-Y plots

Description

Generic function for producing x-y plots. For simulation results, the average results are plotted against the corresponding contamination levels or missing value rates.

Usage

simXyplot(x, ...)

## S4 method for signature 'SimResults'
simXyplot(x, true = NULL, epsilon, NArate,
          select, cond = c("Epsilon", "NArate"),
          average = c("mean", "median"), ...)

Arguments

x

the object to be plotted. For plotting simulation results, this must be an object of class "SimResults".

true

a numeric vector giving the true values. If supplied, reference lines are drawn in the corresponding panels.

epsilon

a numeric vector specifying contamination levels. If supplied, the values corresponding to these contamination levels are extracted from the simulation results and plotted.

NArate

a numeric vector specifying missing value rates. If supplied, the values corresponding to these missing value rates are extracted from the simulation results and plotted.

select

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

cond

a character string; for simulation results with multiple contamination levels and multiple missing value rates, this specifies the column of the simulation results to be used for producing conditional x-y plots. If "Epsilon", conditional plots are produced for the different contamination levels. If "NArate", conditional plots are produced for the different missing value rates. The default is to use whichever results in less plots.

average

a character string specifying how the averages should be computed. Possible values are "mean" for the mean (the default) or "median" for the median.

...

additional arguments to be passed down to methods and eventually to xyplot.

Details

For simulation results with multiple contamination levels and multiple missing value rates, conditional x-y plots are produced, as specified by cond.

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.

Methods

x = "SimResults"

produce x-y plots of simulation results.

Note

Functionality for producing conditional x-y plots (including the argument cond) was added in version 0.2. Prior to that, the function gave an error message if simulation results with multiple contamination levels and multiple missing value rates were supplied.

The argument average that specifies how the averages are computed was added in version 0.1.2. Prior to that, the mean has always been used.

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

simBwplot, simDensityplot, xyplot, "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 = seq(0, 0.05, by = 0.01),
    fun = function(x) x * 25)

## function for simulation runs
sim <- function(x) {
    c(mean = mean(x$eqIncome), trimmed = mean(x$eqIncome, 0.05))
}

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

## plot results
tv <- mean(eusilcP$eqIncome)  # true population mean
simXyplot(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 = seq(0, 0.05, by = 0.01),
    dots = list(mean = 15))

## function for simulation runs
sim <- function(x) {
    c(mean = mean(x$value),
        trimmed = mean(x$value, trim = 0.05),
        median = median(x$value))
}

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

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