simBwplot {simFrame} | R Documentation |
Generic function for producing box-and-whisker plots.
simBwplot(x, ...) ## S4 method for signature 'SimResults' simBwplot(x, true = NULL, epsilon, NArate, select, ...)
x |
the object to be plotted. For plotting simulation results, this
must be an object of class |
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 |
... |
additional arguments to be passed down to methods and eventually
to |
For simulation results with multiple contamination levels or missing value rates, conditional box-and-whisker plots are produced.
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.
x = "SimResults"
produce box-and-whisker plots of simulation results.
Functionality for producing conditional box-and-whisker plots was added in version 0.2. Prior to that, the function gave an error message if simulation results with multiple contamination levels or missing value rates were supplied.
Andreas Alfons
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. URL http://www.jstatsoft.org/v37/i03/.
simDensityplot
, simXyplot
,
bwplot
, "SimResults"
#### 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$eqIncome) # true population mean simBwplot(results, true = tv) #### model-based simulation set.seed(12345) # for reproducibility ## function for generating data rgnorm <- function(n, means) { group <- sample(1:2, n, replace=TRUE) data.frame(group=group, value=rnorm(n) + means[group]) } ## control objects for data generation and contamination means <- c(0, 0.25) dc <- DataControl(size = 500, distribution = rgnorm, dots = list(means = means)) cc <- DCARContControl(target = "value", epsilon = 0.02, dots = list(mean = 15)) ## function for simulation runs sim <- function(x) { c(mean = mean(x$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 simBwplot(results, true = means)