plot-methods {simFrame} | R Documentation |
Plot simulation results. A suitable plot function is selected automatically, depending on the structure of the results.
## S4 method for signature 'SimResults,missing' plot(x, y , ...)
x |
the simulation results. |
y |
not used. |
... |
further arguments to be passed to the selected plot function. |
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.
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.
x = "SimResults", y = "missing"
plot simulation results.
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/.
simBwplot
, simDensityplot
,
simXyplot
, "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$eqIncome) # true population mean plot(results, true = tv) #### model-based simulation set.seed(12345) # for reproducibility ## function for generating data rgnorm <- function(n, means) { group <- sample(1:2, n, replace=TRUE) data.frame(group=group, value=rnorm(n) + means[group]) } ## control objects for data generation and contamination means <- c(0, 0.25) dc <- DataControl(size = 500, distribution = rgnorm, dots = list(means = means)) cc <- DCARContControl(target = "value", epsilon = 0.02, dots = list(mean = 15)) ## function for simulation runs sim <- function(x) { c(mean = mean(x$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)