# X-Y plots Generic function for producing x-y plots. For simulation results, the average results are plotted against the corresponding contamination levels or missing value rates. 1.1 methods ```r 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`. ## Returns 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 Package `simFrame`. **Journal of Statistical Software**, 37 (3), 1--36. tools:::Rd_expr_doi("10.18637/jss.v037.i03") . ## See Also `simBwplot`, `simDensityplot`, `xyplot`, `"SimResults"` ## Examples ```r #### 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) ```

simXyplot function