# Method for aggregating simulation results Aggregate simulation results, i.e, split the data into subsets if applicable and compute summary statistics. methods ```r ## S4 method for signature 'SimResults' aggregate(x, select = NULL, FUN = mean, ...) ``` ## Arguments - `x`: the simulation results to be aggregated, i.e., an object of class `"SimResults"`. - `select`: a character vector specifying the columns to be aggregated. It must be a subset of the `colnames` slot of `x`, which is the default. - `FUN`: a scalar function to compute the summary statistics (defaults to `mean`). - ``...``: additional arguments to be passed down to `aggregate` or `apply`. ## Details If contamination or missing values have been inserted or the simulations have been split into different domains, `aggregate` is called to compute the summary statistics for the respective subsets. Otherwise, `apply` is called to compute the summary statistics for each column specified by `select`. ## Returns If contamination or missing values have been inserted or the simulations have been split into different domains, a `data.frame` is returned, otherwise a vector. ## Methods - **`x = "SimResults"`**: aggregate simulation results. ## 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 `aggregate`, `apply`, `"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 = 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) ## aggregate aggregate(results) # means of results aggregate(results, FUN = sd) # standard deviations of results #### 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) ## aggregate aggregate(results) # means of results aggregate(results, FUN = sd) # standard deviations of results ```

aggregate-methods function