# Simulate from Parameterized MVN Mixture Models Simulate data from parameterized MVN mixture models. ```r sim(modelName, parameters, n, seed = NULL, ...) ``` ## Arguments - `modelName`: A character string indicating the model. The help file for `mclustModelNames` describes the available models. - `parameters`: A list with the following components: - **`pro`**: A vector whose **k**th component is the mixing proportion for the **k**th component of the mixture model. If missing, equal proportions are assumed. - **`mean`**: The mean for each component. If there is more than one component, this is a matrix whose kth column is the mean of the **k**th component of the mixture model. - **`variance`**: A list of variance parameters for the model. The components of this list depend on the model specification. See the help file for `mclustVariance` for details. - `n`: An integer specifying the number of data points to be simulated. - `seed`: An optional integer argument to `set.seed` for reproducible random class assignment. By default the current seed will be used. Reproducibility can also be achieved by calling `set.seed` before calling `sim`. - ``...``: Catches unused arguments in indirect or list calls via `do.call`. ## Returns A matrix in which first column is the classification and the remaining columns are the `n` observations simulated from the specified MVN mixture model. - **Attributes:**: `"modelName"` A character string indicating the variance model used for the simulation. ## Details This function can be used with an indirect or list call using `do.call`, allowing the output of e.g. `mstep`, `em`, `me`, `Mclust` to be passed directly without the need to specify individual parameters as arguments. ## See Also `simE`, `...`, `simVVV`, `Mclust`, `mstep`, `do.call` ## Examples ```r irisBIC <- mclustBIC(iris[,-5]) irisModel <- mclustModel(iris[,-5], irisBIC) names(irisModel) irisSim <- sim(modelName = irisModel$modelName, parameters = irisModel$parameters, n = nrow(iris)) do.call("sim", irisModel) # alternative call par(pty = "s", mfrow = c(1,2)) dimnames(irisSim) <- list(NULL, c("dummy", (dimnames(iris)[[2]])[-5])) dimens <- c(1,2) lim1 <- apply(iris[,dimens],2,range) lim2 <- apply(irisSim[,dimens+1],2,range) lims <- apply(rbind(lim1,lim2),2,range) xlim <- lims[,1] ylim <- lims[,2] coordProj(iris[,-5], parameters=irisModel$parameters, classification=map(irisModel$z), dimens=dimens, xlim=xlim, ylim=ylim) coordProj(iris[,-5], parameters=irisModel$parameters, classification=map(irisModel$z), truth = irisSim[,-1], dimens=dimens, xlim=xlim, ylim=ylim) irisModel3 <- mclustModel(iris[,-5], irisBIC, G=3) irisSim3 <- sim(modelName = irisModel3$modelName, parameters = irisModel3$parameters, n = 500, seed = 1) irisModel3$n <- NULL irisSim3 <- do.call("sim",c(list(n=500,seed=1),irisModel3)) # alternative call clPairs(irisSim3[,-1], cl = irisSim3[,1]) ```

sim function