# Simulate from a Parameterized MVN Mixture Model Simulate data from a parameterized MVN mixture model. ```r simE(parameters, n, seed = NULL, ...) simV(parameters, n, seed = NULL, ...) simEII(parameters, n, seed = NULL, ...) simVII(parameters, n, seed = NULL, ...) simEEI(parameters, n, seed = NULL, ...) simVEI(parameters, n, seed = NULL, ...) simEVI(parameters, n, seed = NULL, ...) simVVI(parameters, n, seed = NULL, ...) simEEE(parameters, n, seed = NULL, ...) simVEE(parameters, n, seed = NULL, ...) simEVE(parameters, n, seed = NULL, ...) simVVE(parameters, n, seed = NULL, ...) simEEV(parameters, n, seed = NULL, ...) simVEV(parameters, n, seed = NULL, ...) simEVV(parameters, n, seed = NULL, ...) simVVV(parameters, n, seed = NULL, ...) ``` ## Arguments - `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 `sim`, `Mclust`, `mstepE`, `mclustVariance`. ## Examples ```r d <- 2 G <- 2 scale <- 1 shape <- c(1, 9) O1 <- diag(2) O2 <- diag(2)[,c(2,1)] O <- array(cbind(O1,O2), c(2, 2, 2)) O variance <- list(d= d, G = G, scale = scale, shape = shape, orientation = O) mu <- matrix(0, d, G) ## center at the origin simdat <- simEEV( n = 200, parameters = list(pro=c(1,1),mean=mu,variance=variance), seed = NULL) cl <- simdat[,1] sigma <- array(apply(O, 3, function(x,y) crossprod(x*y), y = sqrt(scale*shape)), c(2,2,2)) paramList <- list(mu = mu, sigma = sigma) coordProj( simdat, paramList = paramList, classification = cl) ```

simE function