mclust (version 5.2.2)

sim: Simulate from Parameterized MVN Mixture Models

Description

Simulate data from parameterized MVN mixture models.

Usage

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:

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.

Value

A matrix in which first column is the classification and the remaining columns are the n observations simulated from the specified MVN mixture model. mixture model.

References

C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631. C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

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

irisBIC <- mclustBIC(iris[,-5])
irisModel <- mclustModel(iris[,-5], irisBIC)
names(irisModel)
irisSim <- sim(modelName = irisModel$modelName, 
               parameters = irisModel$parameters, 
               n = nrow(iris))

## Not run: 
#   do.call("sim", irisModel) # alternative call
# ## End(Not run)

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)
## Not run: 
#  irisModel3$n <- NULL
#  irisSim3 <- do.call("sim",c(list(n=500,seed=1),irisModel3)) # alternative call
# ## End(Not run)
clPairs(irisSim3[,-1], cl = irisSim3[,1])