| sim {mclust} | R Documentation |
Simulate data from parameterized MVN mixture models.
sim(modelName, parameters, n, seed = NULL, ...)
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.
|
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.
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: |
|
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 and A. E. Raftery (2006). MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering, Technical Report no. 504, Department of Statistics, University of Washington.
simE, ...,
simVVV,
Mclust,
mstep,
do.call
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])