| sim {mclust} | R Documentation |
Simulate data from parameterized MVN mixture models.
sim(modelName, mu, ..., seed = 0)
modelName |
A character string indicating the model. Possible models: "E": equal variance (one-dimensional) "V": variable variance (one-dimensional) "EII": spherical, equal volume "VII": spherical, unequal volume "EEI": diagonal, equal volume, equal shape "VEI": diagonal, varying volume, equal shape "EVI": diagonal, equal volume, varying shape "VVI": diagonal, varying volume, varying shape "EEE": ellipsoidal, equal volume, shape, and orientation "EEV": ellipsoidal, equal volume and equal shape "VEV": ellipsoidal, equal shape "VVV": ellipsoidal, varying volume, shape, and orientation |
mu |
The mean for each component. If there is more than one component,
mu is a matrix whose columns are the means of the components.
|
... |
Arguments for model-specific functions. Specifically:
|
seed |
A integer between 0 and 1000, inclusive, for specifying a seed for random class assignment. The default value is 0. |
This function can be used with an indirect or list call using
do.call, allowing the output of e.g. mstep, em,
me, or EMclust to be passed directly without the need to
specify individual parameters as arguments.
A data set consisting of n points simulated from the specified MVN mixture model.
C. Fraley and A. E. Raftery (2002a). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631. See http://www.stat.washington.edu/mclust.
C. Fraley and A. E. Raftery (2002b). MCLUST:Software for model-based clustering, density estimation and discriminant analysis. Technical Report, Department of Statistics, University of Washington. See http://www.stat.washington.edu/mclust.
simE, ...,
simVVV,
EMclust,
mstep,
do.call
data(iris)
irisMatrix <- as.matrix(iris[,1:4])
irisBic <- EMclust(irisMatrix)
irisSumry <- summary(irisBic,irisMatrix)
names(irisSumry)
irisSim <- sim(modelName = irisSumry$modelName, n = dim(irisMatrix)[1],
mu = irisSumry$mu, decomp = irisSumry$decomp, pro = irisSumry$pro)
## Not run:
irisSim <- do.call("sim", irisSumry) ## alternative call
## End(Not run)
par(pty = "s", mfrow = c(1,2))
dimens <- c(1,2)
xlim <- range(rbind(irisMatrix,irisSim)[,dimens][,1])
ylim <- range(rbind(irisMatrix,irisSim)[,dimens][,2])
cl <- irisSumry$classification
coordProj(irisMatrix, par=irisSumry, classification=cl, dimens=dimens,
xlim=xlim, ylim=ylim)
cl <- attr(irisSim,"classification")
coordProj(irisSim, par=irisSumry, classification=cl, dimens=dimens,
xlim=xlim, ylim=ylim)
irisSumry3 <- summary(irisBic,irisMatrix, G=3)
irisSim3 <- do.call("sim", c(list(n = 500, seed = 1), irisSumry3))
clPairs(irisSim3, cl = attr(irisSim3,"classification"))