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"))