| simE {mclust} | R Documentation |
Simulate data from a parameterized MVN mixture model.
simE(mu, sigmasq, pro, ..., seed = 0) simV(mu, sigmasq, pro, ..., seed = 0) simEII(mu, sigmasq, pro, ..., seed = 0) simVII(mu, sigmasq, pro, ..., seed = 0) simEEI(mu, decomp, pro, ..., seed = 0) simVEI(mu, decomp, pro, ..., seed = 0) simEVI(mu, decomp, pro, ..., seed = 0) simVVI(mu, decomp, pro, ..., seed = 0) simEEE(mu, pro, ..., seed = 0) simEEV(mu, decomp, pro, ..., seed = 0) simVEV(mu, decomp, pro, ..., seed = 0) simVVV(mu, pro, ..., seed = 0)
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.
|
sigmasq |
for the one-dimensional models ("E", "V") and spherical models ("EII", "VII"). This is either a vector whose kth component is the variance for the kth component in the mixture model ("V" and "VII"), or a scalar giving the common variance for all components in the mixture model ("E" and "EII"). |
decomp |
for the diagonal models ("EEI", "VEI", "EVI", "VVI") and some
ellipsoidal models ("EEV", "VEV"). This is a list described in
cdens.
|
pro |
Component mixing proportions. If missing, equal proportions are assumed. |
... |
em, me, or mstep methods for the
specified mixture model.
|
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.
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
decomp <- list(d= d, G = G, scale = scale, shape = shape, orientation = O)
mu <- matrix(0, d, G) ## center at the origin
simdat <- simEEV(n=200, mu=mu, decomp=decomp, pro = c(1,1))
cl <- attr(simdat, "classification")
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)