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)