simE {mclust}R Documentation

Simulate from a Parameterized MVN Mixture Model

Description

Simulate data from a parameterized MVN mixture model.

Usage

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)

Arguments

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.
...
  • Other terms describing variance:
    Sigma
    for the equal variance model "EEE". A d by d matrix giving the common covariance for all components of the mixture model.
    sigma
    for the unconstrained variance model "VVV". A d by d by G matrix array whose [,,k]th entry is the covariance matrix for the kth component of the mixture model.
    The form of the variance specification is the same as for the output for the em, me, or mstep methods for the specified mixture model.
    n
    An integer specifying the number of data points to be simulated.
  • seed A integer between 0 and 1000, inclusive, for specifying a seed for random class assignment. The default value is 0.

    Details

    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.

    Value

    A data set consisting of n points simulated from the specified MVN mixture model.

    References

    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.

    See Also

    sim, EMclust, mstepE, do.call

    Examples

    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)
    

    [Package mclust version 2.1-11 Index]