ldmvnorm: Multivariate Normal Log-Density for Complete and Incomplete...
In snipEM: Snipping Methods for Robust Estimation and Clustering

Description Usage Arguments Author(s) Examples

This function provides the log-density function for the multivariate normal distribution with mean equal to mu and covariance matrix Sigma. Marginal distributions will be used when the vector (or matrix) of quantiles is incomplete. That is, when the vector (or matrix) of quantiles contain NA.

1	ldmvnorm(x, mu, Sigma, onNA=0)

`x`	Vector or matrix of quantiles. If x is a matrix, each row is taken to be a quantile.
`mu`	Mean vector, default is `rep(0, length = ncol(x))`
`Sigma`	Covariance matrix, default is `diag(ncol(x))`.
`onNA`	Action for a row on NAs. Default is to return `0`.

Alessio Farcomeni alessio.farcomeni@uniroma1.it, Andy Leung andy.leung@stat.ubc.ca

x <- matrix(rnorm(1000),100, 10)
u <- matrix(rbinom(1000, 1, 0.1), 100, 10)
x[ u == 1 ] <- NA
mu <- rep(0,10)
Sigma <- diag(10)
ldmvnorm(x, mu, Sigma)