| MVN | R Documentation |
These functions are tools for compute density of (mixture) multivariate Gaussian distribution with unstructured dispersion.
dmvn(x, mu, LTsigma, log = FALSE)
dlmvn(x, mu, LTsigma, log = TRUE)
dmixmvn(x, emobj = NULL, pi = NULL, Mu = NULL, LTSigma = NULL, log = FALSE)
logL(x, emobj = NULL, pi = NULL, Mu = NULL, LTSigma = NULL)
x |
the data matrix, dimension |
mu |
the centers of clusters, length |
LTsigma |
the lower triangular matrices of dispersion, length
|
log |
if logarithm returned. |
emobj |
the desired model which is a list mainly contains |
pi |
the mixing proportion, length |
Mu |
the centers of clusters, dimension |
LTSigma |
the lower triangular matrices of dispersion,
|
The dmvn and dlmvn compute density and log density of
multivariate distribution.
The dmixmvn computes density of mixture multivariate distribution
and is based either an input emobj or inputs pi,
Mu, and LTSigma to assign class id to each observation of
x.
The logL returns the value of the observed log likelihood function
of the parameters at the current values of the parameters pi,
Mu, and LTSigma, with the suplied data matrix x.
A density value is returned.
Wei-Chen Chen wccsnow@gmail.com and Ranjan Maitra.
https://www.stat.iastate.edu/people/ranjan-maitra
init.EM, emcluster.
library(EMCluster, quietly = TRUE)
x2 <- da2$da
x3 <- da3$da
emobj2 <- list(pi = da2$pi, Mu = da2$Mu, LTSigma = da2$LTSigma)
emobj3 <- list(pi = da3$pi, Mu = da3$Mu, LTSigma = da3$LTSigma)
logL(x2, emobj = emobj2)
logL(x3, emobj = emobj3)
dmixmvn2 <- dmixmvn(x2, emobj2)
dmixmvn3 <- dmixmvn(x3, emobj3)
dlmvn(da2$da[1,], da2$Mu[1,], da2$LTSigma[1,])
log(dmvn(da2$da[1,], da2$Mu[1,], da2$LTSigma[1,]))
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