MultivariateNormal | R Documentation |
Density and random generation for the multivariate normal distribution, using the Cholesky factor of either the precision matrix or the covariance matrix.
dmnorm_chol(x, mean, cholesky, prec_param = TRUE, log = FALSE)
rmnorm_chol(n = 1, mean, cholesky, prec_param = TRUE)
x |
vector of values. |
mean |
vector of values giving the mean of the distribution. |
cholesky |
upper-triangular Cholesky factor of either the precision matrix (when |
prec_param |
logical; if TRUE the Cholesky factor is that of the precision matrix; otherwise, of the covariance matrix. |
log |
logical; if TRUE, probability density is returned on the log scale. |
n |
number of observations (only |
See Gelman et al., Appendix A or the BUGS manual for mathematical details. The rate matrix as used here is defined as the inverse of the scale matrix, S^{-1}
, given in Gelman et al.
dmnorm_chol
gives the density and rmnorm_chol
generates random deviates.
Christopher Paciorek
Gelman, A., Carlin, J.B., Stern, H.S., and Rubin, D.B. (2004) Bayesian Data Analysis, 2nd ed. Chapman and Hall/CRC.
Distributions for other standard distributions
mean <- c(-10, 0, 10)
covmat <- matrix(c(1, .9, .3, .9, 1, -0.1, .3, -0.1, 1), 3)
ch <- chol(covmat)
x <- rmnorm_chol(1, mean, ch, prec_param = FALSE)
dmnorm_chol(x, mean, ch, prec_param = FALSE)
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