rtcmvnorm
From MCMCglmm v2.30
by Jarrod Hadfield
Random Generation from a Truncated Conditional Normal Distribution
Samples from the Truncated Conditional Normal Distribution
- Keywords
- distribution
Usage
rtcmvnorm(n = 1, mean = 0, V = 1, x=0, keep=1, lower = -Inf, upper = Inf)Arguments
- n
integer: number of samples to be drawn
- mean
vector of means
- V
covariance matrix
- x
vector of observations to condition on
- keep
element of x to be sampled
- lower
left truncation point
- upper
right truncation point
Value
vector
Examples
# NOT RUN {
par(mfrow=c(2,1))
V1<-cbind(c(1,0.5), c(0.5,1))
x1<-rtcmvnorm(10000, c(0,0), V=V1, c(0,2), keep=1, lower=-1, upper=1)
x2<-rtnorm(10000, 0, 1, lower=-1, upper=1)
plot(density(x1), main="Correlated conditioning observation")
lines(density(x2), col="red")
# denisties of conditional (black) and unconditional (red) distribution
# when the two variables are correlated (r=0.5)
V2<-diag(2)
x3<-rtcmvnorm(10000, c(0,0), V=V2, c(0,2), keep=1, lower=-1, upper=1)
x4<-rtnorm(10000, 0, 1, lower=-1, upper=1)
plot(density(x3), main="Uncorrelated conditioning observation")
lines(density(x4), col="red")
# denisties of conditional (black) and unconditional (red) distribution
# when the two variables are uncorrelated (r=0)
# }
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