On 7/23/2008 4:22 PM, Duncan Murdoch wrote:
On 7/23/2008 3:41 PM, cindy Guo wrote:
Hi, I want to generate random samples from truncated normal say
Normal(0,1)Indicator((0,1),(2,4)). It has more than one intervals. In the
library msm, it seems to me that the 'lower' and 'upper' arguments can only
be a number. I tried rtnorm(1,mean=0,sd=1, lower=c(0,2),upper=c(1,4)) and it
didn't work. Can you tell me how I can do truncated normal at more than one
intervals?
The inverse CDF method will work. For example, this untested code:
rtruncnorm <- function(n, mean=0, sd=1, lower=-Inf, upper=Inf) {
plower <- pnorm(lower, mean, sd) # get the probability values for the
# lower limits
pupper <- pnorm(upper, mean, sd) # ditto for the upper limits
p <- plower + (pupper - plower)*runif(n) # get random values between
# those
qnorm(p, mean, sd) # invert the CDFs
}
As I said, this is untested, but it should work if all of mean, sd,
lower, and upper are the same length, and in some cases where they aren't.
Duncan Murdoch
One case where the code above will *not* work is if your truncation is
too far out in the tails, e.g. a standard normal, truncated to be in the
interval [100, 101]. It is possible to do those in R, but it's tricky
to avoid rounding error: in the example above, both plower and pupper
will be exactly 1 in this case. You need to do calculations on a log
scale, and work with upper tails instead of lower ones. It all gets
kind of messy.
Duncan Murdoch
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