Dear Dr. Murdoch,
First thank you very much for your reply. But it seems to me that it will
generate a point with equal probability in the two intervals. I mean if I
generage 100 points, then 50 will be in one interval and the other 50 in the
other interval. I think more points should be in the interval closer to the
mean. What do you think?
Thanks a lot,
Cindy
On 7/23/08, *Duncan Murdoch* <[EMAIL PROTECTED] <mailto:
[EMAIL PROTECTED]>> wrote:
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
