Description Usage Arguments Value Examples
Random number generation, probability density and cumulative density functions for truncated normal distribution.
1 2 3 4 5 6 |
n |
number of observations. n must be a scalar. |
mean |
mean (must be scalar). |
sd |
standard deviation (must be scalar). |
lower |
lower truncation value (must be scalar). |
upper |
upper truncation value (must be scalar). |
x, q |
vector of quantiles; |
lt |
lower tail. If TRUE (default) probabilities are |
lp |
log probability. If TRUE (default is FALSE) probabilities p are
given as |
a column vector.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | ## rtn example
dat1 <- rtnorm(1e5, 0, 1, 0, Inf)
## dat2 <- msm::rtnorm(n, mean, sd, lower, upper)
## den2 <- density(dat2)
hist(dat1, breaks="fd", freq=F)
## lines(den2$x, den2$y, lwd=2.5)
## res <- microbenchmark(
## rtn(n, mean, sd, lower, upper),
## msm::rtnorm(n, mean, sd, lower, upper))
## print(res)
## dtn example
x <- seq(-5, 5, length.out=1e3)
dat1 <- dtnorm(x, 0, 1, -2, 2, 0)
## dat2 <- msm::dtnorm(x, mean, sd, lower, upper, 0)
plot(x, dat1, type="l", lwd=2)
## lines(x, dat2, lwd=2, lty="dashed", col="red")
## res <- microbenchmark(
## dtnorm(x, mean, sd, lower, upper, 0),
## msm::dtnorm(x, mean, sd, lower, upper, 0))
## print(res)
## ptn example
x <- seq(-50, 10, length.out=1e3)
mean <- 0
sd <- 1
lower <- 0
upper <- 5
dat1 <- ptnorm(x, 0, 1, 0, 5, lp=TRUE)
## dat2 <- msm::ptnorm(x, mean, sd, lower, upper, log.p=TRUE)
## all(dat1[,1] == dat2)
plot(x, log(dat1[,1]))
## lines(x, log(dat2), col="red", lwd=2)
## mtext("pnorm(x, log=TRUE)", adj = 0)
## mtext("log(pnorm(x))", col = "red", adj = 1)
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