The function dtnorm() computes the density of a truncated normal
distribution.
The function rtnorm() samples from a truncated normal distribution.
The function dttnorm() and rttnorm() compute the density and sample from
a two-sided truncated normal distribution, respectively.
The functions with suffix _cpp perform no input checks, hence are faster.
Usage
dtnorm_cpp(x, mean, sd, point, above, log = FALSE)
dttnorm_cpp(x, mean, sd, lower, upper, log = FALSE)
rtnorm_cpp(mean, sd, point, above, log = FALSE)
rttnorm_cpp(mean, sd, lower, upper, log = FALSE)
dtnorm(x, mean, sd, point, above, log = FALSE)
dttnorm(x, mean, sd, lower, upper, log = FALSE)
rtnorm(n = 1, mean, sd, point, above, log = FALSE)
rttnorm(n = 1, mean, sd, lower, upper, log = FALSE)Arguments
- x
[
numeric(1)]
A quantile.- mean
[
numeric(1)]
The mean.- sd
[
numeric(1)]
The non-negative standard deviation.- point, lower, upper
[
numeric(1)]
The truncation point.- above
[
logical(1)]
Truncate from above? Else, from below.- log
[
logical(1)]
Fordtnorm()anddttnorm(), return the logarithm of the density value?For
rtnorm()andrttnorm(), return the exponential of the draw, which is a draw from the truncated log-normal distribution?- n
[
integer(1)]
The number of requested samples.
Value
For dtnorm() and dttnorm(): The density value.
For rtnorm() and rttnorm(): A numeric of length n with the random
draws.
Details
rtnorm() draws by the rejection methods of Robert (1995), and rttnorm()
inverts the distribution function of the truncated tail, so that both
remain accurate when a truncation point lies far in the tail.
References
Robert, C. P. (1995). Simulation of truncated normal variables. Statistics and Computing, 5(2), 121-125.
See also
Other simulation helpers:
Simulator,
correlated_regressors(),
ddirichlet_cpp(),
dmixnorm_cpp(),
dmvnorm_cpp(),
dwishart_cpp(),
gaussian_tv(),
simulate_markov_chain()
Examples
# compute density
dtnorm(x = 1, mean = 0, sd = 1, point = 0, above = FALSE)
#> [1] 0.4839414
dttnorm(x = 0, mean = 0, sd = 1, lower = -1, upper = 1, log = TRUE)
#> [1] -0.5372234
# sample
rtnorm(n = 3, mean = 0, sd = 1, point = 0, above = FALSE)
#> [1] 1.152508 1.376613 2.007539
rttnorm(mean = 0, sd = 1, lower = -1, upper = 1)
#> [1] 0.470705