Half-normal distribution
Density, distribution function, quantile function and random generation for the half-normal distribution.
dhnorm(x, sigma = 1, log = FALSE) phnorm(q, sigma = 1, lower.tail = TRUE, log.p = FALSE) qhnorm(p, sigma = 1, lower.tail = TRUE, log.p = FALSE) rhnorm(n, sigma = 1)
x, q |
vector of quantiles. |
sigma |
positive valued scale parameter. |
log, log.p |
logical; if TRUE, probabilities p are given as log(p). |
lower.tail |
logical; if TRUE (default), probabilities are P[X ≤ x] otherwise, P[X > x]. |
p |
vector of probabilities. |
n |
number of observations. If |
If X follows normal distribution centered at 0 and parametrized by scale σ, then |X| follows half-normal distribution parametrized by scale σ. Half-t distribution with ν=∞ degrees of freedom converges to half-normal distribution.
Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian analysis, 1(3), 515-534.
Jacob, E. and Jayakumar, K. (2012). On Half-Cauchy Distribution and Process. International Journal of Statistika and Mathematika, 3(2), 77-81.
x <- rhnorm(1e5, 2) hist(x, 100, freq = FALSE) curve(dhnorm(x, 2), 0, 8, col = "red", add = TRUE) hist(phnorm(x, 2)) plot(ecdf(x)) curve(phnorm(x, 2), 0, 8, col = "red", lwd = 2, add = TRUE)
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