| hnorm {usefr} | R Documentation |
Probability density function (PDF), cummulative density function (CDF), quantile function and random generation for the Half-normal (hnorm) distribution.
dhnorm(x, theta = 1, log = FALSE) phnorm(q, theta = 1, lower.tail = TRUE, log.p = FALSE) qhnorm(p, theta = 1, sigma = NULL, lower.tail = TRUE, log.p = FALSE) rhnorm(n, theta = 1) theta2sigma(theta) sigma2theta(sigma)
theta |
numerical parameter, strictly positive (default 1). |
q |
numeric vector |
lower.tail |
logical; if TRUE (default), probabilities are P[X<=x], otherwise, P[X > x] |
log.p |
logical; if TRUE, probabilities/densities p are returned as log(p). |
sigma |
Standart deviation of the normal distribution. Here, σ = √ π/(θ√ 2) |
n |
number of observations |
An alternative parametrization to avoid issues when sigma is near zero is applied by using a scaled precision (inverse of the variance) obtained by setting θ=sqrt(π)/σ*sqrt(2). Details about these functions can be found in Wikipedia and in MathWorld. Notice that θ = 1 means σ = √ π/√ 2.
Half-normal PDF values (theta parameter) for dhnorm, Half-normal probability for phnorm, quantiles or Half-normal random generated values for rhnorm.
Robersy Sanchez (https://genomaths.com).
set.seed(123) # set a seed sigma = 1.2 theta = sigma2theta(sigma) x <- rhnorm(n = 1e5, theta = theta) hist(x, 100, freq = FALSE) curve(dhnorm(x, theta = theta), col = "red", add = TRUE) #' # Checking the function outputs for the logarithms of probabilities x <- rhalfnorm(n = 10, theta = sigma2theta(2)) x1 <- phnorm(x, theta = sigma2theta(2), log = TRUE) x2 <- phnorm(x, theta = sigma2theta(2), log = FALSE) all(round(x1, 8) == round(log(x2), 8)) x3 <- dhnorm(x, theta = sigma2theta(2), log = TRUE) x4 <- dhnorm(x, theta = sigma2theta(2), log = FALSE) all(round(x3, 8) == round(log(x4), 8))