| Weibull | R Documentation |
Density, distribution function, quantile function and random
generation for the Weibull distribution with parameters alpha
(or shape) and beta (or scale).
This special Rlab implementation allows the parameters alpha
and beta to be used, to match the function description
often found in textbooks.
dweibull(x, shape, scale = 1, alpha = shape, beta = scale, log = FALSE)
pweibull(q, shape, scale = 1, alpha = shape, beta = scale,
lower.tail = TRUE, log.p = FALSE)
qweibull(p, shape, scale = 1, alpha = shape, beta = scale,
lower.tail = TRUE, log.p = FALSE)
rweibull(n, shape, scale = 1, alpha = shape, beta = scale)
x, q |
vector of quantiles. |
p |
vector of probabilities. |
n |
number of observations. If |
shape, scale |
shape and scale parameters, the latter defaulting to 1. |
alpha, beta |
alpha and beta parameters, the latter defaulting to 1. |
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]. |
The Weibull distribution with alpha (or shape)
parameter a and beta (or scale) parameter
b has density given by
f(x) = (a/b) (x/b)^(a-1) exp(- (x/b)^a)
for x > 0. The cumulative is F(x) = 1 - exp(- (x/b)^a), the mean is E(X) = b Gamma(1 + 1/a), and the Var(X) = b^2 * (gamma(1 + 2/a) - (gamma(1 + 1/a))^2).
dweibull gives the density,
pweibull gives the distribution function,
qweibull gives the quantile function, and
rweibull generates random deviates.
The cumulative hazard H(t) = - log(1 - F(t))
is -pweibull(t, a, b, lower = FALSE, log = TRUE) which is just
H(t) = {(t/b)}^a.
dexp for the Exponential which is a special case of a
Weibull distribution.
x <- c(0,rlnorm(50)) all.equal(dweibull(x, alpha = 1), dexp(x)) all.equal(pweibull(x, alpha = 1, beta = pi), pexp(x, rate = 1/pi)) ## Cumulative hazard H(): all.equal(pweibull(x, 2.5, pi, lower=FALSE, log=TRUE), -(x/pi)^2.5, tol=1e-15) all.equal(qweibull(x/11, alpha = 1, beta = pi), qexp(x/11, rate = 1/pi))
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