Weibull distribution in proportional hazards parameterisation
Density, distribution function, hazards, quantile function and random generation for the Weibull distribution in its proportional hazards parameterisation.
x, q |
Vector of quantiles. |
p |
Vector of probabilities. |
n |
number of observations. If |
shape |
Vector of shape parameters. |
scale |
Vector of scale parameters. |
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 in proportional hazards parameterisation with ‘shape’ parameter a and ‘scale’ parameter m has density given by
f(x) = a m x^{a-1} exp(- m x^a)
cumulative distribution function F(x) = 1 - exp( -m x^a ), survivor function S(x) = exp( -m x^a ), cumulative hazard m x^a and hazard a m x^{a-1}.
dweibull
in base R has the alternative 'accelerated failure
time' (AFT) parameterisation with shape a and scale b. The shape parameter
a is the same in both versions. The scale parameters are related as
b = m^{-1/a}, equivalently m = b^-a.
In survival modelling, covariates are typically included through a linear model on the log scale parameter. Thus, in the proportional hazards model, the coefficients in such a model on m are interpreted as log hazard ratios.
In the AFT model, covariates on b are interpreted as time acceleration factors. For example, doubling the value of a covariate with coefficient beta=log(2) would give half the expected survival time. These coefficients are related to the log hazard ratios γ as β = -γ / a.
dweibullPH
gives the density, pweibullPH
gives the
distribution function, qweibullPH
gives the quantile function,
rweibullPH
generates random deviates, HweibullPH
retuns the
cumulative hazard and hweibullPH
the hazard.
Christopher Jackson <chris.jackson@mrc-bsu.cam.ac.uk>
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