Calculates minus the log likelihood function and its first and second order derivatives for data from a Weibull regression model.
phfunc(
beta = NULL,
lambda,
p,
X = NULL,
Y,
offset = rep(0, length(Y)),
ord = 2,
pfixed = FALSE,
dist = "weibull"
)Regression parameters
The scale paramater
The shape parameter
The design (covariate) matrix.
The response, a survival object.
Offset.
ord = 0 means only loglihood, 1 means score vector as well, 2 loglihood, score and hessian.
Logical, if TRUE the shape parameter is regarded as a known constant in the calculations, meaning that it is not cosidered in the partial derivatives.
Which distribtion? The default is "weibull", with the alternatives "loglogistic" and "lognormal".
A list with components
The log likelihood. Present if
ord >= 0
The score vector. Present if ord >= 1
The negative of the hessian. Present if ord >= 2
Note that the function returns log likelihood, score vector and minus hessian, i.e. the observed information. The model is $$S(t; p, \lambda, \beta, z) = S_0((t / \lambda)^p)^{e^(z \beta)}$$