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"
)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
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".
Göran Broström
Note that the function returns log likelihood, score vector and minus hessian, i.e. the observed information. The model is $$S(t; p, \lambda, \beta, z) = S_0((t / \lambda)^p)^{e^(z \beta)}$$
phreg