Calculates minus the log likelihood function and its first and second order
derivatives for data from a Weibull regression model. Is called by
weibreg.
wfunk(beta = NULL, lambda, p, X = NULL, Y, offset = rep(0, length(Y)),
ord = 2, pfixed = FALSE)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.
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
$$h(t; p, \lambda, \beta, z) = p / \lambda (t / \lambda)^{(p-1)}\exp{(-(
t / \lambda)^p}) \exp(z\beta)$$
This is in correspondence with dweibull.