# wfunk: Loglihood function of a Weibull regression In goranbrostrom/eha: Event History Analysis

## Description

Calculates minus the log likelihood function and its first and second order derivatives for data from a Weibull regression model. Is called by `weibreg`.

## Usage

 ```1 2``` ```wfunk(beta = NULL, lambda, p, X = NULL, Y, offset = rep(0, length(Y)), ord = 2, pfixed = FALSE) ```

## Arguments

 `beta` Regression parameters `lambda` The scale paramater `p` The shape parameter `X` The design (covariate) matrix. `Y` The response, a survival object. `offset` Offset. `ord` ord = 0 means only loglihood, 1 means score vector as well, 2 loglihood, score and hessian. `pfixed` 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.

## Details

Note that the function returns log likelihood, score vector and minus hessian, i.e. the observed information. The model is

h(t; p, λ,β, z) = p / λ (t / λ)^{(p-1)}\exp{(-( t / λ)^p})\exp(zβ)

This is in correspondence with `dweibull`.

## Value

A list with components

 `f` The log likelihood. Present if `ord >= 0` `fp` The score vector. Present if `ord >= 1` `fpp` The negative of the hessian. Present if `ord >= 2`

## Author(s)

Göran Broström

`weibreg`