eha: wfunk – R documentation

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wfunk

Loglihood function of a Weibull regression

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

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