wfunk: Loglihood function of a Weibull regression

In eha: Event History Analysis

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, \lambda,\beta, z) = p / \lambda (t / \lambda)^{(p-1)}\exp{(-( t / \lambda)^p})\exp(z\beta)

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

See Also

weibreg

eha documentation built on Oct. 7, 2024, 1:17 a.m.