phfunc: Loglihood function of a proportional hazards regression
In goranbrostrom/eha: Event History Analysis

phfunc

R Documentation

Loglihood function of a proportional hazards regression

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"
)

`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.
`dist`	Which distribtion? The default is "weibull", with the alternatives "loglogistic" and "lognormal".

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)}

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`

Göran Broström