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eha (version 2.11.5)

phfunc: Loglihood function of a proportional hazards regression

Description

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

Usage

phfunc(
  beta = NULL,
  lambda,
  p,
  X = NULL,
  Y,
  offset = rep(0, length(Y)),
  ord = 2,
  pfixed = FALSE,
  dist = "weibull"
)

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

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.

dist

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

Author

Göran Broström

Details

Note that the function returns log likelihood, score vector and minus hessian, i.e. the observed information. The model is $$S(t; p, \lambda, \beta, z) = S_0((t / \lambda)^p)^{e^(z \beta)}$$

See Also

phreg