wfunk: Loglihood function of a Weibull regression

Description Usage Arguments Details Value Author(s) See Also

View source: R/wfunk.R

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

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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

See Also

weibreg


goranbrostrom/eha documentation built on March 19, 2018, 11:10 a.m.