Description Usage Arguments Details Value Author(s) See Also
Calculates minus the log likelihood function and its first and second order
derivatives for data from a Weibull regression model. Is called by
weibreg
.
1 2 
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. 
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)^{(p1)}\exp{(( t / λ)^p})\exp(zβ)
This is in correspondence with dweibull
.
A list with components
f 
The log likelihood. Present if

fp 
The score vector. Present if 
fpp 
The negative of the hessian. Present if 
Göran Broström
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