Weibull regression model
Weibull regression model.
weib.reg(y, x, tol = 1e-07, maxiters = 100)
y |
The dependent variable; a numerical vector with strictly positive data, i.e. greater than zero. |
x |
A matrix with the data, where the rows denote the samples (and the two groups) and the columns are the variables. This can be a matrix or a data.frame (with factors). |
tol |
The tolerance value to terminate the Newton-Raphson algorithm. |
maxiters |
The max number of iterations that can take place in each regression. |
The function is written in C++ and this is why it is very fast. No standard errors are returned as they are not corectly estimated. We focused on speed.
When full is FALSE a list including:
iters |
The iterations required by the Newton-Raphson. |
loglik |
The log-likelihood of the model. |
shape |
The shape parameter of the Weibull regression. |
be |
The regression coefficients. |
Stefanos Fafalios
R implementation and documentation: Stefanos Fafalios <stefanosfafalios@gmail.com>.
McCullagh, Peter, and John A. Nelder. Generalized linear models. CRC press, USA, 2nd edition, 1989.
## Not run: x <- matrix(rnorm(100 * 2), ncol = 2) y <- rexp(100, 1) res<-weib.reg(y, x) x <- NULL ## End(Not run)
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