| Type: | Package |
| Title: | Generalised Linear Models via Iteratively Reweighted Least Squares |
| Version: | 1.0 |
| Author: | Michail Tsagris [aut, cre], Nikolaos Kontemeniotis [aut], Christos Adam [aut] |
| Maintainer: | Michail Tsagris <mtsagris@uoc.gr> |
| Description: | Generalised linear models via the iteratively reweighted least squares algorithm. The functions perform logistic, Poisson and Gamma regression (ISBN:9780412317606), either for a single model or many regression models in a column-wise fashion. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Depends: | R (≥ 4.2) |
| Imports: | Rcpp (≥ 1.0.13) |
| LinkingTo: | Rcpp (≥ 1.0.13), RcppEigen |
| Suggests: | Rfast, Rfast2 |
| RoxygenNote: | 7.3.3 |
| Encoding: | UTF-8 |
| NeedsCompilation: | yes |
| Packaged: | 2025-12-06 13:43:54 UTC; Administrator |
| Repository: | CRAN |
| Date/Publication: | 2025-12-11 13:40:07 UTC |
Generalised Linear Models via Iteratively Reweighted Least Squares
Description
Description: Generalised linear models via the iteratively reweighted least squares algorithm. The functions perform logistic, Poisson and Gamma regression, either for a single model or many regression models in a column-wise fashion.
Details
| Package: | irls |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2025-12-03 |
| License: | GPL-2 |
Maintainers
Michail Tsagris mtsagris@uoc.gr.
Author(s)
Michail Tsagris mtsagris@uoc.gr, Nikolaos Kontemeniotis kontemeniotisn@gmail.com and Christos Adam pada4m4@gmail.com.
References
McCullagh, Peter, and John A. Nelder. Generalized linear models. CRC press, USA, 2nd edition, 1989.
Column-wise GLMs with IRLS
Description
GLMs with IRLS.
Usage
col.irls(y, x, type = "logistic", maxiter = 100, tol = 1e-6, parallel = FALSE)
Arguments
y |
A numberical vector with the response. Binary data for the binomial regression, count data for the Poisson regression and strictly positive continuous numbers for the Gamma regression. |
x |
A numerical matrix. |
type |
The type of regression model to perform, "logistic", "poisson" or "gamma". |
maxiter |
The maximum number of iterations to perform. |
tol |
The tolerance value to terminate the algorithm. |
parallel |
Should the models be performed in parallel? |
Details
The function does logistic, Poisson and Gamma regression via the IRLS algorithm, for each column of x.
Value
A matrix with 3 or 4 columns with the \alpha (constant) and \beta parameters, the deviance and the \phi (dispersion) parameter in case of Gamma regression.
Author(s)
Michail Tsagris, Nikolaos Kontemeniotis and Christos Adam. R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
McCullagh, Peter, and John A. Nelder. Generalized linear models. CRC press, USA, 2nd edition, 1989.
See Also
Examples
x <- as.matrix(iris[, 1:4])
y <- rbinom(150, 1, 0.5)
col.irls(y, x)
GLMs with IRLS
Description
GLMs with IRLS.
Usage
irls(y, x, type = "logistic", maxiter = 100, tol = 1e-6)
Arguments
y |
A numberical vector with the response. Binary data for the binomial regression, count data for the Poisson regression and strictly positive continuous numbers for the Gamma regression. |
x |
A numerical matrix or a vector. |
type |
The type of regression model to perform, "logistic", "poisson" or "gamma". |
maxiter |
The maximum number of iterations to perform. |
tol |
The tolerance value to terminate the algorithm. |
Details
The function does logistic, Poisson and Gamma regression via the IRLS algorithm.
Value
A list including:
coefficients |
The regression coefficients. |
vcov |
The variance covariance matrix of the coefficients. |
se |
The standard errors of the coefficients. |
phi |
The dispersion parameter |
deviance |
The deviance of the regression model. |
iters |
The number of iterations required. |
Author(s)
Michail Tsagris, Nikolaos Kontemeniotis and Christos Adam. R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
References
McCullagh, Peter, and John A. Nelder. Generalized linear models. CRC press, USA, 2nd edition, 1989.
See Also
Examples
x <- as.matrix(iris[, 1:4])
y <- rbinom(150, 1, 0.5)
irls(y, x)