glm_logistic: Logistic and Poisson regression models

In Rfast: A Collection of Efficient and Extremely Fast R Functions

Logistic and Poisson regression models

Description

Logistic and Poisson regression models.

Usage

glm_logistic(x, y, full = FALSE,tol = 1e-09, maxiters = 100)
glm_poisson(x, y, full = FALSE,tol = 1e-09)

Arguments

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).
y	The dependent variable; a numerical vector with two values (0 and 1) for the logistic regression or integer values, 0, 1, 2,... for the Poisson regression.
full	If this is FALSE, the coefficients and the deviance will be returned only. If this is TRUE, more information is returned.
tol	The tolerance value to terminate the Newton-Raphson algorithm.
maxiters	The max number of iterations that can take place in each regression.

Details

The function is written in C++ and this is why it is very fast.

Value

When full is FALSE a list including:

be	The regression coefficients.
devi	The deviance of the model.

When full is TRUE a list including:

info	The regression coefficients, their standard error, their Wald test statistic and their p-value.
devi	The deviance.

Author(s)

Manos Papadakis <[e-mail]>

R implementation and documentation: Michail Tsagris <[e-mail]> and Manos Papadakis <[e-mail]>.

References

McCullagh, Peter, and John A. Nelder. Generalized linear models. CRC press, USA, 2nd edition, 1989.

See Also

poisson_only, logistic_only, univglms, regression

Examples

x <- matrix(rnorm(100 * 3), ncol = 3)
y <- rbinom(100, 1, 0.6)   ## binary logistic regression
a1 <- glm_logistic(x, y, full = TRUE) 
a2 <- glm(y ~ x, binomial)

x <- matrix(rnorm(100 * 3), ncol = 3)
y <- rpois(100, 10)   ## binary logistic regression
b1 <- glm_poisson(x, y, full = TRUE) 
b2 <- glm(y ~ x, poisson)

x<-y<-a1<-a2<-b1<-b2<-NULL

Rfast documentation built on Nov. 9, 2023, 5:06 p.m.