glm_logistic: Logistic and Poisson regression models

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

Logistic and Poisson regression models.

Usage

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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 <papadakm95@gmail.com>

R implementation and documentation: Michail Tsagris <mtsagris@uoc.gr> and Manos Papadakis <papadakm95@gmail.com>.

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

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## Not run: 
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

## End(Not run)

Rfast documentation built on Dec. 11, 2021, 9:59 a.m.