logistic_only: Many univariate simple binary logistic regressions

View source: R/many_regression_models_correlations.R

Many univariate simple logistic and Poisson regressionsR Documentation

Many univariate simple binary logistic regressions


It performs very many univariate simple binary logistic regressions.


logistic_only(x, y, tol = 1e-09, b_values = FALSE)
poisson_only(x, y, tol = 1e-09, b_values = FALSE)



A matrix with the data, where the rows denote the samples (and the two groups) and the columns are the variables. Currently only continuous variables are allowed.


The dependent variable; a numerical vector with two values (0 and 1) for the logistic regressions and a vector with many discrete values (count data) for the Poisson regressions.


The tolerance value to terminate the Newton-Raphson algorithm.


Do you want the values of the coefficients returned? If yes, set this to TRUE.


The function is written in C++ and this is why it is very fast. It can accept thousands of predictor variables. It is usefull for univariate screening. We provide no p-value correction (such as fdr or q-values); this is up to the user.


A vector with the deviance of each simple binayr logistic regression model for each predictor variable.


Manos Papadakis <papadakm95@gmail.com>

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


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

See Also

univglms, score.glms, prop.regs, quasi.poisson_only, allbetas, correls, regression


## 300 variables, hence 300 univariate regressions are to be fitted
x <- matrix( rnorm(100 * 300), ncol = 300 )

## 100 observations in total
y <- rbinom(100, 1, 0.6)   ## binary logistic regression
a1 <- logistic_only(x, y)
a2 <- glm(y ~ x[, 1], binomial)$deviance 
a2 <- as.vector(a2)

y <- rpois(100, 10)
a1 <- poisson_only(x, y) 

a1 <- x <- NULL

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