Many univariate simple logistic and Poisson regressions {Rfast}

Many univariate simple binary logistic regressions

Description

It performs very many univariate simple binary logistic regressions.

Usage

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

Arguments

x

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.

y

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.

tol

The tolerance value to terminate the Newton-Raphson algorithm.

b_values

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

Details

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.

Value

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

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

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

Examples

## 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