R: Many univariate simple linear regressions

Many univariate simple linear regressions {Rfast} | R Documentation

## Many univariate simple linear regressions

### Description

It performs very many univariate simple linear regressions with or without categorical variables.

### Usage

    regression(x, y, poia = NULL, logged = FALSE)

### Arguments

`x` | A data.frame or a matrix with the data, where the rows denote the samples (and the two groups) and the columns are the variables. A data frame is expected if you have categorical predictor variables. If you only have continuous predictor variables you should the function `allbetas` instead as it is faster.
`y` | The dependent variable; a numerical vector.
`poia` | If the "x" is a data.frame and you know the indices of the columns which are categorical variables supply it here.
`logged` | Do you want the logarithm of the p-values be returned? The default value is FALSE.

### Details

Some parts of the function will be transferred in C++. 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 matrix with two columns, the test statistic value and its corresponding (logged) p-value.

### Author(s)

Manos Papadakis <papadakm95@gmail.com>

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

### References

Draper, N.R. and Smith H. (1988). Applied regression analysis. New York, Wiley, 3rd edition.

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

### See Also

`univglms, allbetas, correls, univglms, mvbetas`

### Examples

    y <- rnorm(150)
    a <- regression(iris, y)
    a
    summary(lm(y ~ iris[, 5]) )  ## check the F-test

[Package Rfast version 2.0.4 Index]