# allbetas: Simple linear regressions coefficients In Rfast: A Collection of Efficient and Extremely Fast R Functions

## Description

Simple linear regressions coefficients.

## Usage

 `1` ```allbetas(y, x, pvalue = FALSE, logged = FALSE) ```

## Arguments

 `y` A numerical vector with the response variable. `x` A matrix with the data, where rows denotes the observations and the columns contain the independent variables. `pvalue` If you want a hypothesis test that each slope (beta coefficient) is equal to zero set this equal to TRUE. It will also produce all the correlations between y and x. `logged` A boolean variable; it will return the logarithm of the pvalue if set to TRUE.

## Value

A matrix with the constant (alpha) and the slope (beta) for each simple linear regression. If the p-value is set to TRUE, the correlation of each y with the x is calculated along with the relevant test statistic and its associated p-value.

## Author(s)

Michail Tsagris

``` mvbetas, correls, univglms, colsums, colVars ```

## Examples

 ```1 2 3 4 5``` ```x <- matrix( rnorm(100 * 50), ncol = 50 ) y <- rnorm(100) r <- cor(y, x) ## correlation of y with each of the xs a <- allbetas(y, x) ## the coefficients of each simple linear regression of y with x x <- NULL ```

### Example output

```Loading required package: Rcpp