R: Many multivariate simple linear regressions coefficients

Many multivariate simple linear regressions coefficients {Rfast} | R Documentation

## Many multivariate simple linear regressions coefficients

### Description

Many multivariate simple linear regressions coefficients.

### Usage

    mvbetas(y, x, pvalue = FALSE)

### Arguments

`y` | A matrix with the data, where rows denotes the observations and the columns contain the dependent variables.
`x` | A numerical vector with one continuous independent variable only.
`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.

### Details

It is a function somehow opposite to the `allbetas`. Instead of having one y and many xs we have many ys and one x.

### 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 p-value.

### Author(s)

Michail Tsagris

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

### See Also

`allbetas, correls, univglms`

### Examples

    y <- matrnorm(100, 100)
    x <- rnorm(100)
    a <- mvbetas(y, x, pvalue = FALSE)
    b <- matrix(nrow = 100, ncol = 2)
    z <- cbind(1, x)

    system.time( a <- mvbetas(y, x) )
    b[2, ] <- coef( lm.fit( z, y[, 1] ) )
    b[2, ] <- coef( lm.fit( z, y[, 2] ) )
    x <- NULL

[Package Rfast version 2.0.0 Index]