R: Many simple linear mixed model regressions

Many simple linear mixed model regressions {Rfast} | R Documentation

## Many simple linear mixed model regressions

### Description

Many simple linear mixed model regressions with random intercepts only.

### Usage

    rint.regs(y, x, id, tol = 1e-08, logged = FALSE, parallel = FALSE, maxiters = 100)

### Arguments

`y` | A numerical vector with the data. The subject values, the clustered data.
`x` | A numerical matrix with data ,the independent variables.
`id` | A numerical variable with 1, 2, ... indicating the subject. Unbalanced design is of course welcome.
`tol` | The tolerance value to terminate the Newton-Raphson algorithm. This is set to 10^{-9} by default.
`logged` | Should the p-values be returned (FALSE) or their logarithm (TRUE)?
`parallel` | Do you want this to be executed in parallel or not. The parallel takes place in C++, and the number of threads is defined by each system's availiable cores.

`maxiters` | The max number of iterations that can take place in each regression.

### Details

Many linear mixed models with a single covariate are fitted. We use Newton-Raphson as described in Demidenko (2013). The test statistic is the usual F-test. This model allows for random intercepts only.

### Value

A two-column matrix with the test statistics (Wald statistic) and the associated p-values (or their loggarithm).

### Author(s)

Stefanos Fafalios

R implementation and documentation: Stefanos Fafalios <stefanosfafalios@gmail.com>

### References

Eugene Demidenko (2013). Mixed Models: Theory and Applications with R, 2nd Edition. New Jersey: Wiley \& Sons (excellent book).

### See Also

`rint.reg, allbetas univglms, score.glms, logistic_only`

### Examples

    ## not a so good example
    y <- rnorm(100)
    id <- sample(1:10, 100, replace = TRUE)
    x <- matrix( rnorm(100 * 100), ncol = 100)
    a <- rint.regs(y, x, id)
    x <- NULL

[Package Rfast version 2.0.4 Index]