R: Forward Selection of Covariates for Multiple Regression

forward {statmod} | R Documentation

## Forward Selection of Covariates for Multiple Regression

### Description

Fit a multi-group negative-binomial model to SAGE data, with Pearson estimation of the common overdispersion parameter.

### Usage

    forward(y, x, xkept=NULL, intercept=TRUE, nvar=ncol(x))

### Arguments

`y` | numeric response vector.
`x` | numeric matrix of covariates, candidates to be added to the regression.
`xkept` | numeric matrix of covariates to be included in the starting regression.
`intercept` | logical, should an intercept be added to `xkept`?
`nvar` | integer, number of covariates from `x` to add to the regression.

### Details

This function has the advantage that `x` can have many more columns than the length of `y`.

### Value

Integer vector of length `nvar`, giving the order in which columns of `x` are added to the regression.

### Author(s)

Gordon Smyth

### See Also

`step`

### Examples

    y <- rnorm(10)
    x <- matrix(rnorm(10*5),10,5)
    forward(y,x)

[Package statmod version 1.4.36 Index]