# forward

From statmod v1.4.2
by Gordon Smyth

##### Forward Selection of Covariates for Multiple Regression

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

- Keywords
- regression

##### 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.

##### See Also

##### Examples

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

*Documentation reproduced from package statmod, version 1.4.2, License: LGPL (>= 2)*

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