R: fit a glm with all the options in 'glmnet'

bigGlm {glmnet} | R Documentation

## fit a glm with all the options in `glmnet`

### Description

Fit a generalized linear model as in `glmnet` but unpenalized. This allows all the features of `glmnet` such as sparse x, bounds on coefficients, offsets, and so on.

### Usage

    bigGlm(x, ..., path = FALSE)

### Arguments

`x` | input matrix
`...` | Most other arguments to glmnet that make sense
`path` | Since `glmnet` does not do stepsize optimization, the Newton algorithm can get stuck and not converge, especially with unpenalized fits. With `path=TRUE`, the fit computed with pathwise lasso regularization. The current implementation does this twice: the first time to get the lambda sequence, and the second time with a zero attached to the end). Default is `path=FALSE`.

### Details

This is essentially the same as fitting a "glmnet" model with a single value `lambda=0`, but it avoids some edge cases. CAVEAT: If the user tries a problem with N smaller than or close to p for some models, it is likely to fail (and maybe not gracefully!) If so, use the `path=TRUE` argument.

### Value

It returns an object of class "bigGlm" that inherits from class "glmnet". That means it can be predicted from, coefficients extracted via `coef`. It has its own print method.

### Author(s)

Trevor Hastie
Maintainer: Trevor Hastie hastie@stanford.edu

### See Also

`print`, `predict`, and `coef` methods.

### Examples

    # Gaussian
    x = matrix(rnorm(100 * 20), 100, 20)
    y = rnorm(100)
    fit1 = bigGlm(x, y)
    print(fit1)

    fit2=bigGlm(x,y>0,family="binomial")
    print(fit2)
    fit2p=bigGlm(x,y>0,family="binomial",path=TRUE)
    print(fit2p)

[Package glmnet version 4.0-2 Index]