View source: R/family.grpnet.R

family.grpnet | R Documentation |

Takes in the `family`

argument from `grpnet`

and returns a list containing the information needed for fitting and/or tuning the model.

```
family.grpnet(object, theta = 1)
```

`object` |
one of the following characters specifying the exponential family: |

`theta` |
Additional ("size") parameter for negative binomial responses, where the variance function is defined as |

There is only one available link function for each `family`

:

* gaussian (identity): `\mu = \mathbf{X}^\top \boldsymbol\beta`

* binomial (logit): `\log(\frac{\pi}{1 - \pi}) = \mathbf{X}^\top \boldsymbol\beta`

* multinomial (symmetric): `\pi_\ell = \frac{\exp(\mathbf{X}^\top \boldsymbol\beta_\ell)}{\sum_{l = 1}^m \exp(\mathbf{X}^\top \boldsymbol\beta_l)}`

* poisson (log): `\log(\mu) = \mathbf{X}^\top \boldsymbol\beta`

* negative.binomial (log): `\log(\mu) = \mathbf{X}^\top \boldsymbol\beta`

* Gamma (log): `\log(\mu) = \mathbf{X}^\top \boldsymbol\beta`

* inverse.gaussian (log): `\log(\mu) = \mathbf{X}^\top \boldsymbol\beta`

List with components:

`family` |
same as input object, i.e., character specifying the family |

`linkinv` |
function for computing inverse of link function |

`dev.resids` |
function for computing deviance residuals |

For `gaussian`

family, this returns the full output produced by `gaussian`

.

Nathaniel E. Helwig <helwig@umn.edu>

Helwig, N. E. (2024). Versatile descent algorithms for group regularization and variable selection in generalized linear models. *Journal of Computational and Graphical Statistics*. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10618600.2024.2362232")}

`grpnet`

for fitting group elastic net regularization paths

`cv.grpnet`

for k-fold cross-validation of `lambda`

```
family.grpnet("gaussian")
family.grpnet("binomial")
family.grpnet("multinomial")
family.grpnet("poisson")
family.grpnet("negbin", theta = 10)
family.grpnet("Gamma")
family.grpnet("inverse.gaussian")
```

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