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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