coef | R Documentation |

Obtain coefficients from a cross-validated group elastic net regularized GLM (cv.grpnet) or a group elastic net regularized GLM (grpnet) object.

```
## S3 method for class 'cv.grpnet'
coef(object,
s = c("lambda.1se", "lambda.min"),
...)
## S3 method for class 'grpnet'
coef(object,
s = NULL,
...)
```

`object` |
Object of class "cv.grpnet" or "grpnet" |

`s` |
Lambda value(s) at which predictions should be obtained. For "cv.grpnet" objects, default uses the 1se solution. For "grpnet" objects, default uses |

`...` |
Additional arguments (ignored) |

*coef.cv.grpnet*:

Returns the coefficients that are used by the `predict.cv.grpnet`

function to form predictions from a fit `cv.grpnet`

object.

*coef.grpnet*:

Returns the coefficients that are used by the `predict.grpnet`

function to form predictions from a fit `grpnet`

object.

For multinomial response variables, returns a list of length `length(object$ylev)`

, where the `j`

-th element is a matrix of dimension `c(ncoef, length(s))`

giving the coefficients for `object$ylev[j]`

.

For other response variables, returns a matrix of dimension `c(ncoef, length(s))`

, where the `i`

-th column gives the coefficients for `s[i]`

.

The syntax of these functions closely mimics that of the `coef.cv.glmnet`

and `coef.glmnet`

functions in the **glmnet** package (Friedman, Hastie, & Tibshirani, 2010).

Nathaniel E. Helwig <helwig@umn.edu>

Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. *Journal of Statistical Software, 33*(1), 1-22. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v033.i01")}

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")}

`print.coef.grpnet`

for printing `coef.grpnet`

objects

`predict.cv.grpnet`

for predicting from `cv.grpnet`

objects

`predict.grpnet`

for predicting from `grpnet`

objects

```
######***###### grpnet ######***######
# load data
data(auto)
# fit model (formula method, response = mpg)
mod <- grpnet(mpg ~ ., data = auto)
# extract coefs for regularization path (output = 12 x 100 matrix)
coef(mod)
# extract coefs at 3 particular points (output = 12 x 3 matrix)
coef(mod, s = c(1.5, 1, 0.5))
######***###### cv.grpnet ######***######
# load data
data(auto)
# 5-fold cv (formula method, response = mpg)
set.seed(1)
mod <- cv.grpnet(mpg ~ ., data = auto, nfolds = 5, alpha = 1)
# extract coefs for "min" solution (output = 12 x 1 matrix)
coef(mod)
# extract coefs for "1se" solution (output = 12 x 1 matrix)
coef(mod, s = "lambda.1se")
# extract coefs at 3 particular points (output = 12 x 3 matrix)
coef(mod, s = c(1.5, 1, 0.5))
```

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