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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