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Prints some basic information about the coefficients (for coef.grpnet objects), observed cross-validation error (for cv.grpnet objects), or the computed regularization path (for grpnet objects).
# S3 method for coef.grpnet
print(x, ...)# S3 method for cv.grpnet
print(x, digits = max(3, getOption("digits") - 3), ...)
# S3 method for grpnet
print(x, ...)
No return value (produces a printout)
an object of class coef.grpnet, cv.grpnet, or grpnet
the number of digits to print (must be a positive integer)
additional arguments for print (currently ignored)
Nathaniel E. Helwig <helwig@umn.edu>
For coef.grpnet objects, prints the non-zero coefficients and uses "." for coefficients shrunk to zero.
For cv.grpnet objects, prints the function call, the cross-validation type.measure, and a two-row table with information about the min and 1se solutions.
For grpnet objects, prints a data frame with columns
* nGrp: number of non-zero groups for each lambda
* Df: effective degrees of freedom for each lambda
* %Dev: percentage of null deviance explained for each lambda
* Lambda: the values of lambda
Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1-22. 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. tools:::Rd_expr_doi("10.1080/10618600.2024.2362232")
coef.grpnet for extracting coefficients
cv.grpnet for k-fold cross-validation of lambda
grpnet for fitting grpnet regularization paths
# see 'coef.grpnet' for coefficient printing examples
?coef.grpnet
# see 'cv.grpnet' for cross-validation error printing examples
?cv.grpnet
# see 'grpnet' for regularization path printing examples
?grpnet
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