Description Usage Arguments References Examples
View source: R/parallel_vboot.R
Validate 'glmnet' linear, logistic or cox regression using bootstrap.
1 2 3 4 5 6 7 8 9 10 11 |
fit |
Object from glmnet fit |
x |
A matrix of the predictors, each row is an observation vector. |
y |
A vector of response variable. It should be quantitative for lineal regression, a factor with two levels for logistic regression or a two-column matrix with columns named 'time' and 'status' for cox regression. |
s |
Value of the penalty parameter "lambda" selected from the original 'cv.glmnet'. |
gamma |
Value of "gamma" parameter selected for relaxed model |
nfolds |
Number of folds for cross validation as in 'cv.glmnet'. |
B |
Number of bootsrap samples |
cv_replicates |
Number of replicates for the cross-validation step |
n_cores |
number of cores to use in parallel. Default detectCores()-1 |
Jerome Friedman, Trevor Hastie, Robert Tibshirani (2010). Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 1-22. URL http://www.jstatsoft.org/v33/i01/.
Noah Simon, Jerome Friedman, Trevor Hastie, Rob Tibshirani (2011). Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent. Journal of Statistical Software, 39(5), 1-13. URL http://www.jstatsoft.org/v39/i05/.
Harrell Jr, F. E. (2015). Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. Springer.
Gordon C.S. Smith, Shaun R. Seaman, Angela M. Wood, Patrick Royston, Ian R. White (2014). Correcting for Optimistic Prediction in Small Data Sets, American Journal of Epidemiology, Volume 180, Issue 3, 1 August 2014, Pages 318-324, https://doi.org/10.1093/aje/kwu140
1 2 3 4 5 6 7 8 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.