| Title: | Add Nonparametric Bootstrap SE to 'glmnet' for Selected Coefficients (No Shrinkage) |
|---|---|
| Description: | Builds a LASSO, Ridge, or Elastic Net model with 'glmnet' or 'cv.glmnet' with bootstrap inference statistics (SE, CI, and p-value) for selected coefficients with no shrinkage applied for them. Model performance can be evaluated on test data and an automated alpha selection is implemented for Elastic Net. Parallelized computation is used to speed up the process. The methods are described in Friedman et al. (2010) <doi:10.18637/jss.v033.i01> and Simon et al. (2011) <doi:10.18637/jss.v039.i05>. |
| Authors: | Sebastian Bahr [cre, aut] |
| Maintainer: | Sebastian Bahr <[email protected]> |
| License: | GPL-3 |
| Version: | 0.0.1 |
| Built: | 2025-02-23 03:00:37 UTC |
| Source: | https://github.com/sebastianbahr/glmnetse |
Builds a LASSO, Ridge, or Elastic Net model with glmnet or cv.glmnet with bootstrap inference statistics (SE, CI, and p-value) for selected coefficients with no shrinkage applied for them. Model performance can be evaluated on test data and an automated alpha selection is implemented for Elastic Net. Parallelized computation is used to speed up the process.
glmnetSE( data, cf.no.shrnkg, alpha = 1, method = "10CVoneSE", test = "none", r = 250, nlambda = 100, seed = 0, family = "gaussian", type = "basic", conf = 0.95, perf.metric = "mse", ncore = "mx.core" )glmnetSE( data, cf.no.shrnkg, alpha = 1, method = "10CVoneSE", test = "none", r = 250, nlambda = 100, seed = 0, family = "gaussian", type = "basic", conf = 0.95, perf.metric = "mse", ncore = "mx.core" )
data |
A data frame, tibble, or matrix object with the outcome variable in the first column and the feature variables in the following columns. Note: all columns beside the first one are used as feature variables. Feature selection has to be done beforehand. |
cf.no.shrnkg |
A character string of the coefficients whose effect size will be interpreted, the inference statistic is of interest and therefore no shrinkage will be applied. |
alpha |
Alpha value [0,1]. An alpha of 0 results in a ridge regression, a value of 1 in a LASSO, and a value between 0 and 1 in an Elastic Net. If a sequence of possible alphas is passed to the |
method |
A character string defining if 10-fold cross-validation is used or not. Possible methods are |
test |
A data frame, tibble, or matrix object with the same outcome and feature variables as supplied to |
r |
Number of nonparametric bootstrap repetitions - default is 250 |
nlambda |
Number of tested lambda values - default is 100. |
seed |
Seed set for the cross-validation and bootstrap sampling - default 0 which means no seed set. |
family |
A character string representing the used model family either |
type |
A character string indicating the type of calculated bootstrap intervals. It can be |
conf |
Indicates the confidence interval level - default is 0.95. |
perf.metric |
A character string indicating the used performance metric to evaluate the performance of different lambdas and the final model. Can be either |
ncore |
A numerical value indicates the number of build clusters and used cores in the computation. If not defined the maximum available number of cores of the OS -1 is used |
glmnetSE object which output can be displayed using summary() or summary.glmnetSE(). If family binomial and performance metric auc is used it is possible to plot the ROC curve with plot() or plot.glmnetSE().
Sebastian Bahr, [email protected]
Friedman J., Hastie T. and Tibshirani R. (2010). Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 1-22. https://www.jstatsoft.org/v33/i01/.
Simon N., Friedman J., Hastie T. and Tibshirani R. (2011). Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent. Journal of Statistical Software, 39(5), 1-13. https://www.jstatsoft.org/v39/i05/.
Efron, B. and Tibshirani, R. (1993) An Introduction to the Bootstrap. Chapman & Hall. https://cds.cern.ch/record/526679/files/0412042312_TOC.pdf
Sloan T.M., Piotrowski M., Forster T. and Ghazal P. (2014) Parallel Optimization of Bootstrapping in R. https://arxiv.org/ftp/arxiv/papers/1401/1401.6389.pdf
summary.glmnetSE and plot.glmnetSE methods.
# LASSO model with gaussian function, no cross validation, a seed of 123, and # the coefficient of interest is Education. Two cores are used for the computation glmnetSE(data=swiss, cf.no.shrnkg = c("Education"), alpha=1, method="none", seed = 123, ncore = 2) # Ridge model with binomial function, 10-fold cross validation selecting the lambda # at which the smallest MSE is achieved, 500 bootstrap repetitions, no seed, the # misclassification error is used as performance metric, and the coefficient of # interest are Education and Catholic. Two cores are used for the computation. # Generate dichotom variable swiss$Fertility <- ifelse(swiss$Fertility >= median(swiss$Fertility), 1, 0) glmnetSE(data=swiss, cf.no.shrnkg = c("Education", "Catholic"), alpha=0, method="10CVmin", r=500, seed = 0, family="binomial", perf.metric = "class", ncore = 2) # Elastic Net with gaussian function, automated alpha selection, selection the lambda # within one standard deviation of the best model, test data to obtain the performance # metric on it, a seed of 123, bias-corrected and accelerated confidence intervals, a # level of 0.9, the performance metric MAE, and the coefficient of interest is Education. # Two cores are used for the computation # Generate a train and test set set.seed(123) train_sample <- sample(nrow(swiss), 0.8*nrow(swiss)) swiss.train <- swiss[train_sample, ] swiss.test <- swiss[-train_sample, ] glmnetSE(data=swiss.train, cf.no.shrnkg = c("Education"), alpha=seq(0.1,0.9,0.1), method="10CVoneSE", test = swiss.test, seed = 123, family = "gaussian", type = "bca", conf = 0.9, perf.metric = "mae", ncore = 2)# LASSO model with gaussian function, no cross validation, a seed of 123, and # the coefficient of interest is Education. Two cores are used for the computation glmnetSE(data=swiss, cf.no.shrnkg = c("Education"), alpha=1, method="none", seed = 123, ncore = 2) # Ridge model with binomial function, 10-fold cross validation selecting the lambda # at which the smallest MSE is achieved, 500 bootstrap repetitions, no seed, the # misclassification error is used as performance metric, and the coefficient of # interest are Education and Catholic. Two cores are used for the computation. # Generate dichotom variable swiss$Fertility <- ifelse(swiss$Fertility >= median(swiss$Fertility), 1, 0) glmnetSE(data=swiss, cf.no.shrnkg = c("Education", "Catholic"), alpha=0, method="10CVmin", r=500, seed = 0, family="binomial", perf.metric = "class", ncore = 2) # Elastic Net with gaussian function, automated alpha selection, selection the lambda # within one standard deviation of the best model, test data to obtain the performance # metric on it, a seed of 123, bias-corrected and accelerated confidence intervals, a # level of 0.9, the performance metric MAE, and the coefficient of interest is Education. # Two cores are used for the computation # Generate a train and test set set.seed(123) train_sample <- sample(nrow(swiss), 0.8*nrow(swiss)) swiss.train <- swiss[train_sample, ] swiss.test <- swiss[-train_sample, ] glmnetSE(data=swiss.train, cf.no.shrnkg = c("Education"), alpha=seq(0.1,0.9,0.1), method="10CVoneSE", test = swiss.test, seed = 123, family = "gaussian", type = "bca", conf = 0.9, perf.metric = "mae", ncore = 2)
Plot the ROC curve of a fitted model glmnetSE (family binomial and performance metric auc) on supplied test data.
## S3 method for class 'glmnetSE' plot(x, ...)## S3 method for class 'glmnetSE' plot(x, ...)
x |
A model of the class |
... |
Additional arguments affecting the plot produced. |
The ROC curve of a glmnetSE object.
# Generate dichotom variable swiss$Fertility <- ifelse(swiss$Fertility >= median(swiss$Fertility), 1, 0) # Generate a train and test set set.seed(1234) train_sample <- sample(nrow(swiss), 0.8*nrow(swiss)) swiss.train <- swiss[train_sample, ] swiss.test <- swiss[-train_sample, ] # Estimate model glmnetSE.model <- glmnetSE(data=swiss.train, cf.no.shrnkg = c("Education"), alpha=seq(0.1,0.9,0.1), method = "10CVoneSE", test = swiss.test, seed = 123, family = "binomial", perf.metric = "auc", ncore = 2) # Plot ROC curve of the fitted model on swiss.test data plot(glmnetSE.model)# Generate dichotom variable swiss$Fertility <- ifelse(swiss$Fertility >= median(swiss$Fertility), 1, 0) # Generate a train and test set set.seed(1234) train_sample <- sample(nrow(swiss), 0.8*nrow(swiss)) swiss.train <- swiss[train_sample, ] swiss.test <- swiss[-train_sample, ] # Estimate model glmnetSE.model <- glmnetSE(data=swiss.train, cf.no.shrnkg = c("Education"), alpha=seq(0.1,0.9,0.1), method = "10CVoneSE", test = swiss.test, seed = 123, family = "binomial", perf.metric = "auc", ncore = 2) # Plot ROC curve of the fitted model on swiss.test data plot(glmnetSE.model)
Print the coefficients with standard errors, confidence intervals, and p-values of a glmnetSE model. The inference statistics are only available for the coefficients without shrinkage applied. They would be biased otherwise. Only if cross-fold validation is used in the glmnetSE model, the selected performance metric is displayed. If test data is supplied the performance metric on the train as test data is displayed.
## S3 method for class 'glmnetSE' summary(object, ...)## S3 method for class 'glmnetSE' summary(object, ...)
object |
A model of the class |
... |
Additional arguments affecting the summary produced. |
The output of a glmnetSE object and the performance metric if cross-fold validation is used.
# Estimate model glmnetSE.model <- glmnetSE(data=swiss,cf.no.shrnkg = c("Education"), ncore = 2) # Display model output with summary summary(glmnetSE.model)# Estimate model glmnetSE.model <- glmnetSE(data=swiss,cf.no.shrnkg = c("Education"), ncore = 2) # Display model output with summary summary(glmnetSE.model)