Bolasso: Bolasso model.

In SparseLearner: Sparse Learning Algorithms Using a LASSO-Type Penalty for Coefficient Estimation and Model Prediction

Description

This function performs a Bolasso logistic regression model and produces an optimal set of predictors.

Usage

Bolasso(x, y, BM = 100, kfold = 10, seed = 0123)

Arguments

x	predictor matrix.
y	response variable, a factor object with values of 0 and 1.
BM	the number of bootstrapping, with the default value 100.
kfold	the number of folds of cross validation - default is 10. Although kfold can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable is kfold=3.
seed	the seed for random sampling, with the default value 0123.

Details

This function runs the LASSO logistic regression model using several bootstrap samples of the original data, and then intersects the non-zero coefficients for estimating consistent coefficients. A specific value of BM parameter should be supplied, however BM=100 is proposed by default. Users can reduce the running time by using 3-fold CV, but the proposed 10-fold CV is assumed by default.

Value

BM	the number of bootstrapping in this procedure.
var.selected	significant variables that are selected by the Bolasso model.

References

[1] Friedman, J., Hastie, T. and Tibshirani, R. (2008). Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 1-22.

[2] Bach, F.R. (2008). Bolasso: model consistent lasso estimation through the bootstrap. Proceedings of the 25th international conference on Machine learning. ACM. pp. 33:40.

Examples

library(datasets)
head(iris)
X <- as.matrix(subset(iris, iris$Species!="setosa")[, -5])
Y <- as.factor(ifelse(subset(iris, iris$Species!="setosa")[, 5]=='versicolor', 0, 1))
# Fit a Bolasso logistic regression model
# The BM parameter in the following example is set as small value to reduce
# the running time, however the default value is proposed
Bolasso.fit <- Bolasso(x=X, y=Y, BM=5, seed=0123)
# Significant variables that are selected by the Bolasso model
Bolasso.fit$var.selected

Example output

Loading required package: glmnet
Loading required package: Matrix
Loading required package: foreach
Loaded glmnet 2.0-16

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa
Boostrap  1 : 
Sepal.Length Sepal.Width Petal.Length Petal.Width 
Boostrap  2 : 
Sepal.Width Petal.Length Petal.Width 
Boostrap  3 : 
Sepal.Length Sepal.Width Petal.Length Petal.Width 
Boostrap  4 : 
Sepal.Length Sepal.Width Petal.Length Petal.Width 
Boostrap  5 : 
Sepal.Length Sepal.Width Petal.Length Petal.Width 
Boostrap  1 : 
Sepal.Length Sepal.Width Petal.Length Petal.Width 
Boostrap  2 : 
Sepal.Width Petal.Length Petal.Width 
Boostrap  3 : 
Sepal.Width Petal.Length Petal.Width 
Boostrap  4 : 
Sepal.Width Petal.Length Petal.Width 
Boostrap  5 : 
Sepal.Width Petal.Length Petal.Width 
[1] "Sepal.Width"  "Petal.Length" "Petal.Width" 

SparseLearner documentation built on May 29, 2017, 9:18 p.m.