Print.bagging: Print a bagging object.

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

Description

This function prints a summary of the bagging object fitted by the bagging.lasso function.

Usage

Print.bagging(x)

Arguments

x	a fitted bagging object.

Details

The call that produced the object Bagging.lasso is printed.

References

[1] Guo, P., Zeng, F., Hu, X., Zhang, D., Zhu, S., Deng, Y., & Hao, Y. (2015). Improved Variable Selection Algorithm Using a LASSO-Type Penalty, with an Application to Assessing Hepatitis B Infection Relevant Factors in Community Residents. PLoS One, 27;10(7):e0134151.

[2] Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the royal statistical society series B (statistical methodology), 73(3):273-282.

[3] Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.

Examples

library(mlbench)
set.seed(0123)
mydata <- mlbench.threenorm(100, d=10)
x <- mydata$x
y <- mydata$classes
mydata <- as.data.frame(cbind(x, y))
colnames(mydata) <- c(paste("A", 1:10, sep=""), "y")
mydata$y <- ifelse(mydata$y==1, 0, 1)
# Split into training and testing data.
S1 <- as.vector(which(mydata$y==0))
S2 <- as.vector(which(mydata$y==1))
S3 <- sample(S1, ceiling(length(S1)*0.8), replace=FALSE)
S4 <- sample(S2, ceiling(length(S2)*0.8), replace=FALSE)
TrainInd <- c(S3, S4)
TestInd <- setdiff(1:length(mydata$y), TrainInd)
TrainXY <- mydata[TrainInd, ]
TestXY <- mydata[TestInd, ]
# Fit a bagging LASSO linear regression model, where the parameters
# of M in the following example is set as small values to reduce the
# running time, however the default value is proposed.
Bagging.fit <- Bagging.lasso(x=TrainXY[, -10], y=TrainXY[, 10],
family=c("gaussian"), M=2, predictor.subset=round((9/10)*ncol(x)),
predictor.importance=FALSE, trimmed=FALSE, weighted=TRUE, seed=0123)
# Print a 'bagging' object fitted by the Bagging.fit function.
Print.bagging(Bagging.fit)

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