Plot.importance: Generate a plot of variable importance.

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

Description

This function generates a plot for evaluating variable importance based on a bagging object fitted by the bagging.lasso model.

Usage

Plot.importance(x, max.var.show = 40, xlab = "Importance Score", ylab = NULL, 
                main = "Variable Importance Plot")

Arguments

x	a fitted bagging object.
max.var.show	the maximum number of variables to be shown in the plot. Defaults to 40.
xlab	a title for the x axis.
ylab	a title for the y axis.
main	an overall title for the plot.

Details

A plot of variable importance based on the Bagging.lasso model is produced, and nothing is returned.

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=TRUE, trimmed=FALSE, weighted=TRUE, seed=0123)
Plot.importance(Bagging.fit)

Example output

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

Iter  1 
Iter  2 

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