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Last data update: 2014.03.03

R: Lasso, Ridge and and Elastic Net Linear regression model to...

sym.glm

R Documentation

Lasso, Ridge and and Elastic Net Linear regression model to interval variables

Description

Execute Lasso, Ridge and and Elastic Net Linear regression model to interval variables.

Usage

sym.glm(sym.data, response = 1, method = c("cm", "crm"), 
               alpha = 1, nfolds = 10, grouped = TRUE)

Arguments

`sym.data`	Should be a symbolic data table read with the function read.sym.table(...).
`response`	The number of the column where is the response variable in the interval data table.
`method`	"cm" to generalized Center Method and "crm" to generalized Center and Range Method.
`alpha`	alpha=1 is the lasso penalty, and alpha=0 the ridge penalty. 0<alpha<1 is the elastic net method.
`nfolds`	Number of folds - default is 10. Although nfolds can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable is nfolds=3
`grouped`	This is an experimental argument, with default TRUE, and can be ignored by most users.

Value

An object of class "cv.glmnet" is returned, which is a list with the ingredients of the cross-validation fit.

Author(s)

Oldemar Rodriguez Rojas

References

Rodriguez O. (2013). A generalization of Centre and Range method for fitting a linear regression model to symbolic interval data using Ridge Regression, Lasso and Elastic Net methods. The IFCS2013 conference of the International Federation of Classification Societies, Tilburg University Holland.

Examples

data(int_prost_train)
data(int_prost_test)
res.cm.lasso<-sym.glm(sym.data=int_prost_train,response=9,method='cm',
                      alpha=1,nfolds=10,grouped=TRUE)
pred.cm.lasso<-predictsym.glm(res.cm.lasso,response=9,int_prost_test,method='cm')
plot(res.cm.lasso)
plot(res.cm.lasso$glmnet.fit, "norm", label=TRUE)
plot(res.cm.lasso$glmnet.fit, "lambda", label=TRUE)
RMSE.L(sym.var(int_prost_test,9),pred.cm.lasso)
RMSE.U(sym.var(int_prost_test,9),pred.cm.lasso)
R2.L(sym.var(int_prost_test,9),pred.cm.lasso)
R2.U(sym.var(int_prost_test,9),pred.cm.lasso)
deter.coefficient(sym.var(int_prost_test,9),pred.cm.lasso)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(RSDA)
Loading required package: XML
Loading required package: scales
Loading required package: ggplot2
Loading required package: princurve
Loading required package: sqldf
Loading required package: gsubfn
Loading required package: proto
Could not load tcltk.  Will use slower R code instead.
Loading required package: RSQLite
Loading required package: DBI
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/RSDA/sym.glm.Rd_%03d_medium.png", width=480, height=480)
> ### Name: sym.glm
> ### Title: Lasso, Ridge and and Elastic Net Linear regression model to
> ###   interval variables
> ### Aliases: sym.glm
> ### Keywords: Symbolic Regression Lasso Ridge
> 
> ### ** Examples
> 
> data(int_prost_train)
> data(int_prost_test)
> res.cm.lasso<-sym.glm(sym.data=int_prost_train,response=9,method='cm',
+                       alpha=1,nfolds=10,grouped=TRUE)
> pred.cm.lasso<-predictsym.glm(res.cm.lasso,response=9,int_prost_test,method='cm')
> plot(res.cm.lasso)
> plot(res.cm.lasso$glmnet.fit, "norm", label=TRUE)
> plot(res.cm.lasso$glmnet.fit, "lambda", label=TRUE)
> RMSE.L(sym.var(int_prost_test,9),pred.cm.lasso)
[1] 0.7077209
> RMSE.U(sym.var(int_prost_test,9),pred.cm.lasso)
[1] 0.7043203
> R2.L(sym.var(int_prost_test,9),pred.cm.lasso)
[1] 0.5221561
> R2.U(sym.var(int_prost_test,9),pred.cm.lasso)
[1] 0.5261883
> deter.coefficient(sym.var(int_prost_test,9),pred.cm.lasso)
[1] 0.4937406
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>