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

Description

To execute Predict method to Lasso, Ridge and and Elastic Net Linear regression model to interval variables.

Usage

predictsym.glm(model, new.sym.data, response = 1, method = c("cm", "crm"))

Arguments

Value

The object returned depends the ... argument which is passed on to the predict method for glmnet objects.

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.

See Also

sym.glm

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 '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/predictsym.glm.Rd_%03d_medium.png", width=480, height=480)
> ### Name: predictsym.glm
> ### Title: Predict method to Lasso, Ridge and and Elastic Net Linear
> ###   regression model to interval variables
> ### Aliases: predictsym.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 
>