Lower boundary correlation coefficient.

Description

Compute the lower boundary correlation coefficient for two interval variables.

Usage

R2.L(sym.var, prediction)

Arguments

Value

The lower boundary correlation coefficient.

Author(s)

Oldemar Rodriguez Rojas

References

LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2008). Centre and range method to fitting a linear regression model on symbolic interval data. Computational Statistics and Data Analysis 52, 1500-1515.

LIMA-NETO, E.A., DE CARVALHO, F.A.T., (2010). Constrained linear regression models for symbolic interval-valued variables. Computational Statistics and Data Analysis 54, 333-347.

See Also

sym.glm

Examples

data(int_prost_train)
data(int_prost_test)
res.cm<-sym.lm(lpsa~.,sym.data=int_prost_train,method='cm')
pred.cm<-predictsym.lm(res.cm,int_prost_test,method='cm')
R2.L(sym.var(int_prost_test,9),pred.cm$Fitted)

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')
R2.L(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)

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> 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/R2.L.Rd_%03d_medium.png", width=480, height=480)
> ### Name: R2.L
> ### Title: Lower boundary correlation coefficient.
> ### Aliases: R2.L
> ### Keywords: lower correlation
> 
> ### ** Examples
> 
> data(int_prost_train)
> data(int_prost_test)
> res.cm<-sym.lm(lpsa~.,sym.data=int_prost_train,method='cm')
> pred.cm<-predictsym.lm(res.cm,int_prost_test,method='cm')
> R2.L(sym.var(int_prost_test,9),pred.cm$Fitted)
[1] 0.501419
> 
> 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')
> R2.L(sym.var(int_prost_test,9),pred.cm.lasso)
[1] 0.5770562
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>