RSDA (version 2.0.8)

R2.L: Lower boundary correlation coefficient.

Description

Compute the lower boundary correlation coefficient for two interval variables.

Usage

R2.L(sym.var, prediction)

Arguments

sym.var

Variable that was predicted.

prediction

The prediction given by the model.

Value

The lower boundary correlation coefficient.

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

# NOT RUN {
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)
# }