Us crime interval data table

Description

Us crime classic data table genetated from uscrime data.

Usage

data(uscrime_int)

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(uscrime_int)
car.data<-uscrime_int
res.cm.lasso<-sym.glm(sym.data=car.data,response=102,method='cm',alpha=1,
                                      nfolds=10,grouped=TRUE)
plot(res.cm.lasso)
plot(res.cm.lasso$glmnet.fit, "norm", label=TRUE)
plot(res.cm.lasso$glmnet.fit, "lambda", label=TRUE)

pred.cm.lasso<-predictsym.glm(res.cm.lasso,response=102,car.data,method='cm')
RMSE.L(sym.var(car.data,102),pred.cm.lasso)
RMSE.U(sym.var(car.data,102),pred.cm.lasso)
R2.L(sym.var(car.data,102),pred.cm.lasso)
R2.U(sym.var(car.data,102),pred.cm.lasso)
deter.coefficient(sym.var(car.data,102),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/uscrime_int.Rd_%03d_medium.png", width=480, height=480)
> ### Name: uscrime_int
> ### Title: Us crime interval data table
> ### Aliases: uscrime_int
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(uscrime_int)
> car.data<-uscrime_int
> res.cm.lasso<-sym.glm(sym.data=car.data,response=102,method='cm',alpha=1,
+                                       nfolds=10,grouped=TRUE)
> plot(res.cm.lasso)
> plot(res.cm.lasso$glmnet.fit, "norm", label=TRUE)
> plot(res.cm.lasso$glmnet.fit, "lambda", label=TRUE)
> 
> pred.cm.lasso<-predictsym.glm(res.cm.lasso,response=102,car.data,method='cm')
> RMSE.L(sym.var(car.data,102),pred.cm.lasso)
[1] 0.305352
> RMSE.U(sym.var(car.data,102),pred.cm.lasso)
[1] 0.3204653
> R2.L(sym.var(car.data,102),pred.cm.lasso)
[1] 0.3130354
> R2.U(sym.var(car.data,102),pred.cm.lasso)
[1] 0.771124
> deter.coefficient(sym.var(car.data,102),pred.cm.lasso)
[1] 0.8027777
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>