CM and CRM Linear regression model

Description

To execute the Center Method (CR) and Center and Range Method (CRM) to Linear regression

Usage

sym.lm(formula, sym.data, method = c("cm", "crm"))

Arguments

Details

Models for lm are specified symbolically. A typical model has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second with duplicates removed. A specification of the form first:second indicates the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second.

Value

sym.lm returns an object of class "lm" or for multiple responses of class c("mlm", "lm")

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')
RMSE.L(sym.var(int_prost_test,9),pred.cm$Fitted)
RMSE.U(sym.var(int_prost_test,9),pred.cm$Fitted)
R2.L(sym.var(int_prost_test,9),pred.cm$Fitted)
R2.U(sym.var(int_prost_test,9),pred.cm$Fitted)
deter.coefficient(sym.var(int_prost_test,9),pred.cm$Fitted)

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.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
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.lm.Rd_%03d_medium.png", width=480, height=480)
> ### Name: sym.lm
> ### Title: CM and CRM Linear regression model
> ### Aliases: sym.lm
> ### Keywords: Symbolic lm
> 
> ### ** 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')
> RMSE.L(sym.var(int_prost_test,9),pred.cm$Fitted)
[1] 0.7229999
> RMSE.U(sym.var(int_prost_test,9),pred.cm$Fitted)
[1] 0.7192467
> R2.L(sym.var(int_prost_test,9),pred.cm$Fitted)
[1] 0.501419
> R2.U(sym.var(int_prost_test,9),pred.cm$Fitted)
[1] 0.5058389
> deter.coefficient(sym.var(int_prost_test,9),pred.cm$Fitted)
[1] 0.4962964
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>