genpathmox (version 0.5)

print.xtree.reg: Print function for the Pathmox Segmentation Trees: linear regression and LAD

Description

The function print.xtree.reg print the reg.pathmox tree

Usage

# S3 method for xtree.reg
print(x, ...)

Arguments

x

An object of class "xtree.reg".

…

Further arguments are ignored.

References

Aluja, T. Lamberti, G. Sanchez, G. (2013). Modeling with heterogeneity. Meetings of Italian Statistical Society, Advances in Latent Variables - Methods, Models and Applications. Brescia.

Lamberti, G. (2014) Modeling with Heterogeneity. PhD Dissertation.

Sanchez, G. (2009) PATHMOX Approach: Segmentation Trees in Partial Least Squares Path Modeling. PhD Dissertation.

summary.xtree.pls.

Examples

# NOT RUN {
 
# }
# NOT RUN {
##example of LM in alumni satisfaction
 
data(fibtelereg)

#identify the segmentation variables  
segvar = fibtelereg[,2:11]

#select the variables
data.fib = fibtelereg[,12:18]          

#re-ordering those segmentation variables with ordinal scale
segvar$Age     = factor(segvar$Age, ordered=T)
segvar$Salary   = factor(segvar$Salary, 
	levels=c("<18k","25k","35k","45k",">45k"), ordered=T)
segvar$Accgrade = factor(segvar$Accgrade, 
	levels=c("accnote<7","7-8accnote","accnote>8"), ordered=T)
segvar$Grade 	= factor(segvar$Grade, 
	levels=c("<6.5note","6.5-7note","7-7.5note",">7.5note"), ordered=T)

fib.reg.pathmox=reg.pathmox(Satisfact~.,data=data.fib,segvar,
	signif=0.05,deep=2,method="lm",size=0.15)

 print(fib.reg.pathmox)

# }
# NOT RUN {
data(fibtelereg)

#Identify the segmentation variables  
segvar= fibtelereg[1:50,3:4]

#Select the variables
data.fib=fibtelereg[1:50,12:18]          

fib.reg.pathmox=reg.pathmox(Satisfact~.,data=data.fib,segvar,
	signif=0.05,deep=1,method="lm",size=0.15)

print(fib.reg.pathmox)

# }