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B580,regression tree,steven,coomans,thesis,permaand

*Unverified author*
R Software Module: /rwasp_regression_trees.wasp (opens new window with default values)
Title produced by software: Recursive Partitioning (Regression Trees)
Date of computation: Wed, 26 May 2010 10:40:57 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/May/26/t1274870608ot5lvz0hwr3h92s.htm/, Retrieved Wed, 26 May 2010 12:43:28 +0200
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/May/26/t1274870608ot5lvz0hwr3h92s.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
B580,regression tree,steven,coomans,thesis,permaand
 
Dataseries X:
» Textbox « » Textfile « » CSV «
209 NA 207.578503057629 208.791000220112 223 175 209 215.531763346576 198.426779339075 201 247.5 205.6 285.751097248607 203.378258089298 241 177 209.79 118.186987745813 206.916502799148 199 188.775 206.511 214.853226812988 198.493885113137 198 194.825 204.7374 138.401727102421 196.263512901256 254 182.275 203.74616 216.486643563448 193.640749579656 232 145.25 201.599044 97.6257034058337 183.618255052768 176 286.3 195.9641396 245.459409265308 194.973766357325 284 257.75 204.99772564 280.672235749570 219.947196737977 229 335 210.272953076 262.276026647169 245.338121076719 229 234.15 222.7456577684 233.243533537997 257.514432415318 292 276.275 223.88609199156 250.184476503336 258.505048267576 252 327.052 229.124982792404 267.134675437714 255.53357353954 192 375.325 238.917684513164 374.008804029831 315.322448001021 NA 199.75 252.558416061847 220.89450878188 293.272867505843 246 215.875 247.277574455663 288.373712865628 268.44574756024 276 225 244.137317010096 194.992809298708 254.391240106182 27 etc...
 
Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework
error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.


Model Performance
#Complexitysplitrelative errorCV errorCV S.D.
10.532011.0320.168
20.11310.4680.5720.108
30.0220.3550.5290.081
40.0130.3340.530.082
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/May/26/t1274870608ot5lvz0hwr3h92s/1ezsu1274870453.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/26/t1274870608ot5lvz0hwr3h92s/1ezsu1274870453.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/May/26/t1274870608ot5lvz0hwr3h92s/2ezsu1274870453.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/26/t1274870608ot5lvz0hwr3h92s/2ezsu1274870453.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/May/26/t1274870608ot5lvz0hwr3h92s/3ezsu1274870453.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/26/t1274870608ot5lvz0hwr3h92s/3ezsu1274870453.ps (open in new window)


 
Parameters (Session):
par1 = 1 ; par2 = No ;
 
Parameters (R input):
par1 = 1 ; par2 = No ;
 
R code (references can be found in the software module):
library(rpart)
library(partykit)
par1 <- as.numeric(par1)
autoprune <- function ( tree, method='Minimum CV'){
xerr <- tree$cptable[,'xerror']
cpmin.id <- which.min(xerr)
if (method == 'Minimum CV Error plus 1 SD'){
xstd <- tree$cptable[,'xstd']
errt <- xerr[cpmin.id] + xstd[cpmin.id]
cpSE1.min <- which.min( errt < xerr )
mycp <- (tree$cptable[,'CP'])[cpSE1.min]
}
if (method == 'Minimum CV') {
mycp <- (tree$cptable[,'CP'])[cpmin.id]
}
return (mycp)
}
conf.multi.mat <- function(true, new)
{
if ( all( is.na(match( levels(true),levels(new) ) )) )
stop ( 'conflict of vector levels')
multi.t <- list()
for (mylev in levels(true) ) {
true.tmp <- true
new.tmp <- new
left.lev <- levels (true.tmp)[- match(mylev,levels(true) ) ]
levels(true.tmp) <- list ( mylev = mylev, all = left.lev )
levels(new.tmp) <- list ( mylev = mylev, all = left.lev )
curr.t <- conf.mat ( true.tmp , new.tmp )
multi.t[[mylev]] <- curr.t
multi.t[[mylev]]$precision <-
round( curr.t$conf[1,1] / sum( curr.t$conf[1,] ), 2 )
}
return (multi.t)
}
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
m <- rpart(as.data.frame(x1))
par2
if (par2 != 'No') {
mincp <- autoprune(m,method=par2)
print(mincp)
m <- prune(m,cp=mincp)
}
m$cptable
bitmap(file='test1.png')
plot(as.party(m),tp_args=list(id=FALSE))
dev.off()
bitmap(file='test2.png')
plotcp(m)
dev.off()
cbind(y=m$y,pred=predict(m),res=residuals(m))
myr <- residuals(m)
myp <- predict(m)
bitmap(file='test4.png')
op <- par(mfrow=c(2,2))
plot(myr,ylab='residuals')
plot(density(myr),main='Residual Kernel Density')
plot(myp,myr,xlab='predicted',ylab='residuals',main='Predicted vs Residuals')
plot(density(myp),main='Prediction Kernel Density')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Model Performance',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Complexity',header=TRUE)
a<-table.element(a,'split',header=TRUE)
a<-table.element(a,'relative error',header=TRUE)
a<-table.element(a,'CV error',header=TRUE)
a<-table.element(a,'CV S.D.',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$cptable[,1])) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,round(m$cptable[i,'CP'],3))
a<-table.element(a,m$cptable[i,'nsplit'])
a<-table.element(a,round(m$cptable[i,'rel error'],3))
a<-table.element(a,round(m$cptable[i,'xerror'],3))
a<-table.element(a,round(m$cptable[i,'xstd'],3))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
 





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