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b28A,steven,coomans,thesis,regressiontree,per3maand

*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 11:55:24 +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/t1274874988kzmviesa97e5ciq.htm/, Retrieved Wed, 26 May 2010 13:56:28 +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/t1274874988kzmviesa97e5ciq.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:

b28A,steven,coomans,thesis,regressiontree,per3maand

Dataseries X:

» Textbox « » Textfile « » CSV «

275.0916667 NA 275.182487301474 274.816575230748 341.6167 401.3423333 275.0916667 400.972242874856 295.968930016752 379.0167 260.6333333 287.71673336 260.845832809383 338.328662343278 346.35 286.1 285.008393354 286.165919817652 308.265916154109 302.25 368.0833333 285.1175540186 367.853139451386 301.071991695963 239.7667 385.1916667 293.41413194674 385.000486465639 324.590521416548 386.9333 181.5333333 302.591885422066 182.007237238174 345.060642650557 229.25 145.1 290.486030209860 145.595640852172 289.120656303052 182.2667 203.1833333 275.947427188874 203.328434462353 240.050659948121 272.3333 227.5833333 268.671017799986 227.574414189821 227.504420390666 234.4 239.0833333 264.562249349988 239.026738533428 227.532064816018 315.8 109.175 262.014357744989 109.701909322141 231.457107359672 118.75 231.0833333 246.73042197049 230.854906936773 189.871114035390 200.6667 216.9166667 245.165713103441 216.849097935047 203.886458556338 147.3333 229.8583333 242.340808463097 229.753265490343 208.317704231102 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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Charts produced by software:

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')

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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