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B521,regression tree,steven,coomans,thesis,per2maand

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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 11:26:45 +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/t1274873276vj2ucpnjh4tu3fn.htm/, Retrieved Wed, 26 May 2010 13:27:56 +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/t1274873276vj2ucpnjh4tu3fn.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:

B521,regression tree,steven,coomans,thesis,per2maand

Dataseries X:

» Textbox « » Textfile « » CSV «

341.25 NA 333.775928269088 340.90875028286 381,75 303.6875 341.25 338.492504687478 332.86089603337 417,75 357.5 337.49375 316.528510995532 325.788548906474 359,25 295.075 339.494375 342.383916967292 327.541311770725 336 386.5755 335.0524375 312.529223276755 332.035063343418 361,8 455.6625 340.20474375 359.256754833345 355.979142780996 420,275 424.926 351.750519375 420.094418776242 422.853279739822 400,9 506.751 359.0680674375 423.143429126151 447.538297256219 248,15 433.9 373.83636069375 475.904696528962 463.452634199947 480,125 466.3375 379.842724624375 449.397276177252 469.038817658043 441,4 496.7 388.492202161938 460.087548237949 454.838864911353 483 464.45 399.312981945744 483.19214675098 483.073307566238 477,75 385.375 405.826683751169 471.364755928644 464.126997390433 486,25 381.875 403.781515376053 417.100190422978 429.475306145968 433,875 219.6375 401.590863838447 394.871034955396 366.780195970248 286,4 268.975 383.395527454603 284.288425042335 314.305881579131 231,875 292.2875 371.953 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	3 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
#	Complexity	split	relative error	CV error	CV S.D.
1	0.603	0	1	1.13	0.2
2	0.01	1	0.397	1.01	0.285

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/26/t1274873276vj2ucpnjh4tu3fn/1sy5l1274873201.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/26/t1274873276vj2ucpnjh4tu3fn/1sy5l1274873201.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/26/t1274873276vj2ucpnjh4tu3fn/2sy5l1274873201.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/26/t1274873276vj2ucpnjh4tu3fn/2sy5l1274873201.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/May/26/t1274873276vj2ucpnjh4tu3fn/3sy5l1274873201.png (open in new window)

http://www.freestatistics.org/blog/date/2010/May/26/t1274873276vj2ucpnjh4tu3fn/3sy5l1274873201.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')

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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