Home » date » 2010 » May » 26 »

B382,regression tree,per maand,steven,coomans,thesis

*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 10:07:43 +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/t127486865199wlbhr5jbs6ars.htm/, Retrieved Wed, 26 May 2010 12:10:55 +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/t127486865199wlbhr5jbs6ars.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:
B382,regression tree,per maand,steven,coomans,thesis
 
Dataseries X:
» Textbox « » Textfile « » CSV «
283.25 NA 266.188606019126 282.966750204949 213 286.75 283.25 270.891611334875 283.840363485213 297 230.25 283.6 275.263005974429 281.694091305162 199 200.5 278.265 262.855086280826 260.877303843805 250 297.95 270.4885 245.666789558327 240.477467725152 302 329.5 273.23465 260.078754776835 260.301637689324 276 289.75 278.861185 279.214851491821 283.473863134466 251 223.775 279.9500665 282.118884977697 285.501477042126 265 281.78 274.33255985 266.036283432995 265.016411510642 271 265.8 275.077303865 270.376068493188 270.574646930639 278 256.75 274.1495734785 269.114666612848 268.990967395394 255 89.275 272.40961613065 265.706323023052 264.927551074807 205 225.5 254.096154517585 217.072697249434 206.724868986268 207 124.25 251.236539065827 219.395699090770 212.945092462218 221 230 238.537885159244 193.168608321036 183.557737684414 NA 286.525 237.684096643320 203.321250657694 198.945546067943 186 227 242.568186978988 226.256520535747 227.963047580939 191 218.3 241.011368281089 226.461462047352 22 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'Gwilym Jenkins' @ 72.249.127.135
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.
10.371011.0730.164
20.08310.6290.7750.154
30.03220.5460.8080.164
40.0140.4820.8060.162
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/May/26/t127486865199wlbhr5jbs6ars/19tse1274868461.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/26/t127486865199wlbhr5jbs6ars/19tse1274868461.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/May/26/t127486865199wlbhr5jbs6ars/29tse1274868461.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/26/t127486865199wlbhr5jbs6ars/29tse1274868461.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/May/26/t127486865199wlbhr5jbs6ars/3kkrz1274868461.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/26/t127486865199wlbhr5jbs6ars/3kkrz1274868461.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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