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*The author of this computation has been verified*
R Software Module: /rwasp_regression_trees1.wasp (opens new window with default values)
Title produced by software: Recursive Partitioning (Regression Trees)
Date of computation: Tue, 21 Dec 2010 14:59:48 +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/Dec/21/t1292943507ago2a80urdfvs6w.htm/, Retrieved Tue, 21 Dec 2010 15:58:31 +0100
 
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/Dec/21/t1292943507ago2a80urdfvs6w.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:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
1 1081 213118 6282929 1 309 81767 4324047 1 458 153198 4108272 0 588 -26007 -1212617 1 299 126942 1485329 1 156 157214 1779876 0 481 129352 1367203 1 323 234817 2519076 1 452 60448 912684 1 109 47818 1443586 0 115 245546 1220017 0 110 48020 984885 1 239 -1710 1457425 0 247 32648 -572920 1 497 95350 929144 0 103 151352 1151176 0 109 288170 790090 1 502 114337 774497 1 248 37884 990576 0 373 122844 454195 1 119 82340 876607 1 84 79801 711969 0 102 165548 702380 0 295 116384 264449 0 105 134028 450033 0 64 63838 541063 1 267 74996 588864 0 129 31080 -37216 0 37 32168 783310 0 361 49857 467359 1 28 87161 688779 1 85 106113 608419 1 44 80570 696348 1 49 102129 597793 0 22 301670 821730 0 155 102313 377934 0 91 88577 651939 1 81 112477 697458 1 79 191778 700368 0 145 79804 225986 0 816 128294 348695 0 61 96448 373683 0 226 93811 501709 0 105 117520 413743 0 62 69159 379825 1 24 101792 336260 1 26 210568 636765 1 322 136996 481231 0 84 121920 469107 0 33 76403 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 time17 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework
error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.


Goodness of Fit
Correlation0.5446
R-squared0.2966
RMSE374807.3694


Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
162829291573533.733333334709395.26666667
243240471573533.733333332750513.26666667
341082721573533.733333332534738.26666667
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346325560324328.9253246751231.07467532466
347312275324328.925324675-12053.9253246753
348325560324328.9253246751231.07467532466
349325560324328.9253246751231.07467532466
350325560324328.9253246751231.07467532466
351320576324328.925324675-3752.92532467534
352325246324328.925324675917.074675324664
353332961324328.9253246758632.07467532466
354323010324328.925324675-1318.92532467534
355325560324328.9253246751231.07467532466
356325560324328.9253246751231.07467532466
357345253324328.92532467520924.0746753247
358325560324328.9253246751231.07467532466
359325560324328.9253246751231.07467532466
360325560324328.9253246751231.07467532466
361325559324328.9253246751230.07467532466
362325560324328.9253246751231.07467532466
363325560324328.9253246751231.07467532466
364319634324328.925324675-4694.92532467534
365319951324328.925324675-4377.92532467534
366325560324328.9253246751231.07467532466
367325560324328.9253246751231.07467532466
368325560324328.9253246751231.07467532466
369325560324328.9253246751231.07467532466
370325560324328.9253246751231.07467532466
371325560324328.9253246751231.07467532466
372318519324328.925324675-5809.92532467534
373343222324328.92532467518893.0746753247
374317234324328.925324675-7094.92532467534
375325560324328.9253246751231.07467532466
376325560324328.9253246751231.07467532466
377314025324328.925324675-10303.9253246753
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379325560324328.9253246751231.07467532466
380325560324328.9253246751231.07467532466
381325560324328.9253246751231.07467532466
382349365324328.92532467525036.0746753247
383289197295013.541666667-5816.54166666669
384325560324328.9253246751231.07467532466
385329245324328.9253246754916.07467532466
386240869324328.925324675-83459.9253246753
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388322876324328.925324675-1452.92532467534
389323117324328.925324675-1211.92532467534
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391335137324328.92532467510808.0746753247
392308271324328.925324675-16057.9253246753
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397243650295013.541666667-51363.5416666667
398532682620208.315789474-87526.3157894737
399319771324328.925324675-4557.92532467534
400171493620208.315789474-448715.315789474
401347262324328.92532467522933.0746753247
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403311874324328.925324675-12454.9253246753
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405316708324328.925324675-7620.92532467534
406333463324328.9253246759134.07467532466
407344282324328.92532467519953.0746753247
408319635324328.925324675-4693.92532467534
409301186324328.925324675-23142.9253246753
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411318765324328.925324675-5563.92532467534
412286146324328.925324675-38182.9253246753
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414307705324328.925324675-16623.9253246753
415312448324328.925324675-11880.9253246753
416299715324328.925324675-24613.9253246753
417373399295013.54166666778385.4583333333
418299446324328.925324675-24882.9253246753
419325586324328.9253246751257.07467532466
420291221324328.925324675-33107.9253246753
421261173324328.925324675-63155.9253246753
422255027324328.925324675-69301.9253246753
423-78375295013.541666667-373388.541666667
424-58143295013.541666667-353156.541666667
425227033295013.541666667-67980.5416666667
426235098620208.315789474-385110.315789474
42721267295013.541666667-273746.541666667
428238675808308.818181818-569633.818181818
429197687324328.925324675-126641.925324675
430418341340884.56521739177456.4347826087
431-297706340884.565217391-638590.565217391
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292943507ago2a80urdfvs6w/2h43n1292943568.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292943507ago2a80urdfvs6w/2h43n1292943568.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292943507ago2a80urdfvs6w/3h43n1292943568.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292943507ago2a80urdfvs6w/3h43n1292943568.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/21/t1292943507ago2a80urdfvs6w/4se2q1292943568.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/21/t1292943507ago2a80urdfvs6w/4se2q1292943568.ps (open in new window)


 
Parameters (Session):
par1 = 4 ; par2 = none ; par3 = 3 ; par4 = no ;
 
Parameters (R input):
par1 = 4 ; par2 = none ; par3 = 3 ; par4 = no ;
 
R code (references can be found in the software module):
library(party)
library(Hmisc)
par1 <- as.numeric(par1)
par3 <- as.numeric(par3)
x <- data.frame(t(y))
is.data.frame(x)
x <- x[!is.na(x[,par1]),]
k <- length(x[1,])
n <- length(x[,1])
colnames(x)[par1]
x[,par1]
if (par2 == 'kmeans') {
cl <- kmeans(x[,par1], par3)
print(cl)
clm <- matrix(cbind(cl$centers,1:par3),ncol=2)
clm <- clm[sort.list(clm[,1]),]
for (i in 1:par3) {
cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='')
}
cl$cluster <- as.factor(cl$cluster)
print(cl$cluster)
x[,par1] <- cl$cluster
}
if (par2 == 'quantiles') {
x[,par1] <- cut2(x[,par1],g=par3)
}
if (par2 == 'hclust') {
hc <- hclust(dist(x[,par1])^2, 'cen')
print(hc)
memb <- cutree(hc, k = par3)
dum <- c(mean(x[memb==1,par1]))
for (i in 2:par3) {
dum <- c(dum, mean(x[memb==i,par1]))
}
hcm <- matrix(cbind(dum,1:par3),ncol=2)
hcm <- hcm[sort.list(hcm[,1]),]
for (i in 1:par3) {
memb[memb==hcm[i,2]] <- paste('C',i,sep='')
}
memb <- as.factor(memb)
print(memb)
x[,par1] <- memb
}
if (par2=='equal') {
ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep=''))
x[,par1] <- as.factor(ed)
}
table(x[,par1])
colnames(x)
colnames(x)[par1]
x[,par1]
if (par2 == 'none') {
m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
}
load(file='createtable')
if (par2 != 'none') {
m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x)
if (par4=='yes') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Prediction (training)',par3+1,TRUE)
a<-table.element(a,'Prediction (testing)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Actual',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE)
a<-table.element(a,'CV',1,TRUE)
a<-table.row.end(a)
for (i in 1:10) {
ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1))
m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,])
if (i==1) {
m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,])
m.ct.i.actu <- x[ind==1,par1]
m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,])
m.ct.x.actu <- x[ind==2,par1]
} else {
m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,]))
m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1])
m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,]))
m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1])
}
}
print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,]))
numer <- numer + m.ct.i.tab[i,i]
}
print(m.ct.i.cp <- numer / sum(m.ct.i.tab))
print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred))
numer <- 0
for (i in 1:par3) {
print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,]))
numer <- numer + m.ct.x.tab[i,i]
}
print(m.ct.x.cp <- numer / sum(m.ct.x.tab))
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj])
a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4))
for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj])
a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4))
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'Overall',1,TRUE)
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.i.cp,4))
for (jjj in 1:par3) a<-table.element(a,'-')
a<-table.element(a,round(m.ct.x.cp,4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
}
}
m
bitmap(file='test1.png')
plot(m)
dev.off()
bitmap(file='test1a.png')
plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response')
dev.off()
if (par2 == 'none') {
forec <- predict(m)
result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec))
colnames(result) <- c('Actuals','Forecasts','Residuals')
print(result)
}
if (par2 != 'none') {
print(cbind(as.factor(x[,par1]),predict(m)))
myt <- table(as.factor(x[,par1]),predict(m))
print(myt)
}
bitmap(file='test2.png')
if(par2=='none') {
op <- par(mfrow=c(2,2))
plot(density(result$Actuals),main='Kernel Density Plot of Actuals')
plot(density(result$Residuals),main='Kernel Density Plot of Residuals')
plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals')
plot(density(result$Forecasts),main='Kernel Density Plot of Predictions')
par(op)
}
if(par2!='none') {
plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted')
}
dev.off()
if (par2 == 'none') {
detcoef <- cor(result$Forecasts,result$Actuals)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goodness of Fit',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.element(a,round(detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'R-squared',1,TRUE)
a<-table.element(a,round(detcoef*detcoef,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'RMSE',1,TRUE)
a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Actuals',header=TRUE)
a<-table.element(a,'Forecasts',header=TRUE)
a<-table.element(a,'Residuals',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(result$Actuals)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,result$Actuals[i])
a<-table.element(a,result$Forecasts[i])
a<-table.element(a,result$Residuals[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
}
if (par2 != 'none') {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
for (i in 1:par3) {
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
}
a<-table.row.end(a)
for (i in 1:par3) {
a<-table.row.start(a)
a<-table.element(a,paste('C',i,sep=''),1,TRUE)
for (j in 1:par3) {
a<-table.element(a,myt[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}
 




Copyright

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Software written by Ed van Stee & Patrick Wessa


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