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berekening 8

*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, 28 Dec 2010 13:49:51 +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/28/t12935441289srk8f63xrgurj0.htm/, Retrieved Tue, 28 Dec 2010 14:48:48 +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/28/t12935441289srk8f63xrgurj0.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 1595 17 60720 319369 0 5565 47 94355 493408 0 601 6 60720 319210 1 188 11 77655 381180 1 7146 105 134028 297978 0 1135 17 62285 290476 1 450 8 59325 292136 1 34 8 60630 314353 1 133 14 65990 339445 1 119 6 59118 303677 0 2053 53 100423 397144 0 4036 63 100269 424898 1 0 0 60720 315380 1 4655 393 60720 341570 1 131 11 61808 308989 1 1766 73 60438 305959 1 312 14 58598 318690 1 448 40 59781 323361 1 115 13 60945 318903 1 60 10 61124 314049 1 0 0 60720 315380 1 364 35 47705 298466 1 0 0 60720 315380 0 1442 118 121173 438493 0 1389 9 57530 378049 1 149 5 62041 313332 1 2212 14 60720 319864 0 7489 322 136996 430866 1 0 0 60720 315380 1 402 26 84990 332743 0 7419 29 63255 286963 1 0 0 60720 315380 1 307 15 58856 331955 0 1134 162 146216 527021 0 7561 87 82425 364304 1 5131 71 86111 292154 1 9 6 60835 314987 1 2264 24 55174 272713 1 40949 481 129352 1398893 0 4980 91 113521 409642 1 272 8 112995 429112 1 757 19 45689 382712 1 1424 94 131116 374943 1 0 0 60720 315380 0 37 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 time11 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Goodness of Fit
Correlation0.6588
R-squared0.434
RMSE323514.9255


Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
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364325479436863.25862069-111384.258620690
365325506311459.0087719314046.9912280702
366984885678836.631578947306048.368421053
367313267311459.008771931807.99122807017
368315793311459.008771934333.99122807017
369315380311459.008771933920.99122807017
370215362311459.00877193-96097.0087719298
371314073436863.25862069-122790.258620690
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380315380311459.008771933920.99122807017
381387475311459.0087719376015.9912280702
382291787311459.00877193-19672.0087719298
383247060311459.00877193-64399.0087719298
384329784311459.0087719318324.9912280702
385315834311459.008771934374.99122807017
386304555311459.00877193-6904.00877192983
387376641311459.0087719365181.9912280702
388315380311459.008771933920.99122807017
389315380311459.008771933920.99122807017
390315380311459.008771933920.99122807017
391315380311459.008771933920.99122807017
392315380311459.008771933920.99122807017
393357760678836.631578947-321076.631578947
394315637311459.008771934177.99122807017
395315380311459.008771933920.99122807017
396315380311459.008771933920.99122807017
397315380311459.008771933920.99122807017
398315380311459.008771933920.99122807017
399327071311459.0087719315611.9912280702
400377516436863.25862069-59347.2586206897
401299243436863.25862069-137620.258620690
402387699436863.25862069-49164.2586206897
403309836311459.00877193-1623.00877192983
404444477678836.631578947-234359.631578947
405322327311459.0087719310867.9912280702
406314913311459.008771933453.99122807017
407315553311459.008771934093.99122807017
408688779436863.25862069251915.741379310
409321896436863.25862069-114967.258620690
410301607436863.25862069-135256.258620690
411304485311459.00877193-6974.00877192983
412315380311459.008771933920.99122807017
413315380311459.008771933920.99122807017
414231861311459.00877193-79598.0087719298
415347385311459.0087719335925.9912280702
416316386311459.008771934926.99122807017
417491303436863.2586206954439.7413793103
418261216311459.00877193-50243.0087719298
419315388311459.008771933928.99122807017
420315380311459.008771933920.99122807017
421313729311459.008771932269.99122807017
422358649311459.0087719347189.9912280702
42319265171991179.08333333-64662.0833333333
424296656311459.00877193-14803.0087719298
425275311311459.00877193-36148.0087719298
426-42143311459.00877193-353602.00877193
427315380311459.008771933920.99122807017
428343929311459.0087719332469.9912280702
429367655311459.0087719356195.9912280702
430315380311459.008771933920.99122807017
431313491311459.008771932031.99122807017
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935441289srk8f63xrgurj0/2zymj1293544177.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935441289srk8f63xrgurj0/2zymj1293544177.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t12935441289srk8f63xrgurj0/3zymj1293544177.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935441289srk8f63xrgurj0/3zymj1293544177.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t12935441289srk8f63xrgurj0/4spl41293544177.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935441289srk8f63xrgurj0/4spl41293544177.ps (open in new window)


 
Parameters (Session):
par1 = 5 ; par2 = none ; par4 = no ; par5 = 1 ; par6 = 1 ; par7 = 1 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;
 
Parameters (R input):
par1 = 5 ; par2 = none ; par4 = no ; par5 = 1 ; par6 = 1 ; par7 = 1 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;
 
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')
}
 




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Software written by Ed van Stee & Patrick Wessa


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