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Verbetering WS10

*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: Mon, 20 Dec 2010 08:33:33 +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/20/t1292833888iocj3h30rxvp4fj.htm/, Retrieved Mon, 20 Dec 2010 09:31:29 +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/20/t1292833888iocj3h30rxvp4fj.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 «
0.504208603 0.397232704 0.457969746 0.382767296 0.509923035 0.396037736 0.606622221 0.441761006 0.626210885 0.445220126 0.626631316 0.438490566 0.676731276 0.467484277 0.613117455 0.465786164 0.486215861 0.402075472 0.452529881 0.376163522 0.467150592 0.37591195 0.494624486 0.392955975 0.444567428 0.34490566 0.478862605 0.368553459 0.544458459 0.390880503 0.628201498 0.424842767 0.672578445 0.426855346 0.652706633 0.442327044 0.645430599 0.474842767 0.576334011 0.447610063 0.618334234 0.480754717 0.639896351 0.516037736 0.72850438 0.580628931 0.694655375 0.573522013 0.689773225 0.578867925 0.712244845 0.593584906 0.760337031 0.645974843 0.837816503 0.690503145 0.90688735 0.782201258 0.976018259 0.839056604 0.962066806 0.847484277 0.837593417 0.726855346 0.767638807 0.635534591 0.580006349 0.470943396 0.387740568 0.346163522 0.331274078 0.272327044 0.345251272 0.286792453 0.380172806 0.27672956 0.399838692 0.297421384 0.425742404 0.321698113 0.524183377 0.365597484 0.59 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 time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk


Goodness of Fit
Correlation0.8984
R-squared0.8071
RMSE0.0541


Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
10.3972327040.3559503843888890.0412823196111111
20.3827672960.3559503843888890.0268169116111111
30.3960377360.3559503843888890.0400873516111111
40.4417610060.4726436059-0.0308825999
50.4452201260.4726436059-0.0274234799
60.4384905660.4726436059-0.0341530399
70.4674842770.4726436059-0.00515932889999998
80.4657861640.4726436059-0.00685744189999998
90.4020754720.3559503843888890.0461250876111111
100.3761635220.3559503843888890.0202131376111111
110.375911950.3559503843888890.0199615656111111
120.3929559750.3559503843888890.0370055906111111
130.344905660.355950384388889-0.0110447243888889
140.3685534590.3559503843888890.0126030746111112
150.3908805030.3559503843888890.0349301186111111
160.4248427670.4726436059-0.0478008389
170.4268553460.4726436059-0.0457882599
180.4423270440.4726436059-0.0303165619
190.4748427670.47264360590.00219916110000001
200.4476100630.4726436059-0.0250335429
210.4807547170.47264360590.00811111110000001
220.5160377360.47264360590.0433941301
230.5806289310.681292167181818-0.100663236181818
240.5735220130.681292167181818-0.107770154181818
250.5788679250.681292167181818-0.102424242181818
260.5935849060.681292167181818-0.0877072611818182
270.6459748430.681292167181818-0.0353173241818182
280.6905031450.6812921671818180.00921097781818181
290.7822012580.6812921671818180.100909090818182
300.8390566040.6812921671818180.157764436818182
310.8474842770.6812921671818180.166192109818182
320.7268553460.6812921671818180.0455631788181818
330.6355345910.681292167181818-0.0457575761818182
340.4709433960.4726436059-0.00170020989999997
350.3461635220.355950384388889-0.00978686238888887
360.2723270440.355950384388889-0.0836233403888889
370.2867924530.355950384388889-0.0691579313888889
380.276729560.355950384388889-0.0792208243888889
390.2974213840.355950384388889-0.0585290003888889
400.3216981130.355950384388889-0.0342522713888889
410.3655974840.3559503843888890.0096470996111111
420.4352201260.4726436059-0.0374234799
430.4128930820.3559503843888890.0569426976111111
440.4586792450.4726436059-0.0139643609
450.4284276730.4726436059-0.0442159329
460.4635220130.4726436059-0.00912159289999998
470.4871698110.47264360590.0145262051
480.4735849060.47264360590.000941300100000042
490.4918867920.47264360590.0192431861
500.4748427670.47264360590.00219916110000001
510.5023270440.47264360590.0296834381
520.5393710690.47264360590.0667274631
530.4844025160.47264360590.0117589101
540.4746540880.47264360590.00201048209999999
550.4735220130.47264360590.000878407100000034
560.487547170.47264360590.0149035641
570.4933333330.47264360590.0206897271
580.5251572330.47264360590.0525136271
590.5427044030.47264360590.0700607971
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/20/t1292833888iocj3h30rxvp4fj/283zs1292834007.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/20/t1292833888iocj3h30rxvp4fj/283zs1292834007.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/20/t1292833888iocj3h30rxvp4fj/383zs1292834007.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/20/t1292833888iocj3h30rxvp4fj/383zs1292834007.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/20/t1292833888iocj3h30rxvp4fj/41ugv1292834007.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/20/t1292833888iocj3h30rxvp4fj/41ugv1292834007.ps (open in new window)


 
Parameters (Session):
par1 = 2 ; par2 = none ; par4 = yes ;
 
Parameters (R input):
par1 = 2 ; par2 = none ; par4 = yes ;
 
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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