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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: Fri, 10 Dec 2010 09:51:34 +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/10/t1291974658kowlh1mulvdyskl.htm/, Retrieved Fri, 10 Dec 2010 10:51:01 +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/10/t1291974658kowlh1mulvdyskl.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 «

101.76 101.82 107.34 93.63 99.85 102.37 101.68 107.34 93.63 99.91 102.38 101.68 107.34 93.63 99.87 102.86 102.45 107.34 96.13 99.86 102.87 102.45 107.34 96.13 100.10 102.92 102.45 107.34 96.13 100.10 102.95 102.45 107.34 96.13 100.12 103.02 102.45 107.34 96.13 99.95 104.08 102.45 112.60 96.13 99.94 104.16 102.52 112.60 96.13 100.18 104.24 102.52 112.60 96.13 100.31 104.33 102.85 112.60 96.13 100.65 104.73 102.85 112.61 96.13 100.65 104.86 102.85 112.61 96.13 100.69 105.03 103.25 112.61 96.13 101.26 105.62 103.25 112.61 98.73 101.26 105.63 103.25 112.61 98.73 101.38 105.63 103.25 112.61 98.73 101.38 105.94 104.45 112.61 98.73 101.38 106.61 104.45 112.61 98.73 101.44 107.69 104.45 118.65 98.73 101.40 107.78 104.80 118.65 98.73 101.40 107.93 104.80 118.65 98.73 100.58 108.48 105.29 118.65 98.73 100.58 108.14 105.29 114.29 98.73 100.58 108.48 105.29 114.29 98.73 100.59 108.48 105.29 114.29 98.73 100.81 108.89 106.04 114.29 101.67 100.75 108.93 105.94 114.29 101.67 100.75 109.21 105.94 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	5 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Goodness of Fit
Correlation	0.9348
R-squared	0.8738
RMSE	0.4459

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	99.85	100.155714285714	-0.305714285714316
2	99.91	100.155714285714	-0.245714285714314
3	99.87	100.155714285714	-0.285714285714306
4	99.86	100.155714285714	-0.295714285714311
5	100.1	100.155714285714	-0.0557142857143162
6	100.1	100.155714285714	-0.0557142857143162
7	100.12	100.155714285714	-0.035714285714306
8	99.95	100.155714285714	-0.205714285714308
9	99.94	100.155714285714	-0.215714285714313
10	100.18	100.155714285714	0.0242857142856963
11	100.31	100.155714285714	0.154285714285692
12	100.65	100.155714285714	0.494285714285695
13	100.65	100.155714285714	0.494285714285695
14	100.69	100.155714285714	0.534285714285687
15	101.26	101.080555555556	0.179444444444457
16	101.26	101.080555555556	0.179444444444457
17	101.38	101.080555555556	0.299444444444447
18	101.38	101.080555555556	0.299444444444447
19	101.38	101.080555555556	0.299444444444447
20	101.44	101.080555555556	0.359444444444449
21	101.4	101.080555555556	0.319444444444457
22	101.4	101.080555555556	0.319444444444457
23	100.58	101.080555555556	-0.50055555555555
24	100.58	101.080555555556	-0.50055555555555
25	100.58	101.080555555556	-0.50055555555555
26	100.59	101.080555555556	-0.490555555555545
27	100.81	101.080555555556	-0.270555555555546
28	100.75	101.080555555556	-0.330555555555549
29	100.75	101.080555555556	-0.330555555555549
30	100.96	101.080555555556	-0.120555555555555
31	101.31	101.080555555556	0.229444444444454
32	101.64	101.080555555556	0.559444444444452
33	101.46	101.753333333333	-0.293333333333337
34	101.73	101.753333333333	-0.0233333333333263
35	101.73	101.753333333333	-0.0233333333333263
36	101.64	101.753333333333	-0.113333333333330
37	101.77	101.753333333333	0.0166666666666657
38	101.74	101.753333333333	-0.0133333333333354
39	101.89	101.753333333333	0.136666666666670
40	101.89	101.753333333333	0.136666666666670
41	101.93	101.753333333333	0.176666666666677
42	101.93	103.237058823529	-1.30705882352939
43	102.32	103.237058823529	-0.917058823529402
44	102.41	103.237058823529	-0.827058823529399
45	103.58	103.237058823529	0.342941176470603
46	104.12	103.237058823529	0.88294117647061
47	104.1	103.237058823529	0.8629411764706
48	104.15	103.237058823529	0.91294117647061
49	104.15	103.237058823529	0.91294117647061
50	104.16	103.237058823529	0.922941176470601
51	102.94	103.237058823529	-0.297058823529397
52	103.07	103.237058823529	-0.167058823529402
53	103.04	103.237058823529	-0.197058823529389
54	103.06	103.237058823529	-0.177058823529393
55	103.05	103.237058823529	-0.187058823529398
56	102.95	103.237058823529	-0.287058823529392
57	102.95	103.237058823529	-0.287058823529392
58	103.05	103.237058823529	-0.187058823529398

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291974658kowlh1mulvdyskl/2koio1291974688.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291974658kowlh1mulvdyskl/2koio1291974688.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291974658kowlh1mulvdyskl/3vfhr1291974688.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291974658kowlh1mulvdyskl/3vfhr1291974688.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291974658kowlh1mulvdyskl/4ophu1291974688.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291974658kowlh1mulvdyskl/4ophu1291974688.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 5 ; 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')

}

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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