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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, 28 Dec 2010 19:15:15 +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/t1293563574hwgu9ve6wqp05t1.htm/, Retrieved Tue, 28 Dec 2010 20:12:57 +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/t1293563574hwgu9ve6wqp05t1.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 9 12 9 24 13 14 1 9 15 6 25 12 8 1 9 14 13 19 15 12 1 8 10 7 18 12 7 1 14 10 8 18 10 10 0 14 9 8 23 12 7 1 15 18 11 23 15 16 1 11 11 11 23 9 11 0 14 14 8 17 7 12 0 8 24 20 30 11 7 1 16 18 16 26 10 11 0 11 14 8 23 14 15 1 7 18 11 35 11 7 0 9 12 8 21 15 14 0 16 5 4 23 12 7 1 10 12 8 20 14 15 0 14 11 8 24 15 17 0 11 9 6 20 9 15 1 6 11 8 17 13 14 1 12 16 14 27 16 8 1 14 14 10 18 13 8 0 13 8 9 24 12 14 0 14 18 10 26 11 8 0 10 10 8 26 16 16 1 14 13 10 25 12 10 1 8 12 7 20 13 14 1 10 12 8 26 16 16 0 9 12 7 18 14 13 1 9 13 6 19 15 5 0 15 7 5 21 8 10 1 12 14 7 24 17 15 1 14 9 9 23 13 16 0 11 9 5 31 6 15 0 12 10 8 23 8 8 0 13 10 6 19 14 13 1 14 11 8 26 12 14 1 15 13 8 14 11 12 0 11 13 6 25 16 16 0 9 13 8 27 8 10 1 8 6 6 20 15 15 0 10 13 6 24 16 16 0 10 21 12 32 14 19 1 10 11 5 26 16 14 0 9 9 7 21 9 6 1 13 18 12 21 14 13 0 8 9 11 24 13 7 1 10 15 10 23 15 13 1 11 11 8 24 15 14 1 10 14 9 21 13 13 0 16 14 9 21 11 11 0 11 8 4 13 11 14 1 6 8 11 29 12 14 0 9 11 10 21 7 7 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	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Goodness of Fit
Correlation	NA
R-squared	NA
RMSE	2.8404

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	9	11.2207792207792	-2.22077922077922
2	9	11.2207792207792	-2.22077922077922
3	9	11.2207792207792	-2.22077922077922
4	8	11.2207792207792	-3.22077922077922
5	14	11.2207792207792	2.77922077922078
6	14	11.2207792207792	2.77922077922078
7	15	11.2207792207792	3.77922077922078
8	11	11.2207792207792	-0.220779220779221
9	14	11.2207792207792	2.77922077922078
10	8	11.2207792207792	-3.22077922077922
11	16	11.2207792207792	4.77922077922078
12	11	11.2207792207792	-0.220779220779221
13	7	11.2207792207792	-4.22077922077922
14	9	11.2207792207792	-2.22077922077922
15	16	11.2207792207792	4.77922077922078
16	10	11.2207792207792	-1.22077922077922
17	14	11.2207792207792	2.77922077922078
18	11	11.2207792207792	-0.220779220779221
19	6	11.2207792207792	-5.22077922077922
20	12	11.2207792207792	0.779220779220779
21	14	11.2207792207792	2.77922077922078
22	13	11.2207792207792	1.77922077922078
23	14	11.2207792207792	2.77922077922078
24	10	11.2207792207792	-1.22077922077922
25	14	11.2207792207792	2.77922077922078
26	8	11.2207792207792	-3.22077922077922
27	10	11.2207792207792	-1.22077922077922
28	9	11.2207792207792	-2.22077922077922
29	9	11.2207792207792	-2.22077922077922
30	15	11.2207792207792	3.77922077922078
31	12	11.2207792207792	0.779220779220779
32	14	11.2207792207792	2.77922077922078
33	11	11.2207792207792	-0.220779220779221
34	12	11.2207792207792	0.779220779220779
35	13	11.2207792207792	1.77922077922078
36	14	11.2207792207792	2.77922077922078
37	15	11.2207792207792	3.77922077922078
38	11	11.2207792207792	-0.220779220779221
39	9	11.2207792207792	-2.22077922077922
40	8	11.2207792207792	-3.22077922077922
41	10	11.2207792207792	-1.22077922077922
42	10	11.2207792207792	-1.22077922077922
43	10	11.2207792207792	-1.22077922077922
44	9	11.2207792207792	-2.22077922077922
45	13	11.2207792207792	1.77922077922078
46	8	11.2207792207792	-3.22077922077922
47	10	11.2207792207792	-1.22077922077922
48	11	11.2207792207792	-0.220779220779221
49	10	11.2207792207792	-1.22077922077922
50	16	11.2207792207792	4.77922077922078
51	11	11.2207792207792	-0.220779220779221
52	6	11.2207792207792	-5.22077922077922
53	9	11.2207792207792	-2.22077922077922
54	20	11.2207792207792	8.77922077922078
55	12	11.2207792207792	0.779220779220779
56	9	11.2207792207792	-2.22077922077922
57	14	11.2207792207792	2.77922077922078
58	8	11.2207792207792	-3.22077922077922
59	7	11.2207792207792	-4.22077922077922
60	11	11.2207792207792	-0.220779220779221
61	14	11.2207792207792	2.77922077922078
62	14	11.2207792207792	2.77922077922078
63	9	11.2207792207792	-2.22077922077922
64	16	11.2207792207792	4.77922077922078
65	13	11.2207792207792	1.77922077922078
66	13	11.2207792207792	1.77922077922078
67	8	11.2207792207792	-3.22077922077922
68	9	11.2207792207792	-2.22077922077922
69	11	11.2207792207792	-0.220779220779221
70	8	11.2207792207792	-3.22077922077922
71	7	11.2207792207792	-4.22077922077922
72	11	11.2207792207792	-0.220779220779221
73	9	11.2207792207792	-2.22077922077922
74	16	11.2207792207792	4.77922077922078
75	13	11.2207792207792	1.77922077922078
76	12	11.2207792207792	0.779220779220779
77	9	11.2207792207792	-2.22077922077922

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/28/t1293563574hwgu9ve6wqp05t1/25met1293563707.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/28/t1293563574hwgu9ve6wqp05t1/25met1293563707.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/28/t1293563574hwgu9ve6wqp05t1/3gvdx1293563707.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/28/t1293563574hwgu9ve6wqp05t1/3gvdx1293563707.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/28/t1293563574hwgu9ve6wqp05t1/4qmcz1293563707.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/28/t1293563574hwgu9ve6wqp05t1/4qmcz1293563707.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 2 ; 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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