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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: Mon, 13 Dec 2010 19:27:53 +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/13/t1292268349wiqlglb3ho107xm.htm/, Retrieved Mon, 13 Dec 2010 20:25:49 +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/13/t1292268349wiqlglb3ho107xm.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 «

15 15 11 12 13 6 1 9 12 12 7 11 4 0 12 15 12 13 14 6 0 15 12 11 11 12 5 0 17 14 11 16 12 5 0 14 8 10 10 6 4 0 9 11 11 15 10 5 1 12 15 9 5 11 3 1 11 4 10 4 10 2 0 13 13 12 7 12 5 0 16 19 12 15 15 6 1 16 10 12 5 13 6 1 10 6 9 15 11 6 0 16 7 12 13 12 3 1 12 14 12 13 13 6 0 15 16 12 15 14 6 0 13 16 12 15 16 7 1 18 14 13 10 16 8 1 13 15 11 17 16 6 0 17 14 12 14 15 7 1 14 12 12 9 13 4 1 13 9 15 6 8 4 0 13 12 11 11 14 2 1 15 14 12 13 15 6 1 13 12 10 12 13 6 1 15 14 11 10 16 6 1 13 10 13 4 13 6 1 14 14 6 13 12 6 1 13 16 12 15 15 7 1 14 8 10 10 14 3 1 15 11 12 7 13 6 1 9 8 11 9 12 4 0 16 13 9 14 14 6 0 16 11 10 5 13 3 1 13 16 12 16 14 6 0 17 16 11 14 15 6 1 15 13 12 16 16 6 1 14 14 11 15 15 8 1 10 5 14 4 5 2 0 13 14 10 12 15 6 0 16 14 11 15 16 6 0 16 14 11 15 16 6 0 15 11 10 12 14 5 1 15 15 12 13 13 6 1 12 16 11 14 14 6 1 15 11 12 15 12 6 0 17 10 11 13 15 6 1 10 8 7 4 13 6 1 11 9 11 8 10 4 0 15 12 8 13 13 5 1 15 14 11 15 14 6 0 7 12 12 15 13 6 1 14 14 14 17 18 6 0 1 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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.6235
R-squared	0.3888
RMSE	1.8972

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	13	14.3414634146341	-1.34146341463415
2	11	13	-2
3	14	14.3414634146341	-0.341463414634147
4	12	13	-1
5	12	13	-1
6	6	10.4375	-4.4375
7	10	13	-3
8	11	13	-2
9	10	10.4375	-0.4375
10	12	13	-1
11	15	14.3414634146341	0.658536585365853
12	13	14.3414634146341	-1.34146341463415
13	11	10.4375	0.5625
14	12	10.4375	1.5625
15	13	14.3414634146341	-1.34146341463415
16	14	14.3414634146341	-0.341463414634147
17	16	14.3414634146341	1.65853658536585
18	16	14.3414634146341	1.65853658536585
19	16	14.3414634146341	1.65853658536585
20	15	14.3414634146341	0.658536585365853
21	13	13	0
22	8	10.4375	-2.4375
23	14	13	1
24	15	14.3414634146341	0.658536585365853
25	13	14.3414634146341	-1.34146341463415
26	16	14.3414634146341	1.65853658536585
27	13	14.3414634146341	-1.34146341463415
28	12	14.3414634146341	-2.34146341463415
29	15	14.3414634146341	0.658536585365853
30	14	10.4375	3.5625
31	13	14.3414634146341	-1.34146341463415
32	12	10.4375	1.5625
33	14	14.3414634146341	-0.341463414634147
34	13	13	0
35	14	14.3414634146341	-0.341463414634147
36	15	14.3414634146341	0.658536585365853
37	16	14.3414634146341	1.65853658536585
38	15	14.3414634146341	0.658536585365853
39	5	10.4375	-5.4375
40	15	14.3414634146341	0.658536585365853
41	16	14.3414634146341	1.65853658536585
42	16	14.3414634146341	1.65853658536585
43	14	13	1
44	13	14.3414634146341	-1.34146341463415
45	14	14.3414634146341	-0.341463414634147
46	12	14.3414634146341	-2.34146341463415
47	15	14.3414634146341	0.658536585365853
48	13	10.4375	2.5625
49	10	10.4375	-0.4375
50	13	13	0
51	14	14.3414634146341	-0.341463414634147
52	13	14.3414634146341	-1.34146341463415
53	18	14.3414634146341	3.65853658536585
54	16	14.3414634146341	1.65853658536585
55	15	14.3414634146341	0.658536585365853
56	14	13	1
57	16	13	3
58	11	10.4375	0.5625
59	13	13	0
60	14	14.3414634146341	-0.341463414634147
61	14	10.4375	3.5625
62	12	10.4375	1.5625
63	16	13	3
64	14	14.3414634146341	-0.341463414634147
65	12	14.3414634146341	-2.34146341463415
66	13	14.3414634146341	-1.34146341463415
67	13	13	0
68	10	10.4375	-0.4375
69	15	14.3414634146341	0.658536585365853
70	13	13	0
71	14	13	1
72	15	10.4375	4.5625
73	14	14.3414634146341	-0.341463414634147
74	12	13	-1
75	13	14.3414634146341	-1.34146341463415
76	14	13	1
77	4	10.4375	-6.4375

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292268349wiqlglb3ho107xm/2ytdn1292268466.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292268349wiqlglb3ho107xm/2ytdn1292268466.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292268349wiqlglb3ho107xm/3ytdn1292268466.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292268349wiqlglb3ho107xm/3ytdn1292268466.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292268349wiqlglb3ho107xm/4rkv81292268466.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292268349wiqlglb3ho107xm/4rkv81292268466.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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