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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, 14 Dec 2010 16:15:29 +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/14/t12923432475k3u5t1b2rzl4r8.htm/, Retrieved Tue, 14 Dec 2010 17:14:07 +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/14/t12923432475k3u5t1b2rzl4r8.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 13 13 14 13 3 0 12 12 8 13 5 1 15 10 12 16 6 1 12 9 7 12 6 1 10 10 10 11 5 1 12 12 7 12 3 0 15 13 16 18 8 1 9 12 11 11 4 0 12 12 14 14 4 0 11 6 6 9 4 1 11 5 16 14 6 0 11 12 11 12 6 0 15 11 16 11 5 1 7 14 12 12 4 0 11 14 7 13 6 1 11 12 13 11 4 1 10 12 11 12 6 0 14 11 15 16 6 0 10 11 7 9 4 0 6 7 9 11 4 0 11 9 7 13 2 0 15 11 14 15 7 0 11 11 15 10 5 0 12 12 7 11 4 1 14 12 15 13 6 1 15 11 17 16 6 0 9 11 15 15 7 1 13 8 14 14 5 1 13 9 14 14 6 1 16 12 8 14 4 1 13 10 8 8 4 0 12 10 14 13 7 1 14 12 14 15 7 1 11 8 8 13 4 0 9 12 11 11 4 0 16 11 16 15 6 1 12 12 10 15 6 0 10 7 8 9 5 1 13 11 14 13 6 1 16 11 16 16 7 1 14 12 13 13 6 1 15 9 5 11 3 1 5 15 8 12 3 0 8 11 10 12 4 0 11 11 8 12 6 1 16 11 13 14 7 1 17 11 15 14 5 1 9 15 6 8 4 1 9 11 12 13 5 1 13 12 16 16 6 1 10 12 5 13 6 0 6 9 15 11 6 1 12 12 12 14 5 1 8 12 8 13 4 1 14 13 13 13 5 1 12 11 14 13 5 0 11 9 12 12 4 0 16 9 16 16 6 1 8 11 10 15 2 0 15 11 15 15 8 1 7 12 8 12 3 0 16 12 16 14 6 1 14 9 19 12 6 1 16 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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.7449
R-squared	0.5549
RMSE	0.9433

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	3	5.42105263157895	-2.42105263157895
2	5	4.31818181818182	0.681818181818182
3	6	6.6	-0.6
4	6	4.31818181818182	1.68181818181818
5	5	5.72727272727273	-0.727272727272728
6	3	4.31818181818182	-1.31818181818182
7	8	6.6	1.4
8	4	3.5	0.5
9	4	5.42105263157895	-1.42105263157895
10	4	4.31818181818182	-0.318181818181818
11	6	5.42105263157895	0.578947368421052
12	6	5.72727272727273	0.272727272727272
13	5	5.42105263157895	-0.421052631578948
14	4	5.42105263157895	-1.42105263157895
15	6	4.31818181818182	1.68181818181818
16	4	5.42105263157895	-1.42105263157895
17	6	5.72727272727273	0.272727272727272
18	6	6.6	-0.6
19	4	4.31818181818182	-0.318181818181818
20	4	3.5	0.5
21	2	4.31818181818182	-2.31818181818182
22	7	6.6	0.4
23	5	5.42105263157895	-0.421052631578948
24	4	4.31818181818182	-0.318181818181818
25	6	5.42105263157895	0.578947368421052
26	6	6.6	-0.6
27	7	6.6	0.4
28	5	5.42105263157895	-0.421052631578948
29	6	5.42105263157895	0.578947368421052
30	4	4.31818181818182	-0.318181818181818
31	4	4.31818181818182	-0.318181818181818
32	7	5.42105263157895	1.57894736842105
33	7	6.6	0.4
34	4	4.31818181818182	-0.318181818181818
35	4	3.5	0.5
36	6	6.6	-0.6
37	6	5.72727272727273	0.272727272727272
38	5	4.31818181818182	0.681818181818182
39	6	5.42105263157895	0.578947368421052
40	7	6.6	0.4
41	6	5.42105263157895	0.578947368421052
42	3	4.31818181818182	-1.31818181818182
43	3	3.5	-0.5
44	4	3.5	0.5
45	6	4.31818181818182	1.68181818181818
46	7	5.42105263157895	1.57894736842105
47	5	5.42105263157895	-0.421052631578948
48	4	3.5	0.5
49	5	5.42105263157895	-0.421052631578948
50	6	6.6	-0.6
51	6	4.31818181818182	1.68181818181818
52	6	5.42105263157895	0.578947368421052
53	5	5.42105263157895	-0.421052631578948
54	4	3.5	0.5
55	5	5.42105263157895	-0.421052631578948
56	5	5.42105263157895	-0.421052631578948
57	4	5.42105263157895	-1.42105263157895
58	6	6.6	-0.6
59	2	3.5	-1.5
60	8	6.6	1.4
61	3	3.5	-0.5
62	6	5.42105263157895	0.578947368421052
63	6	5.42105263157895	0.578947368421052
64	6	6.6	-0.6
65	5	3.5	1.5
66	5	5.42105263157895	-0.421052631578948
67	6	5.42105263157895	0.578947368421052
68	5	4.31818181818182	0.681818181818182
69	6	5.42105263157895	0.578947368421052
70	2	3.5	-1.5
71	5	5.42105263157895	-0.421052631578948
72	5	5.42105263157895	-0.421052631578948
73	5	5.72727272727273	-0.727272727272728
74	6	6.6	-0.6
75	6	5.42105263157895	0.578947368421052
76	6	5.42105263157895	0.578947368421052
77	5	5.42105263157895	-0.421052631578948
78	5	5.42105263157895	-0.421052631578948
79	4	4.31818181818182	-0.318181818181818
80	2	3.5	-1.5
81	4	3.5	0.5
82	6	5.42105263157895	0.578947368421052
83	6	6.6	-0.6
84	5	5.72727272727273	-0.727272727272728
85	3	4.31818181818182	-1.31818181818182
86	6	5.42105263157895	0.578947368421052
87	4	3.5	0.5
88	5	5.72727272727273	-0.727272727272728
89	8	6.6	1.4
90	4	4.31818181818182	-0.318181818181818
91	6	5.72727272727273	0.272727272727272
92	6	6.6	-0.6
93	7	6.6	0.4
94	6	5.42105263157895	0.578947368421052
95	5	5.72727272727273	-0.727272727272728
96	4	4.31818181818182	-0.318181818181818
97	6	3.5	2.5
98	3	3.5	-0.5
99	5	6.6	-1.6
100	6	5.72727272727273	0.272727272727272
101	7	6.6	0.4
102	7	6.6	0.4
103	6	6.6	-0.6
104	3	4.31818181818182	-1.31818181818182
105	2	3.5	-1.5
106	8	5.72727272727273	2.27272727272727
107	3	3.5	-0.5
108	8	6.6	1.4
109	3	3.5	-0.5
110	4	3.5	0.5
111	5	5.42105263157895	-0.421052631578948
112	7	5.42105263157895	1.57894736842105
113	6	4.31818181818182	1.68181818181818
114	6	6.6	-0.6
115	7	6.6	0.4
116	6	5.42105263157895	0.578947368421052

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t12923432475k3u5t1b2rzl4r8/2g1vb1292343321.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t12923432475k3u5t1b2rzl4r8/2g1vb1292343321.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t12923432475k3u5t1b2rzl4r8/3g1vb1292343321.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t12923432475k3u5t1b2rzl4r8/3g1vb1292343321.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t12923432475k3u5t1b2rzl4r8/4ravw1292343321.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t12923432475k3u5t1b2rzl4r8/4ravw1292343321.ps (open in new window)

Parameters (Session):

par1 = 6 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 6 ; 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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