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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: Wed, 22 Dec 2010 13:40:54 +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/22/t1293025136o1pefg5t5asvf6a.htm/, Retrieved Wed, 22 Dec 2010 14:38:57 +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/22/t1293025136o1pefg5t5asvf6a.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 «

1,4562 8,1000 7,9000 8,7000 104,5000 2443,2700 16,2000 16,3000 3,0000 -12,0000 65,0000 1,4268 8,3000 8,1000 8,9000 89,1000 2293,4100 12,5000 13,6000 6,0000 -11,0000 55,0000 1,4088 8,1000 8,3000 8,9000 82,6000 2070,8300 14,8000 14,3000 7,0000 -11,0000 57,0000 1,4016 7,4000 8,1000 8,1000 102,7000 2029,6000 15,4000 15,5000 -4,0000 -17,0000 57,0000 1,3650 7,3000 7,4000 8,0000 91,8000 2052,0200 13,6000 13,9000 -5,0000 -18,0000 57,0000 1,3190 7,7000 7,3000 8,3000 94,1000 1864,4400 14,2000 14,3000 -7,0000 -19,0000 65,0000 1,3050 8,0000 7,7000 8,5000 103,1000 1670,0700 15,0000 15,8000 -10,0000 -22,0000 69,0000 1,2785 8,0000 8,0000 8,7000 93,2000 1810,9900 14,1000 14,5000 -21,0000 -24,0000 70,0000 1,3239 7,7000 8,0000 8,6000 91,0000 1905,4100 13,7000 15,1000 -22,0000 -24,0000 71,0000 1,3449 6,9000 7,7000 8,3000 94,3000 1862,8300 14,4000 15,8000 -16,0000 -20,0000 71,0000 1,2732 6,6000 6,9000 7,9000 99,4000 2014,4500 15,6000 17,2000 -25,0000 -25,0000 73,0000 1,3322 6,9000 6,6000 7,9000 115,7000 2197,8200 19,7 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	5 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Goodness of Fit
Correlation	0.8856
R-squared	0.7842
RMSE	0.3551

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	8.1	7.99545454545455	0.104545454545454
2	8.3	7.99545454545455	0.304545454545456
3	8.1	8.45428571428571	-0.354285714285714
4	7.4	7.99545454545455	-0.595454545454545
5	7.3	7.3764705882353	-0.0764705882352947
6	7.7	7.3764705882353	0.323529411764706
7	8	7.3764705882353	0.623529411764705
8	8	7.99545454545455	0.00454545454545485
9	7.7	7.99545454545455	-0.295454545454545
10	6.9	7.3764705882353	-0.476470588235294
11	6.6	7.3764705882353	-0.776470588235295
12	6.9	6.42142857142857	0.478571428571429
13	7.5	7.3764705882353	0.123529411764705
14	7.9	7.3764705882353	0.523529411764706
15	7.7	7.99545454545455	-0.295454545454545
16	6.5	7.3764705882353	-0.876470588235295
17	6.1	6.42142857142857	-0.321428571428572
18	6.4	6.42142857142857	-0.0214285714285714
19	6.8	6.42142857142857	0.378571428571428
20	7.1	6.42142857142857	0.678571428571428
21	7.3	7.3764705882353	-0.0764705882352947
22	7.2	7.3764705882353	-0.176470588235294
23	7	7.3764705882353	-0.376470588235295
24	7	7.3764705882353	-0.376470588235295
25	7	7.3764705882353	-0.376470588235295
26	7.3	7.3764705882353	-0.0764705882352947
27	7.5	7.3764705882353	0.123529411764705
28	7.2	7.3764705882353	-0.176470588235294
29	7.7	7.3764705882353	0.323529411764706
30	8	7.3764705882353	0.623529411764705
31	7.9	7.99545454545455	-0.0954545454545448
32	8	7.99545454545455	0.00454545454545485
33	8	7.99545454545455	0.00454545454545485
34	7.9	7.99545454545455	-0.0954545454545448
35	7.9	7.99545454545455	-0.0954545454545448
36	8	7.99545454545455	0.00454545454545485
37	8.1	7.99545454545455	0.104545454545454
38	8.1	7.99545454545455	0.104545454545454
39	8.2	7.99545454545455	0.204545454545454
40	8	8.45428571428571	-0.454285714285714
41	8.3	7.99545454545455	0.304545454545456
42	8.5	8.45428571428571	0.0457142857142863
43	8.6	8.45428571428571	0.145714285714286
44	8.7	8.45428571428571	0.245714285714286
45	8.7	8.45428571428571	0.245714285714286
46	8.5	8.45428571428571	0.0457142857142863
47	8.4	8.45428571428571	-0.0542857142857134
48	8.5	8.45428571428571	0.0457142857142863
49	8.7	8.45428571428571	0.245714285714286
50	8.7	8.45428571428571	0.245714285714286
51	8.6	8.45428571428571	0.145714285714286
52	7.9	8.45428571428571	-0.554285714285713
53	8.1	7.99545454545455	0.104545454545454
54	8.2	7.99545454545455	0.204545454545454
55	8.5	8.45428571428571	0.0457142857142863
56	8.6	8.45428571428571	0.145714285714286
57	8.5	8.45428571428571	0.0457142857142863
58	8.3	8.45428571428571	-0.154285714285713
59	8.2	8.45428571428571	-0.254285714285714
60	8.7	8.45428571428571	0.245714285714286
61	9.3	8.45428571428571	0.845714285714287
62	9.3	8.45428571428571	0.845714285714287
63	8.8	8.45428571428571	0.345714285714287
64	7.4	8.45428571428571	-1.05428571428571
65	7.2	7.3764705882353	-0.176470588235294
66	7.5	7.3764705882353	0.123529411764705
67	8.3	7.3764705882353	0.923529411764706
68	8.8	8.45428571428571	0.345714285714287
69	8.9	8.45428571428571	0.445714285714287
70	8.6	8.45428571428571	0.145714285714286
71	8.4	8.45428571428571	-0.0542857142857134
72	8.4	8.45428571428571	-0.0542857142857134
73	8.4	8.45428571428571	-0.0542857142857134
74	8.4	8.45428571428571	-0.0542857142857134
75	8.3	8.45428571428571	-0.154285714285713
76	7.6	8.45428571428571	-0.854285714285714
77	7.6	7.3764705882353	0.223529411764705
78	7.9	7.3764705882353	0.523529411764706
79	8	7.99545454545455	0.00454545454545485
80	8.2	7.99545454545455	0.204545454545454
81	8.3	8.45428571428571	-0.154285714285713
82	8.2	8.45428571428571	-0.254285714285714
83	8.1	8.45428571428571	-0.354285714285714
84	8	7.99545454545455	0.00454545454545485
85	7.8	7.99545454545455	-0.195454545454545
86	7.6	7.3764705882353	0.223529411764705
87	7.5	7.3764705882353	0.123529411764705
88	6.8	7.3764705882353	-0.576470588235295
89	6.9	6.42142857142857	0.478571428571429
90	7.1	7.3764705882353	-0.276470588235295
91	7.3	7.3764705882353	-0.0764705882352947
92	7.4	7.3764705882353	0.0235294117647058
93	7.6	7.3764705882353	0.223529411764705
94	7.6	7.3764705882353	0.223529411764705
95	7.5	7.3764705882353	0.123529411764705
96	7.5	7.3764705882353	0.123529411764705
97	6.8	7.3764705882353	-0.576470588235295
98	6.4	6.42142857142857	-0.0214285714285714
99	6.2	6.42142857142857	-0.221428571428572
100	6	6.42142857142857	-0.421428571428572
101	6.3	6.42142857142857	-0.121428571428572
102	6.3	6.42142857142857	-0.121428571428572
103	6.1	6.42142857142857	-0.321428571428572
104	6.1	6.42142857142857	-0.321428571428572
105	6.3	6.42142857142857	-0.121428571428572

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025136o1pefg5t5asvf6a/2g1m21293025247.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025136o1pefg5t5asvf6a/2g1m21293025247.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025136o1pefg5t5asvf6a/3g1m21293025247.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025136o1pefg5t5asvf6a/3g1m21293025247.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025136o1pefg5t5asvf6a/4ra451293025247.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293025136o1pefg5t5asvf6a/4ra451293025247.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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As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

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