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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 10:18:16 +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/t12922356123tdqpiauq7k3rr9.htm/, Retrieved Mon, 13 Dec 2010 11:20:16 +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/t12922356123tdqpiauq7k3rr9.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 15 15 13 6 2 9 12 11 4 2 12 15 14 6 2 15 12 12 5 2 17 14 12 5 2 14 8 6 4 1 9 11 10 5 1 12 15 11 3 2 11 4 10 2 2 13 13 12 5 1 16 19 15 6 1 16 10 13 6 1 15 15 18 8 2 10 6 11 6 1 16 7 12 3 2 12 14 13 6 2 15 16 14 6 1 13 16 16 7 1 18 14 16 8 2 13 15 16 6 1 17 14 15 7 1 14 12 13 4 2 13 9 8 4 1 13 12 14 2 1 15 14 15 6 1 13 12 13 6 1 15 14 16 6 1 13 10 13 6 1 14 14 12 6 1 13 16 15 7 1 16 10 11 4 1 14 8 14 3 2 12 8 14 3 1 18 12 13 5 1 15 11 13 6 2 9 8 12 4 2 16 13 14 6 1 16 11 13 3 2 17 12 12 3 2 13 16 14 6 1 17 16 15 6 1 15 13 16 6 1 14 14 15 8 2 10 5 5 2 2 13 14 15 6 1 11 13 8 4 1 11 16 16 7 2 16 15 14 6 2 16 15 14 6 1 11 15 16 6 1 15 11 14 5 1 15 15 13 6 1 12 16 14 6 1 17 13 14 5 2 15 11 12 6 2 16 12 13 7 1 14 12 15 5 1 17 10 15 6 1 10 8 13 6 2 11 9 10 4 1 15 12 13 5 2 15 14 14 6 1 7 12 13 6 2 17 11 13 4 2 14 14 18 6 2 18 7 12 4 2 14 16 14 7 1 12 16 16 8 2 14 11 13 6 1 9 16 16 6 1 14 13 15 6 1 11 11 14 5 1 15 11 14 5 1 16 13 13 6 1 17 14 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	6 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Goodness of Fit
Correlation	0.6806
R-squared	0.4632
RMSE	2.1802

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	15	12.5428571428571	2.45714285714286
2	12	8.8125	3.1875
3	15	14.3877551020408	0.612244897959183
4	12	12.5428571428571	-0.542857142857143
5	14	12.5428571428571	1.45714285714286
6	8	8.8125	-0.8125
7	11	10.6363636363636	0.363636363636363
8	15	8.8125	6.1875
9	4	8.8125	-4.8125
10	13	12.5428571428571	0.457142857142857
11	19	14.3877551020408	4.61224489795918
12	10	12.5428571428571	-2.54285714285714
13	15	14.3877551020408	0.612244897959183
14	6	10.6363636363636	-4.63636363636364
15	7	8.8125	-1.8125
16	14	10.6363636363636	3.36363636363636
17	16	14.3877551020408	1.61224489795918
18	16	14.3877551020408	1.61224489795918
19	14	14.3877551020408	-0.387755102040817
20	15	14.3877551020408	0.612244897959183
21	14	14.3877551020408	-0.387755102040817
22	12	11.1	0.9
23	9	8.8125	0.1875
24	12	11.1	0.9
25	14	14.3877551020408	-0.387755102040817
26	12	12.5428571428571	-0.542857142857143
27	14	14.3877551020408	-0.387755102040817
28	10	12.5428571428571	-2.54285714285714
29	14	12.5428571428571	1.45714285714286
30	16	14.3877551020408	1.61224489795918
31	10	8.8125	1.1875
32	8	11.1	-3.1
33	8	11.1	-3.1
34	12	12.5428571428571	-0.542857142857143
35	11	12.5428571428571	-1.54285714285714
36	8	8.8125	-0.8125
37	13	14.3877551020408	-1.38775510204082
38	11	11.1	-0.0999999999999996
39	12	8.8125	3.1875
40	16	14.3877551020408	1.61224489795918
41	16	14.3877551020408	1.61224489795918
42	13	14.3877551020408	-1.38775510204082
43	14	14.3877551020408	-0.387755102040817
44	5	8.8125	-3.8125
45	14	14.3877551020408	-0.387755102040817
46	13	8.8125	4.1875
47	16	14.3877551020408	1.61224489795918
48	15	14.3877551020408	0.612244897959183
49	15	14.3877551020408	0.612244897959183
50	15	14.3877551020408	0.612244897959183
51	11	11.75	-0.75
52	15	12.5428571428571	2.45714285714286
53	16	14.3877551020408	1.61224489795918
54	13	11.75	1.25
55	11	12.5428571428571	-1.54285714285714
56	12	12.5428571428571	-0.542857142857143
57	12	11.75	0.25
58	10	14.3877551020408	-4.38775510204082
59	8	10.6363636363636	-2.63636363636364
60	9	8.8125	0.1875
61	12	12.5428571428571	-0.542857142857143
62	14	14.3877551020408	-0.387755102040817
63	12	10.6363636363636	1.36363636363636
64	11	11.1	-0.0999999999999996
65	14	14.3877551020408	-0.387755102040817
66	7	8.8125	-1.8125
67	16	14.3877551020408	1.61224489795918
68	16	14.3877551020408	1.61224489795918
69	11	12.5428571428571	-1.54285714285714
70	16	14.3877551020408	1.61224489795918
71	13	14.3877551020408	-1.38775510204082
72	11	11.75	-0.75
73	11	11.75	-0.75
74	13	12.5428571428571	0.457142857142857
75	14	12.5428571428571	1.45714285714286
76	15	11.1	3.9
77	10	10.6363636363636	-0.636363636363637
78	15	14.3877551020408	0.612244897959183
79	11	14.3877551020408	-3.38775510204082
80	6	8.8125	-2.8125
81	11	11.1	-0.0999999999999996
82	12	11.1	0.9
83	13	14.3877551020408	-1.38775510204082
84	12	14.3877551020408	-2.38775510204082
85	8	11.75	-3.75
86	9	8.8125	0.1875
87	10	11.1	-1.1
88	16	14.3877551020408	1.61224489795918
89	15	12.5428571428571	2.45714285714286
90	14	12.5428571428571	1.45714285714286
91	12	12.5428571428571	-0.542857142857143
92	12	12.5428571428571	-0.542857142857143
93	10	8.8125	1.1875
94	12	11.1	0.9
95	8	8.8125	-0.8125
96	16	14.3877551020408	1.61224489795918
97	11	8.8125	2.1875
98	12	11.1	0.9
99	9	12.5428571428571	-3.54285714285714
100	14	12.5428571428571	1.45714285714286
101	15	14.3877551020408	0.612244897959183
102	8	11.1	-3.1
103	12	11.75	0.25
104	10	10.6363636363636	-0.636363636363637
105	16	14.3877551020408	1.61224489795918
106	17	11.75	5.25
107	8	11.1	-3.1
108	9	12.5428571428571	-3.54285714285714
109	8	11.1	-3.1
110	11	11.75	-0.75
111	16	14.3877551020408	1.61224489795918
112	13	14.3877551020408	-1.38775510204082
113	5	8.8125	-3.8125
114	5	8.8125	-3.8125
115	15	12.5428571428571	2.45714285714286
116	15	12.5428571428571	2.45714285714286
117	12	12.5428571428571	-0.542857142857143
118	12	12.5428571428571	-0.542857142857143
119	16	14.3877551020408	1.61224489795918
120	12	12.5428571428571	-0.542857142857143
121	10	10.6363636363636	-0.636363636363637
122	12	11.75	0.25
123	4	8.8125	-4.8125
124	11	14.3877551020408	-3.38775510204082
125	16	14.3877551020408	1.61224489795918
126	7	8.8125	-1.8125
127	9	8.8125	0.1875
128	14	8.8125	5.1875
129	11	8.8125	2.1875
130	10	10.6363636363636	-0.636363636363637
131	6	8.8125	-2.8125
132	14	12.5428571428571	1.45714285714286
133	11	12.5428571428571	-1.54285714285714
134	11	8.8125	2.1875
135	9	14.3877551020408	-5.38775510204082
136	16	11.1	4.9
137	7	8.8125	-1.8125
138	8	8.8125	-0.8125
139	10	8.8125	1.1875
140	14	12.5428571428571	1.45714285714286
141	9	8.8125	0.1875
142	13	10.6363636363636	2.36363636363636
143	13	8.8125	4.1875
144	12	11.75	0.25
145	11	11.75	-0.75
146	10	14.3877551020408	-4.38775510204082
147	12	12.5428571428571	-0.542857142857143
148	14	14.3877551020408	-0.387755102040817
149	11	14.3877551020408	-3.38775510204082
150	13	11.1	1.9
151	14	14.3877551020408	-0.387755102040817
152	13	12.5428571428571	0.457142857142857
153	16	14.3877551020408	1.61224489795918
154	13	12.5428571428571	0.457142857142857
155	13	10.6363636363636	2.36363636363636
156	12	11.1	0.9
157	9	11.1	-2.1
158	14	11.1	2.9
159	15	14.3877551020408	0.612244897959183

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/13/t12922356123tdqpiauq7k3rr9/24h5s1292235489.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t12922356123tdqpiauq7k3rr9/24h5s1292235489.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t12922356123tdqpiauq7k3rr9/34h5s1292235489.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t12922356123tdqpiauq7k3rr9/34h5s1292235489.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t12922356123tdqpiauq7k3rr9/4f9mv1292235489.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t12922356123tdqpiauq7k3rr9/4f9mv1292235489.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 3 ; 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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We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

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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