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RP 1

*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: Sat, 18 Dec 2010 21:31:06 +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/18/t1292707806aqcm57ln7m11e1m.htm/, Retrieved Sat, 18 Dec 2010 22:30:06 +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/18/t1292707806aqcm57ln7m11e1m.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 «

14 11 11 26 9 2 1 1 18 12 8 20 9 1 1 1 11 15 12 21 9 4 1 1 12 10 10 31 14 1 1 2 16 12 7 21 8 5 2 1 18 11 6 18 8 1 1 1 14 5 8 26 11 1 1 1 14 16 16 22 10 1 1 1 15 11 8 22 9 1 1 1 15 15 16 29 15 1 1 1 17 12 7 15 14 2 1 2 19 9 11 16 11 1 1 1 10 11 16 24 14 3 2 2 18 15 16 17 6 1 1 1 14 12 12 19 20 1 1 2 14 16 13 22 9 1 1 2 17 14 19 31 10 1 1 1 14 11 7 28 8 1 1 2 16 10 8 38 11 2 1 1 18 7 12 26 14 4 2 2 14 11 13 25 11 1 1 1 12 10 11 25 16 2 1 1 17 11 8 29 14 1 1 2 9 16 16 28 11 2 4 1 16 14 15 15 11 3 1 2 14 12 11 18 12 1 1 1 11 12 12 21 9 1 2 2 16 11 7 25 7 1 2 1 13 6 9 23 13 1 1 2 17 14 15 23 10 1 1 1 15 9 6 19 9 2 1 1 14 15 14 18 9 1 1 2 16 12 14 18 13 1 1 2 9 12 7 26 16 1 1 2 15 9 15 18 12 1 1 2 17 13 14 18 6 1 1 1 13 15 17 28 14 1 1 2 15 11 14 17 14 1 1 2 16 10 5 29 10 2 2 1 16 13 14 12 4 1 1 2 12 16 8 28 12 1 1 1 11 13 8 20 14 1 1 1 15 14 13 17 9 2 1 1 17 14 14 17 9 1 1 1 13 16 16 20 10 1 1 2 16 9 11 31 14 1 1 1 14 8 10 21 10 1 1 2 11 8 10 19 9 1 1 2 12 12 10 23 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	7 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.3086
R-squared	0.0952
RMSE	2.2518

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	14	14.6593406593407	-0.659340659340659
2	18	14.6593406593407	3.34065934065934
3	11	14.6593406593407	-3.65934065934066
4	12	13.1481481481481	-1.14814814814815
5	16	14.6593406593407	1.34065934065934
6	18	14.6593406593407	3.34065934065934
7	14	14.6593406593407	-0.659340659340659
8	14	14.6593406593407	-0.659340659340659
9	15	14.6593406593407	0.340659340659341
10	15	13.1481481481481	1.85185185185185
11	17	13.1481481481481	3.85185185185185
12	19	14.6593406593407	4.34065934065934
13	10	13.1481481481481	-3.14814814814815
14	18	14.6593406593407	3.34065934065934
15	14	13.1481481481481	0.851851851851851
16	14	14.6593406593407	-0.659340659340659
17	17	14.6593406593407	2.34065934065934
18	14	14.6593406593407	-0.659340659340659
19	16	14.6593406593407	1.34065934065934
20	18	13.1481481481481	4.85185185185185
21	14	14.6593406593407	-0.659340659340659
22	12	13.1481481481481	-1.14814814814815
23	17	13.1481481481481	3.85185185185185
24	9	14.6593406593407	-5.65934065934066
25	16	14.6593406593407	1.34065934065934
26	14	13.1481481481481	0.851851851851851
27	11	14.6593406593407	-3.65934065934066
28	16	14.6593406593407	1.34065934065934
29	13	13.1481481481481	-0.148148148148149
30	17	14.6593406593407	2.34065934065934
31	15	14.6593406593407	0.340659340659341
32	14	14.6593406593407	-0.659340659340659
33	16	13.1481481481481	2.85185185185185
34	9	13.1481481481481	-4.14814814814815
35	15	13.1481481481481	1.85185185185185
36	17	14.6593406593407	2.34065934065934
37	13	13.1481481481481	-0.148148148148149
38	15	13.1481481481481	1.85185185185185
39	16	14.6593406593407	1.34065934065934
40	16	14.6593406593407	1.34065934065934
41	12	13.1481481481481	-1.14814814814815
42	11	13.1481481481481	-2.14814814814815
43	15	14.6593406593407	0.340659340659341
44	17	14.6593406593407	2.34065934065934
45	13	14.6593406593407	-1.65934065934066
46	16	13.1481481481481	2.85185185185185
47	14	14.6593406593407	-0.659340659340659
48	11	14.6593406593407	-3.65934065934066
49	12	13.1481481481481	-1.14814814814815
50	12	14.6593406593407	-2.65934065934066
51	15	14.6593406593407	0.340659340659341
52	16	14.6593406593407	1.34065934065934
53	15	14.6593406593407	0.340659340659341
54	12	14.6593406593407	-2.65934065934066
55	12	14.6593406593407	-2.65934065934066
56	8	13.1481481481481	-5.14814814814815
57	13	14.6593406593407	-1.65934065934066
58	11	14.6593406593407	-3.65934065934066
59	14	14.6593406593407	-0.659340659340659
60	15	13.1481481481481	1.85185185185185
61	10	14.6593406593407	-4.65934065934066
62	11	14.6593406593407	-3.65934065934066
63	12	13.1481481481481	-1.14814814814815
64	15	13.1481481481481	1.85185185185185
65	15	14.6593406593407	0.340659340659341
66	14	13.1481481481481	0.851851851851851
67	16	13.1481481481481	2.85185185185185
68	15	14.6593406593407	0.340659340659341
69	15	14.6593406593407	0.340659340659341
70	13	14.6593406593407	-1.65934065934066
71	17	14.6593406593407	2.34065934065934
72	13	13.1481481481481	-0.148148148148149
73	15	14.6593406593407	0.340659340659341
74	13	14.6593406593407	-1.65934065934066
75	15	13.1481481481481	1.85185185185185
76	16	14.6593406593407	1.34065934065934
77	15	14.6593406593407	0.340659340659341
78	16	14.6593406593407	1.34065934065934
79	15	14.6593406593407	0.340659340659341
80	14	14.6593406593407	-0.659340659340659
81	15	13.1481481481481	1.85185185185185
82	7	13.1481481481481	-6.14814814814815
83	17	14.6593406593407	2.34065934065934
84	13	14.6593406593407	-1.65934065934066
85	15	14.6593406593407	0.340659340659341
86	14	13.1481481481481	0.851851851851851
87	13	13.1481481481481	-0.148148148148149
88	16	14.6593406593407	1.34065934065934
89	12	14.6593406593407	-2.65934065934066
90	14	14.6593406593407	-0.659340659340659
91	17	14.6593406593407	2.34065934065934
92	15	14.6593406593407	0.340659340659341
93	17	13.1481481481481	3.85185185185185
94	12	13.1481481481481	-1.14814814814815
95	16	14.6593406593407	1.34065934065934
96	11	14.6593406593407	-3.65934065934066
97	15	13.1481481481481	1.85185185185185
98	9	14.6593406593407	-5.65934065934066
99	16	14.6593406593407	1.34065934065934
100	10	13.1481481481481	-3.14814814814815
101	10	13.1481481481481	-3.14814814814815
102	15	14.6593406593407	0.340659340659341
103	11	13.1481481481481	-2.14814814814815
104	13	14.6593406593407	-1.65934065934066
105	14	13.1481481481481	0.851851851851851
106	18	14.6593406593407	3.34065934065934
107	16	14.6593406593407	1.34065934065934
108	14	13.1481481481481	0.851851851851851
109	14	13.1481481481481	0.851851851851851
110	14	13.1481481481481	0.851851851851851
111	14	14.6593406593407	-0.659340659340659
112	12	13.1481481481481	-1.14814814814815
113	14	14.6593406593407	-0.659340659340659
114	15	14.6593406593407	0.340659340659341
115	15	14.6593406593407	0.340659340659341
116	13	13.1481481481481	-0.148148148148149
117	17	14.6593406593407	2.34065934065934
118	17	14.6593406593407	2.34065934065934
119	19	14.6593406593407	4.34065934065934
120	15	14.6593406593407	0.340659340659341
121	13	14.6593406593407	-1.65934065934066
122	9	13.1481481481481	-4.14814814814815
123	15	14.6593406593407	0.340659340659341
124	15	14.6593406593407	0.340659340659341
125	16	14.6593406593407	1.34065934065934
126	11	13.1481481481481	-2.14814814814815
127	14	14.6593406593407	-0.659340659340659
128	11	13.1481481481481	-2.14814814814815
129	15	13.1481481481481	1.85185185185185
130	13	13.1481481481481	-0.148148148148149
131	16	13.1481481481481	2.85185185185185
132	14	14.6593406593407	-0.659340659340659
133	15	14.6593406593407	0.340659340659341
134	16	13.1481481481481	2.85185185185185
135	16	14.6593406593407	1.34065934065934
136	11	13.1481481481481	-2.14814814814815
137	13	14.6593406593407	-1.65934065934066
138	16	14.6593406593407	1.34065934065934
139	12	13.1481481481481	-1.14814814814815
140	9	13.1481481481481	-4.14814814814815
141	13	13.1481481481481	-0.148148148148149
142	13	14.6593406593407	-1.65934065934066
143	14	14.6593406593407	-0.659340659340659
144	19	14.6593406593407	4.34065934065934
145	13	14.6593406593407	-1.65934065934066

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292707806aqcm57ln7m11e1m/2w8e31292707857.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292707806aqcm57ln7m11e1m/2w8e31292707857.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292707806aqcm57ln7m11e1m/3w8e31292707857.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292707806aqcm57ln7m11e1m/3w8e31292707857.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292707806aqcm57ln7m11e1m/47hd61292707857.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292707806aqcm57ln7m11e1m/47hd61292707857.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = none ; 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

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

We may request personal information to be submitted to our servers in order to be able to:

personalize online software applications according to your needs
enforce strict security rules with respect to the data that you upload (e.g. statistical data)
manage user sessions of online applications
alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

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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