Home » date » 2010 » Dec » 16 »

Recursive Partitioning Expectations (no categorization)

*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: Thu, 16 Dec 2010 22:52:57 +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/16/t1292539945mo0sb67p842lel1.htm/, Retrieved Thu, 16 Dec 2010 23:52:25 +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/16/t1292539945mo0sb67p842lel1.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 26 9 15 25 25 0 16 20 9 15 25 24 0 19 21 9 14 19 21 1 15 31 14 10 18 23 0 14 21 8 10 18 17 0 13 18 8 12 22 19 0 19 26 11 18 29 18 0 15 22 10 12 26 27 0 14 22 9 14 25 23 0 15 29 15 18 23 23 1 16 15 14 9 23 29 0 16 16 11 11 23 21 1 16 24 14 11 24 26 0 17 17 6 17 30 25 1 15 19 20 8 19 25 1 15 22 9 16 24 23 0 20 31 10 21 32 26 1 18 28 8 24 30 20 0 16 38 11 21 29 29 1 16 26 14 14 17 24 0 19 25 11 7 25 23 0 16 25 16 18 26 24 1 17 29 14 18 26 30 0 17 28 11 13 25 22 1 16 15 11 11 23 22 0 15 18 12 13 21 13 1 14 21 9 13 19 24 0 15 25 7 18 35 17 1 12 23 13 14 19 24 0 14 23 10 12 20 21 0 16 19 9 9 21 23 1 14 18 9 12 21 24 1 10 26 16 5 23 24 1 14 18 12 10 19 23 0 16 18 6 11 17 26 1 16 28 14 11 24 24 1 16 17 14 12 15 21 0 14 29 10 12 25 23 1 20 12 4 15 27 28 1 14 25 12 12 29 23 0 14 28 12 16 27 22 0 11 20 14 14 18 24 0 15 17 9 17 25 21 0 16 17 9 13 22 23 1 14 20 10 10 26 23 0 16 31 14 17 23 20 1 14 21 10 12 16 23 1 12 19 9 13 27 21 0 16 23 14 13 25 27 1 9 15 8 11 14 12 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	0.35
R-squared	0.1225
RMSE	3.1728

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	15	12.9724770642202	2.02752293577982
2	15	12.9724770642202	2.02752293577982
3	14	12.9724770642202	1.02752293577982
4	10	12.9724770642202	-2.97247706422018
5	10	12.9724770642202	-2.97247706422018
6	12	12.9724770642202	-0.972477064220184
7	18	15.4166666666667	2.58333333333333
8	12	12.9724770642202	-0.972477064220184
9	14	12.9724770642202	1.02752293577982
10	18	12.9724770642202	5.02752293577982
11	9	10.6666666666667	-1.66666666666667
12	11	10.6666666666667	0.333333333333334
13	11	12.9724770642202	-1.97247706422018
14	17	15.4166666666667	1.58333333333333
15	8	12.9724770642202	-4.97247706422018
16	16	12.9724770642202	3.02752293577982
17	21	15.4166666666667	5.58333333333333
18	24	15.4166666666667	8.58333333333333
19	21	15.4166666666667	5.58333333333333
20	14	12.9724770642202	1.02752293577982
21	7	12.9724770642202	-5.97247706422018
22	18	12.9724770642202	5.02752293577982
23	18	12.9724770642202	5.02752293577982
24	13	12.9724770642202	0.0275229357798157
25	11	10.6666666666667	0.333333333333334
26	13	12.9724770642202	0.0275229357798157
27	13	12.9724770642202	0.0275229357798157
28	18	15.4166666666667	2.58333333333333
29	14	12.9724770642202	1.02752293577982
30	12	12.9724770642202	-0.972477064220184
31	9	12.9724770642202	-3.97247706422018
32	12	12.9724770642202	-0.972477064220184
33	5	12.9724770642202	-7.97247706422018
34	10	12.9724770642202	-2.97247706422018
35	11	12.9724770642202	-1.97247706422018
36	11	12.9724770642202	-1.97247706422018
37	12	12.9724770642202	-0.972477064220184
38	12	12.9724770642202	-0.972477064220184
39	15	10.6666666666667	4.33333333333333
40	12	15.4166666666667	-3.41666666666667
41	16	12.9724770642202	3.02752293577982
42	14	12.9724770642202	1.02752293577982
43	17	12.9724770642202	4.02752293577982
44	13	12.9724770642202	0.0275229357798157
45	10	12.9724770642202	-2.97247706422018
46	17	12.9724770642202	4.02752293577982
47	12	12.9724770642202	-0.972477064220184
48	13	12.9724770642202	0.0275229357798157
49	13	12.9724770642202	0.0275229357798157
50	11	10.6666666666667	0.333333333333334
51	13	12.9724770642202	0.0275229357798157
52	12	12.9724770642202	-0.972477064220184
53	12	10.6666666666667	1.33333333333333
54	12	12.9724770642202	-0.972477064220184
55	9	12.9724770642202	-3.97247706422018
56	7	12.9724770642202	-5.97247706422018
57	17	12.9724770642202	4.02752293577982
58	12	10.6666666666667	1.33333333333333
59	12	15.4166666666667	-3.41666666666667
60	9	12.9724770642202	-3.97247706422018
61	9	15.4166666666667	-6.41666666666667
62	13	12.9724770642202	0.0275229357798157
63	10	12.9724770642202	-2.97247706422018
64	11	12.9724770642202	-1.97247706422018
65	12	12.9724770642202	-0.972477064220184
66	10	10.6666666666667	-0.666666666666666
67	13	10.6666666666667	2.33333333333333
68	6	10.6666666666667	-4.66666666666667
69	7	10.6666666666667	-3.66666666666667
70	13	12.9724770642202	0.0275229357798157
71	11	12.9724770642202	-1.97247706422018
72	18	12.9724770642202	5.02752293577982
73	9	12.9724770642202	-3.97247706422018
74	9	12.9724770642202	-3.97247706422018
75	11	10.6666666666667	0.333333333333334
76	11	12.9724770642202	-1.97247706422018
77	15	12.9724770642202	2.02752293577982
78	8	12.9724770642202	-4.97247706422018
79	11	12.9724770642202	-1.97247706422018
80	14	12.9724770642202	1.02752293577982
81	14	12.9724770642202	1.02752293577982
82	12	12.9724770642202	-0.972477064220184
83	12	10.6666666666667	1.33333333333333
84	8	10.6666666666667	-2.66666666666667
85	11	12.9724770642202	-1.97247706422018
86	10	12.9724770642202	-2.97247706422018
87	17	12.9724770642202	4.02752293577982
88	16	12.9724770642202	3.02752293577982
89	13	12.9724770642202	0.0275229357798157
90	15	10.6666666666667	4.33333333333333
91	11	12.9724770642202	-1.97247706422018
92	12	10.6666666666667	1.33333333333333
93	16	12.9724770642202	3.02752293577982
94	20	12.9724770642202	7.02752293577982
95	16	12.9724770642202	3.02752293577982
96	11	12.9724770642202	-1.97247706422018
97	15	12.9724770642202	2.02752293577982
98	15	12.9724770642202	2.02752293577982
99	12	12.9724770642202	-0.972477064220184
100	9	15.4166666666667	-6.41666666666667
101	24	12.9724770642202	11.0275229357798
102	15	12.9724770642202	2.02752293577982
103	18	12.9724770642202	5.02752293577982
104	17	12.9724770642202	4.02752293577982
105	12	10.6666666666667	1.33333333333333
106	15	12.9724770642202	2.02752293577982
107	11	12.9724770642202	-1.97247706422018
108	11	12.9724770642202	-1.97247706422018
109	15	12.9724770642202	2.02752293577982
110	12	12.9724770642202	-0.972477064220184
111	14	12.9724770642202	1.02752293577982
112	11	12.9724770642202	-1.97247706422018
113	20	12.9724770642202	7.02752293577982
114	11	12.9724770642202	-1.97247706422018
115	12	12.9724770642202	-0.972477064220184
116	17	12.9724770642202	4.02752293577982
117	12	12.9724770642202	-0.972477064220184
118	11	10.6666666666667	0.333333333333334
119	10	10.6666666666667	-0.666666666666666
120	11	12.9724770642202	-1.97247706422018
121	12	10.6666666666667	1.33333333333333
122	9	10.6666666666667	-1.66666666666667
123	8	12.9724770642202	-4.97247706422018
124	6	10.6666666666667	-4.66666666666667
125	12	12.9724770642202	-0.972477064220184
126	15	12.9724770642202	2.02752293577982
127	13	12.9724770642202	0.0275229357798157
128	17	12.9724770642202	4.02752293577982
129	14	12.9724770642202	1.02752293577982
130	16	12.9724770642202	3.02752293577982
131	16	12.9724770642202	3.02752293577982
132	11	12.9724770642202	-1.97247706422018
133	11	12.9724770642202	-1.97247706422018
134	16	12.9724770642202	3.02752293577982
135	15	15.4166666666667	-0.416666666666666
136	14	12.9724770642202	1.02752293577982
137	9	15.4166666666667	-6.41666666666667
138	13	12.9724770642202	0.0275229357798157
139	11	10.6666666666667	0.333333333333334
140	14	12.9724770642202	1.02752293577982
141	11	12.9724770642202	-1.97247706422018
142	12	12.9724770642202	-0.972477064220184
143	8	10.6666666666667	-2.66666666666667
144	7	12.9724770642202	-5.97247706422018
145	11	12.9724770642202	-1.97247706422018
146	13	10.6666666666667	2.33333333333333
147	9	10.6666666666667	-1.66666666666667
148	12	10.6666666666667	1.33333333333333

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292539945mo0sb67p842lel1/2zid71292539969.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292539945mo0sb67p842lel1/2zid71292539969.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292539945mo0sb67p842lel1/3zid71292539969.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292539945mo0sb67p842lel1/3zid71292539969.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292539945mo0sb67p842lel1/4a9ca1292539969.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292539945mo0sb67p842lel1/4a9ca1292539969.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = none ; par3 = 2 ; par4 = no ;

Parameters (R input):

par1 = 5 ; par2 = none ; par3 = 2 ; 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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