Home » date » 2010 » Dec » 12 »

WS 10 Recursive Partitioning PS

*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: Sun, 12 Dec 2010 13:44:39 +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/12/t12921613800f036flov71w2l1.htm/, Retrieved Sun, 12 Dec 2010 14:43:00 +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/12/t12921613800f036flov71w2l1.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 41 25 15 9 3 1 38 25 15 9 4 1 37 19 14 9 4 1 42 18 10 8 4 1 40 23 18 15 3 1 43 25 14 9 4 1 40 23 11 11 4 1 45 30 17 6 5 1 45 32 21 10 4 1 44 25 7 11 4 1 42 26 18 16 4 1 32 25 13 11 5 1 32 25 13 11 5 1 41 35 18 7 4 1 38 20 12 10 4 1 38 21 9 9 4 1 24 23 11 15 3 1 46 17 11 6 5 1 42 27 16 12 4 1 46 25 12 10 4 1 43 18 14 14 5 1 38 22 13 9 4 1 39 23 17 14 4 1 40 25 13 14 3 1 37 19 13 9 2 1 41 20 12 8 4 1 46 26 12 10 4 1 26 16 12 9 3 1 37 22 9 9 3 1 39 25 17 9 4 1 44 29 18 11 5 1 38 22 12 10 2 1 38 32 12 8 0 1 38 23 9 14 4 1 33 18 13 10 3 1 43 26 11 14 4 1 41 14 13 15 2 1 49 20 6 8 4 1 45 25 11 10 5 1 31 21 18 13 3 1 30 21 18 13 3 1 38 23 15 10 4 1 39 24 11 11 4 1 40 21 14 10 4 1 36 17 12 16 2 1 49 29 8 6 5 1 41 25 11 11 4 1 18 16 10 12 2 1 42 25 17 14 3 1 41 25 16 9 5 1 43 21 13 11 4 1 46 23 15 8 3 1 41 25 16 8 5 1 39 25 7 11 4 1 42 24 16 16 4 1 35 21 13 12 5 1 36 22 15 14 3 1 48 14 12 8 4 1 41 20 12 10 4 1 47 21 24 14 3 1 41 22 15 10 3 1 31 19 8 5 5 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	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.4502
R-squared	0.2027
RMSE	3.8248

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	25	20.4909090909091	4.50909090909091
2	25	22.5903614457831	2.40963855421687
3	19	22.5903614457831	-3.59036144578313
4	18	22.5903614457831	-4.59036144578313
5	23	20.4909090909091	2.50909090909091
6	25	22.5903614457831	2.40963855421687
7	23	22.5903614457831	0.409638554216869
8	30	22.5903614457831	7.40963855421687
9	32	29	3
10	25	22.5903614457831	2.40963855421687
11	26	29	-3
12	25	22.5903614457831	2.40963855421687
13	25	22.5903614457831	2.40963855421687
14	35	29	6
15	20	22.5903614457831	-2.59036144578313
16	21	22.5903614457831	-1.59036144578313
17	23	20.4909090909091	2.50909090909091
18	17	22.5903614457831	-5.59036144578313
19	27	22.5903614457831	4.40963855421687
20	25	22.5903614457831	2.40963855421687
21	18	22.5903614457831	-4.59036144578313
22	22	22.5903614457831	-0.590361445783131
23	23	22.5903614457831	0.409638554216869
24	25	20.4909090909091	4.50909090909091
25	19	20.4909090909091	-1.49090909090909
26	20	22.5903614457831	-2.59036144578313
27	26	22.5903614457831	3.40963855421687
28	16	20.4909090909091	-4.49090909090909
29	22	20.4909090909091	1.50909090909091
30	25	22.5903614457831	2.40963855421687
31	29	29	0
32	22	20.4909090909091	1.50909090909091
33	32	20.4909090909091	11.5090909090909
34	23	22.5903614457831	0.409638554216869
35	18	20.4909090909091	-2.49090909090909
36	26	22.5903614457831	3.40963855421687
37	14	20.4909090909091	-6.49090909090909
38	20	22.5903614457831	-2.59036144578313
39	25	22.5903614457831	2.40963855421687
40	21	20.4909090909091	0.509090909090908
41	21	20.4909090909091	0.509090909090908
42	23	22.5903614457831	0.409638554216869
43	24	22.5903614457831	1.40963855421687
44	21	22.5903614457831	-1.59036144578313
45	17	20.4909090909091	-3.49090909090909
46	29	22.5903614457831	6.40963855421687
47	25	22.5903614457831	2.40963855421687
48	16	20.4909090909091	-4.49090909090909
49	25	20.4909090909091	4.50909090909091
50	25	22.5903614457831	2.40963855421687
51	21	22.5903614457831	-1.59036144578313
52	23	20.4909090909091	2.50909090909091
53	25	22.5903614457831	2.40963855421687
54	25	22.5903614457831	2.40963855421687
55	24	22.5903614457831	1.40963855421687
56	21	22.5903614457831	-1.59036144578313
57	22	20.4909090909091	1.50909090909091
58	14	22.5903614457831	-8.59036144578313
59	20	22.5903614457831	-2.59036144578313
60	21	20.4909090909091	0.509090909090908
61	22	20.4909090909091	1.50909090909091
62	19	22.5903614457831	-3.59036144578313
63	28	29	-1
64	25	22.5903614457831	2.40963855421687
65	21	22.5903614457831	-1.59036144578313
66	27	20.4909090909091	6.50909090909091
67	19	22.5903614457831	-3.59036144578313
68	20	20.4909090909091	-0.490909090909092
69	17	20.4909090909091	-3.49090909090909
70	22	22.5903614457831	-0.590361445783131
71	26	22.5903614457831	3.40963855421687
72	17	20.4909090909091	-3.49090909090909
73	15	22.5903614457831	-7.59036144578313
74	27	22.5903614457831	4.40963855421687
75	25	20.4909090909091	4.50909090909091
76	19	22.5903614457831	-3.59036144578313
77	18	22.5903614457831	-4.59036144578313
78	15	22.5903614457831	-7.59036144578313
79	29	22.5903614457831	6.40963855421687
80	24	22.5903614457831	1.40963855421687
81	24	22.5903614457831	1.40963855421687
82	22	20.4909090909091	1.50909090909091
83	22	20.4909090909091	1.50909090909091
84	25	20.4909090909091	4.50909090909091
85	21	20.4909090909091	0.509090909090908
86	21	20.4909090909091	0.509090909090908
87	18	22.5903614457831	-4.59036144578313
88	10	20.4909090909091	-10.4909090909091
89	18	20.4909090909091	-2.49090909090909
90	23	22.5903614457831	0.409638554216869
91	24	22.5903614457831	1.40963855421687
92	32	22.5903614457831	9.40963855421687
93	24	22.5903614457831	1.40963855421687
94	17	22.5903614457831	-5.59036144578313
95	30	29	1
96	25	22.5903614457831	2.40963855421687
97	23	22.5903614457831	0.409638554216869
98	19	20.4909090909091	-1.49090909090909
99	21	22.5903614457831	-1.59036144578313
100	24	20.4909090909091	3.50909090909091
101	23	20.4909090909091	2.50909090909091
102	19	20.4909090909091	-1.49090909090909
103	27	22.5903614457831	4.40963855421687
104	26	22.5903614457831	3.40963855421687
105	26	29	-3
106	16	20.4909090909091	-4.49090909090909
107	27	20.4909090909091	6.50909090909091
108	14	22.5903614457831	-8.59036144578313
109	18	20.4909090909091	-2.49090909090909
110	21	20.4909090909091	0.509090909090908
111	22	22.5903614457831	-0.590361445783131
112	31	22.5903614457831	8.40963855421687
113	23	22.5903614457831	0.409638554216869
114	24	22.5903614457831	1.40963855421687
115	19	20.4909090909091	-1.49090909090909
116	22	20.4909090909091	1.50909090909091
117	24	22.5903614457831	1.40963855421687
118	28	22.5903614457831	5.40963855421687
119	24	22.5903614457831	1.40963855421687
120	15	22.5903614457831	-7.59036144578313
121	21	20.4909090909091	0.509090909090908
122	21	22.5903614457831	-1.59036144578313
123	13	22.5903614457831	-9.59036144578313
124	20	20.4909090909091	-0.490909090909092
125	22	22.5903614457831	-0.590361445783131
126	19	20.4909090909091	-1.49090909090909
127	26	29	-3
128	19	20.4909090909091	-1.49090909090909
129	20	22.5903614457831	-2.59036144578313
130	14	20.4909090909091	-6.49090909090909
131	17	22.5903614457831	-5.59036144578313
132	29	22.5903614457831	6.40963855421687
133	21	20.4909090909091	0.509090909090908
134	19	22.5903614457831	-3.59036144578313
135	17	20.4909090909091	-3.49090909090909
136	19	20.4909090909091	-1.49090909090909
137	17	20.4909090909091	-3.49090909090909
138	19	22.5903614457831	-3.59036144578313
139	21	20.4909090909091	0.509090909090908
140	20	20.4909090909091	-0.490909090909092
141	20	20.4909090909091	-0.490909090909092
142	29	22.5903614457831	6.40963855421687
143	23	22.5903614457831	0.409638554216869
144	23	22.5903614457831	0.409638554216869
145	19	20.4909090909091	-1.49090909090909
146	22	22.5903614457831	-0.590361445783131

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921613800f036flov71w2l1/2vjz01292161471.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921613800f036flov71w2l1/2vjz01292161471.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921613800f036flov71w2l1/3vjz01292161471.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921613800f036flov71w2l1/3vjz01292161471.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921613800f036flov71w2l1/4oby31292161471.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921613800f036flov71w2l1/4oby31292161471.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

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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