Home » date » 2010 » Dec » 21 »

PAPER BAEYENS (Recursive Partitioning1)

*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: Tue, 21 Dec 2010 14:20:30 +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/21/t1292941694nb3u5y7gc347vaq.htm/, Retrieved Tue, 21 Dec 2010 15:28:14 +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/21/t1292941694nb3u5y7gc347vaq.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 «

5 13 14 3 12 18 0 15 11 7 12 12 4 10 16 1 12 18 6 15 14 3 9 14 12 12 15 0 11 15 5 11 17 6 11 19 6 15 10 6 7 16 2 11 18 1 11 14 5 10 14 7 14 17 3 10 14 3 6 16 3 11 18 7 15 11 8 11 14 6 12 12 3 14 17 5 15 9 5 9 16 10 13 14 2 13 15 6 16 11 4 13 16 6 12 13 8 14 17 4 11 15 5 9 14 10 16 16 6 12 9 7 10 15 4 13 17 10 16 13 4 14 15 3 15 16 3 5 16 3 8 12 3 11 12 7 16 11 15 17 15 0 9 15 0 9 17 4 13 13 5 10 16 5 6 14 2 12 11 3 8 12 0 14 12 9 12 15 2 11 16 7 16 15 7 8 12 0 15 12 0 7 8 10 16 13 2 14 11 1 16 14 8 9 15 6 14 10 11 11 11 3 13 12 8 15 15 6 5 15 9 15 14 9 13 16 8 11 15 8 11 15 7 12 13 6 12 12 5 12 17 4 12 13 6 14 15 3 6 13 2 7 15 12 14 16 8 14 15 5 10 16 9 13 15 6 12 14 5 9 15 2 12 14 4 16 13 7 10 7 5 14 17 6 10 13 7 16 15 8 15 14 6 12 13 0 10 16 1 8 12 5 8 14 5 11 17 5 13 15 7 16 17 7 16 12 1 14 16 3 11 11 4 4 15 8 14 9 6 9 16 6 14 15 2 8 10 2 8 10 3 11 15 3 12 11 0 11 13 2 14 14 8 15 18 8 16 16 0 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	0.3172
R-squared	0.1006
RMSE	2.7761

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	13	11.4259259259259	1.57407407407407
2	12	11.4259259259259	0.574074074074074
3	15	11.4259259259259	3.57407407407407
4	12	13.4375	-1.4375
5	10	11.4259259259259	-1.42592592592593
6	12	11.4259259259259	0.574074074074074
7	15	11.4259259259259	3.57407407407407
8	9	11.4259259259259	-2.42592592592593
9	12	13.4375	-1.4375
10	11	11.4259259259259	-0.425925925925926
11	11	11.4259259259259	-0.425925925925926
12	11	11.4259259259259	-0.425925925925926
13	15	11.4259259259259	3.57407407407407
14	7	11.4259259259259	-4.42592592592593
15	11	11.4259259259259	-0.425925925925926
16	11	11.4259259259259	-0.425925925925926
17	10	11.4259259259259	-1.42592592592593
18	14	13.4375	0.5625
19	10	11.4259259259259	-1.42592592592593
20	6	11.4259259259259	-5.42592592592593
21	11	11.4259259259259	-0.425925925925926
22	15	13.4375	1.5625
23	11	13.4375	-2.4375
24	12	11.4259259259259	0.574074074074074
25	14	11.4259259259259	2.57407407407407
26	15	11.4259259259259	3.57407407407407
27	9	11.4259259259259	-2.42592592592593
28	13	13.4375	-0.4375
29	13	11.4259259259259	1.57407407407407
30	16	11.4259259259259	4.57407407407407
31	13	11.4259259259259	1.57407407407407
32	12	11.4259259259259	0.574074074074074
33	14	13.4375	0.5625
34	11	11.4259259259259	-0.425925925925926
35	9	11.4259259259259	-2.42592592592593
36	16	13.4375	2.5625
37	12	11.4259259259259	0.574074074074074
38	10	13.4375	-3.4375
39	13	11.4259259259259	1.57407407407407
40	16	13.4375	2.5625
41	14	11.4259259259259	2.57407407407407
42	15	11.4259259259259	3.57407407407407
43	5	11.4259259259259	-6.42592592592593
44	8	11.4259259259259	-3.42592592592593
45	11	11.4259259259259	-0.425925925925926
46	16	13.4375	2.5625
47	17	13.4375	3.5625
48	9	11.4259259259259	-2.42592592592593
49	9	11.4259259259259	-2.42592592592593
50	13	11.4259259259259	1.57407407407407
51	10	11.4259259259259	-1.42592592592593
52	6	11.4259259259259	-5.42592592592593
53	12	11.4259259259259	0.574074074074074
54	8	11.4259259259259	-3.42592592592593
55	14	11.4259259259259	2.57407407407407
56	12	13.4375	-1.4375
57	11	11.4259259259259	-0.425925925925926
58	16	13.4375	2.5625
59	8	13.4375	-5.4375
60	15	11.4259259259259	3.57407407407407
61	7	11.4259259259259	-4.42592592592593
62	16	13.4375	2.5625
63	14	11.4259259259259	2.57407407407407
64	16	11.4259259259259	4.57407407407407
65	9	13.4375	-4.4375
66	14	11.4259259259259	2.57407407407407
67	11	13.4375	-2.4375
68	13	11.4259259259259	1.57407407407407
69	15	13.4375	1.5625
70	5	11.4259259259259	-6.42592592592593
71	15	13.4375	1.5625
72	13	13.4375	-0.4375
73	11	13.4375	-2.4375
74	11	13.4375	-2.4375
75	12	13.4375	-1.4375
76	12	11.4259259259259	0.574074074074074
77	12	11.4259259259259	0.574074074074074
78	12	11.4259259259259	0.574074074074074
79	14	11.4259259259259	2.57407407407407
80	6	11.4259259259259	-5.42592592592593
81	7	11.4259259259259	-4.42592592592593
82	14	13.4375	0.5625
83	14	13.4375	0.5625
84	10	11.4259259259259	-1.42592592592593
85	13	13.4375	-0.4375
86	12	11.4259259259259	0.574074074074074
87	9	11.4259259259259	-2.42592592592593
88	12	11.4259259259259	0.574074074074074
89	16	11.4259259259259	4.57407407407407
90	10	13.4375	-3.4375
91	14	11.4259259259259	2.57407407407407
92	10	11.4259259259259	-1.42592592592593
93	16	13.4375	2.5625
94	15	13.4375	1.5625
95	12	11.4259259259259	0.574074074074074
96	10	11.4259259259259	-1.42592592592593
97	8	11.4259259259259	-3.42592592592593
98	8	11.4259259259259	-3.42592592592593
99	11	11.4259259259259	-0.425925925925926
100	13	11.4259259259259	1.57407407407407
101	16	13.4375	2.5625
102	16	13.4375	2.5625
103	14	11.4259259259259	2.57407407407407
104	11	11.4259259259259	-0.425925925925926
105	4	11.4259259259259	-7.42592592592593
106	14	13.4375	0.5625
107	9	11.4259259259259	-2.42592592592593
108	14	11.4259259259259	2.57407407407407
109	8	11.4259259259259	-3.42592592592593
110	8	11.4259259259259	-3.42592592592593
111	11	11.4259259259259	-0.425925925925926
112	12	11.4259259259259	0.574074074074074
113	11	11.4259259259259	-0.425925925925926
114	14	11.4259259259259	2.57407407407407
115	15	13.4375	1.5625
116	16	13.4375	2.5625
117	16	11.4259259259259	4.57407407407407
118	11	11.4259259259259	-0.425925925925926
119	14	13.4375	0.5625
120	14	11.4259259259259	2.57407407407407
121	12	11.4259259259259	0.574074074074074
122	14	11.4259259259259	2.57407407407407
123	8	13.4375	-5.4375
124	13	13.4375	-0.4375
125	16	13.4375	2.5625
126	12	11.4259259259259	0.574074074074074
127	16	13.4375	2.5625
128	12	11.4259259259259	0.574074074074074
129	11	11.4259259259259	-0.425925925925926
130	4	11.4259259259259	-7.42592592592593
131	16	13.4375	2.5625
132	15	11.4259259259259	3.57407407407407
133	10	11.4259259259259	-1.42592592592593
134	13	13.4375	-0.4375
135	15	11.4259259259259	3.57407407407407
136	12	11.4259259259259	0.574074074074074
137	14	11.4259259259259	2.57407407407407
138	7	11.4259259259259	-4.42592592592593
139	19	11.4259259259259	7.57407407407407
140	12	13.4375	-1.4375
141	12	11.4259259259259	0.574074074074074
142	13	11.4259259259259	1.57407407407407
143	15	11.4259259259259	3.57407407407407
144	8	13.4375	-5.4375
145	12	11.4259259259259	0.574074074074074
146	10	11.4259259259259	-1.42592592592593
147	8	11.4259259259259	-3.42592592592593
148	10	13.4375	-3.4375
149	15	13.4375	1.5625
150	16	11.4259259259259	4.57407407407407
151	13	11.4259259259259	1.57407407407407
152	16	13.4375	2.5625
153	9	11.4259259259259	-2.42592592592593
154	14	11.4259259259259	2.57407407407407
155	14	13.4375	0.5625
156	12	11.4259259259259	0.574074074074074

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941694nb3u5y7gc347vaq/21qgw1292941222.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941694nb3u5y7gc347vaq/21qgw1292941222.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941694nb3u5y7gc347vaq/31qgw1292941222.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941694nb3u5y7gc347vaq/31qgw1292941222.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941694nb3u5y7gc347vaq/4cixh1292941222.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292941694nb3u5y7gc347vaq/4cixh1292941222.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

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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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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