Home » date » 2010 » Dec » 13 »

Recursive Partitioning Bezorgdheid over fouten (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: Mon, 13 Dec 2010 18:25:13 +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/t1292264663kusvkmqag0rctzf.htm/, Retrieved Mon, 13 Dec 2010 19:24:24 +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/t1292264663kusvkmqag0rctzf.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 6 25 25 0 16 20 9 6 25 24 0 19 21 9 13 19 21 1 15 31 14 8 18 23 0 14 21 8 7 18 17 0 13 18 8 9 22 19 0 19 26 11 5 29 18 0 15 22 10 8 26 27 0 14 22 9 9 25 23 0 15 29 15 11 23 23 1 16 15 14 8 23 29 0 16 16 11 11 23 21 1 16 24 14 12 24 26 0 17 17 6 8 30 25 1 15 19 20 7 19 25 1 15 22 9 9 24 23 0 20 31 10 12 32 26 1 18 28 8 20 30 20 0 16 38 11 7 29 29 1 16 26 14 8 17 24 0 19 25 11 8 25 23 0 16 25 16 16 26 24 1 17 29 14 10 26 30 0 17 28 11 6 25 22 1 16 15 11 8 23 22 0 15 18 12 9 21 13 1 14 21 9 9 19 24 0 15 25 7 11 35 17 1 12 23 13 12 19 24 0 14 23 10 8 20 21 0 16 19 9 7 21 23 1 14 18 9 8 21 24 1 7 18 13 9 24 24 1 10 26 16 4 23 24 1 14 18 12 8 19 23 0 16 18 6 8 17 26 1 16 28 14 8 24 24 1 16 17 14 6 15 21 0 14 29 10 8 25 23 1 20 12 4 4 27 28 1 14 25 12 7 29 23 0 14 28 12 14 27 22 0 11 20 14 10 18 24 0 15 17 9 9 25 21 0 16 17 9 6 22 23 1 14 20 10 8 26 23 0 16 31 14 11 23 20 1 14 21 10 8 16 23 1 12 19 9 8 27 21 0 16 23 14 10 25 27 1 9 15 8 8 14 12 0 14 24 9 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	4 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Goodness of Fit
Correlation	0.6639
R-squared	0.4408
RMSE	4.249

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	26	22.5925925925926	3.40740740740741
2	20	22.5925925925926	-2.59259259259259
3	21	20.0625	0.9375
4	31	18.4406779661017	12.5593220338983
5	21	18.4406779661017	2.5593220338983
6	18	20.0625	-2.0625
7	26	28.3478260869565	-2.34782608695652
8	22	28.3478260869565	-6.34782608695652
9	22	22.5925925925926	-0.592592592592592
10	29	25.0555555555556	3.94444444444444
11	15	18.4406779661017	-3.4406779661017
12	16	20.0625	-4.0625
13	24	22.5925925925926	1.40740740740741
14	17	16	1
15	19	18.4406779661017	0.559322033898304
16	22	22.5925925925926	-0.592592592592592
17	31	28.3478260869565	2.65217391304348
18	28	28.3478260869565	-0.347826086956523
19	38	28.3478260869565	9.65217391304348
20	26	18.4406779661017	7.5593220338983
21	25	22.5925925925926	2.40740740740741
22	25	28.3478260869565	-3.34782608695652
23	29	28.3478260869565	0.652173913043477
24	28	22.5925925925926	5.40740740740741
25	15	18.4406779661017	-3.4406779661017
26	18	25.0555555555556	-7.05555555555556
27	21	20.0625	0.9375
28	25	16	9
29	23	25.0555555555556	-2.05555555555556
30	23	18.4406779661017	4.5593220338983
31	19	18.4406779661017	0.559322033898304
32	18	18.4406779661017	-0.440677966101696
33	18	22.5925925925926	-4.59259259259259
34	26	18.4406779661017	7.5593220338983
35	18	18.4406779661017	-0.440677966101696
36	18	18.4406779661017	-0.440677966101696
37	28	22.5925925925926	5.40740740740741
38	17	18.4406779661017	-1.4406779661017
39	29	22.5925925925926	6.40740740740741
40	12	16	-4
41	25	28.3478260869565	-3.34782608695652
42	28	28.3478260869565	-0.347826086956523
43	20	25.0555555555556	-5.05555555555556
44	17	22.5925925925926	-5.59259259259259
45	17	18.4406779661017	-1.4406779661017
46	20	28.3478260869565	-8.34782608695652
47	31	25.0555555555556	5.94444444444444
48	21	18.4406779661017	2.5593220338983
49	19	28.3478260869565	-9.34782608695652
50	23	22.5925925925926	0.407407407407408
51	15	18.4406779661017	-3.4406779661017
52	24	20.0625	3.9375
53	28	18.4406779661017	9.5593220338983
54	16	18.4406779661017	-2.4406779661017
55	19	18.4406779661017	0.559322033898304
56	21	20.0625	0.9375
57	21	18.4406779661017	2.5593220338983
58	20	18.4406779661017	1.5593220338983
59	16	18.4406779661017	-2.4406779661017
60	25	28.3478260869565	-3.34782608695652
61	30	25.0555555555556	4.94444444444444
62	29	28.3478260869565	0.652173913043477
63	22	18.4406779661017	3.5593220338983
64	19	18.4406779661017	0.559322033898304
65	33	28.3478260869565	4.65217391304348
66	17	22.5925925925926	-5.59259259259259
67	9	18.4406779661017	-9.4406779661017
68	14	18.4406779661017	-4.4406779661017
69	15	18.4406779661017	-3.4406779661017
70	12	18.4406779661017	-6.4406779661017
71	21	22.5925925925926	-1.59259259259259
72	20	22.5925925925926	-2.59259259259259
73	29	25.0555555555556	3.94444444444444
74	33	28.3478260869565	4.65217391304348
75	21	22.5925925925926	-1.59259259259259
76	15	18.4406779661017	-3.4406779661017
77	19	20.0625	-1.0625
78	23	20.0625	2.9375
79	20	18.4406779661017	1.5593220338983
80	20	22.5925925925926	-2.59259259259259
81	18	20.0625	-2.0625
82	31	25.0555555555556	5.94444444444444
83	18	18.4406779661017	-0.440677966101696
84	13	18.4406779661017	-5.4406779661017
85	9	16	-7
86	20	22.5925925925926	-2.59259259259259
87	18	18.4406779661017	-0.440677966101696
88	23	22.5925925925926	0.407407407407408
89	17	22.5925925925926	-5.59259259259259
90	17	18.4406779661017	-1.4406779661017
91	16	20.0625	-4.0625
92	31	18.4406779661017	12.5593220338983
93	15	18.4406779661017	-3.4406779661017
94	28	22.5925925925926	5.40740740740741
95	26	28.3478260869565	-2.34782608695652
96	20	22.5925925925926	-2.59259259259259
97	19	18.4406779661017	0.559322033898304
98	25	25.0555555555556	-0.0555555555555571
99	18	18.4406779661017	-0.440677966101696
100	20	18.4406779661017	1.5593220338983
101	33	28.3478260869565	4.65217391304348
102	24	25.0555555555556	-1.05555555555556
103	22	18.4406779661017	3.5593220338983
104	32	28.3478260869565	3.65217391304348
105	31	22.5925925925926	8.4074074074074
106	13	18.4406779661017	-5.4406779661017
107	18	20.0625	-2.0625
108	17	18.4406779661017	-1.4406779661017
109	29	28.3478260869565	0.652173913043477
110	22	25.0555555555556	-3.05555555555556
111	18	18.4406779661017	-0.440677966101696
112	22	20.0625	1.9375
113	25	18.4406779661017	6.5593220338983
114	20	22.5925925925926	-2.59259259259259
115	20	18.4406779661017	1.5593220338983
116	17	20.0625	-3.0625
117	21	25.0555555555556	-4.05555555555556
118	26	22.5925925925926	3.40740740740741
119	10	18.4406779661017	-8.4406779661017
120	15	18.4406779661017	-3.4406779661017
121	20	16	4
122	14	18.4406779661017	-4.4406779661017
123	16	18.4406779661017	-2.4406779661017
124	23	18.4406779661017	4.5593220338983
125	11	18.4406779661017	-7.4406779661017
126	19	18.4406779661017	0.559322033898304
127	30	28.3478260869565	1.65217391304348
128	21	18.4406779661017	2.5593220338983
129	20	18.4406779661017	1.5593220338983
130	22	22.5925925925926	-0.592592592592592
131	30	25.0555555555556	4.94444444444444
132	25	25.0555555555556	-0.0555555555555571
133	28	20.0625	7.9375
134	23	25.0555555555556	-2.05555555555556
135	23	20.0625	2.9375
136	21	18.4406779661017	2.5593220338983
137	30	28.3478260869565	1.65217391304348
138	22	18.4406779661017	3.5593220338983
139	32	28.3478260869565	3.65217391304348
140	22	22.5925925925926	-0.592592592592592
141	15	18.4406779661017	-3.4406779661017
142	21	25.0555555555556	-4.05555555555556
143	27	25.0555555555556	1.94444444444444
144	22	25.0555555555556	-3.05555555555556
145	9	16	-7
146	29	28.3478260869565	0.652173913043477
147	20	16	4
148	16	18.4406779661017	-2.4406779661017
149	16	18.4406779661017	-2.4406779661017
150	16	20.0625	-4.0625

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292264663kusvkmqag0rctzf/2f4mz1292264706.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292264663kusvkmqag0rctzf/2f4mz1292264706.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292264663kusvkmqag0rctzf/38v4k1292264706.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292264663kusvkmqag0rctzf/38v4k1292264706.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292264663kusvkmqag0rctzf/4jnl51292264706.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292264663kusvkmqag0rctzf/4jnl51292264706.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 3 ; 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

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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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