Home » date » 2010 » Dec » 13 »

Recursive Partitioning Twijfels (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 19:35:50 +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/t1292268843xgp94x7kl65rkiy.htm/, Retrieved Mon, 13 Dec 2010 20:34:04 +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/t1292268843xgp94x7kl65rkiy.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	5 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Goodness of Fit
Correlation	0.4867
R-squared	0.2369
RMSE	3.3357

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	25	21.906976744186	3.09302325581395
2	24	21.906976744186	2.09302325581395
3	21	21.906976744186	-0.906976744186046
4	23	24.3076923076923	-1.30769230769231
5	17	21.906976744186	-4.90697674418605
6	19	21.906976744186	-2.90697674418605
7	18	21.906976744186	-3.90697674418605
8	27	21.906976744186	5.09302325581395
9	23	21.906976744186	1.09302325581395
10	23	21.906976744186	1.09302325581395
11	29	24.3076923076923	4.69230769230769
12	21	21.906976744186	-0.906976744186046
13	26	24.3076923076923	1.69230769230769
14	25	21.906976744186	3.09302325581395
15	25	24.3076923076923	0.692307692307693
16	23	24.3076923076923	-1.30769230769231
17	26	21.906976744186	4.09302325581395
18	20	24.3076923076923	-4.30769230769231
19	29	21.906976744186	7.09302325581395
20	24	24.3076923076923	-0.307692307692307
21	23	21.906976744186	1.09302325581395
22	24	21.906976744186	2.09302325581395
23	30	24.3076923076923	5.69230769230769
24	22	21.906976744186	0.0930232558139537
25	22	24.3076923076923	-2.30769230769231
26	13	21.906976744186	-8.90697674418605
27	24	24.3076923076923	-0.307692307692307
28	17	21.906976744186	-4.90697674418605
29	24	24.3076923076923	-0.307692307692307
30	21	21.906976744186	-0.906976744186046
31	23	21.906976744186	1.09302325581395
32	24	24.3076923076923	-0.307692307692307
33	24	24.3076923076923	-0.307692307692307
34	24	24.3076923076923	-0.307692307692307
35	23	24.3076923076923	-1.30769230769231
36	26	21.906976744186	4.09302325581395
37	24	24.3076923076923	-0.307692307692307
38	21	17.3333333333333	3.66666666666667
39	23	21.906976744186	1.09302325581395
40	28	24.3076923076923	3.69230769230769
41	23	24.3076923076923	-1.30769230769231
42	22	21.906976744186	0.0930232558139537
43	24	21.906976744186	2.09302325581395
44	21	21.906976744186	-0.906976744186046
45	23	21.906976744186	1.09302325581395
46	23	24.3076923076923	-1.30769230769231
47	20	21.906976744186	-1.90697674418605
48	23	17.3333333333333	5.66666666666667
49	21	24.3076923076923	-3.30769230769231
50	27	21.906976744186	5.09302325581395
51	12	17.3333333333333	-5.33333333333333
52	15	21.906976744186	-6.90697674418605
53	22	21.906976744186	0.0930232558139537
54	21	17.3333333333333	3.66666666666667
55	21	24.3076923076923	-3.30769230769231
56	20	21.906976744186	-1.90697674418605
57	24	24.3076923076923	-0.307692307692307
58	24	24.3076923076923	-0.307692307692307
59	29	21.906976744186	7.09302325581395
60	25	21.906976744186	3.09302325581395
61	14	21.906976744186	-7.90697674418605
62	30	24.3076923076923	5.69230769230769
63	19	21.906976744186	-2.90697674418605
64	29	24.3076923076923	4.69230769230769
65	25	21.906976744186	3.09302325581395
66	25	24.3076923076923	0.692307692307693
67	25	24.3076923076923	0.692307692307693
68	16	17.3333333333333	-1.33333333333333
69	25	21.906976744186	3.09302325581395
70	28	24.3076923076923	3.69230769230769
71	24	24.3076923076923	-0.307692307692307
72	25	21.906976744186	3.09302325581395
73	21	21.906976744186	-0.906976744186046
74	22	24.3076923076923	-2.30769230769231
75	20	24.3076923076923	-4.30769230769231
76	25	24.3076923076923	0.692307692307693
77	27	24.3076923076923	2.69230769230769
78	21	21.906976744186	-0.906976744186046
79	13	17.3333333333333	-4.33333333333333
80	26	21.906976744186	4.09302325581395
81	26	21.906976744186	4.09302325581395
82	25	24.3076923076923	0.692307692307693
83	22	21.906976744186	0.0930232558139537
84	19	17.3333333333333	1.66666666666667
85	23	21.906976744186	1.09302325581395
86	25	21.906976744186	3.09302325581395
87	15	17.3333333333333	-2.33333333333333
88	21	21.906976744186	-0.906976744186046
89	23	21.906976744186	1.09302325581395
90	25	21.906976744186	3.09302325581395
91	24	21.906976744186	2.09302325581395
92	24	24.3076923076923	-0.307692307692307
93	21	24.3076923076923	-3.30769230769231
94	24	21.906976744186	2.09302325581395
95	22	24.3076923076923	-2.30769230769231
96	24	21.906976744186	2.09302325581395
97	28	24.3076923076923	3.69230769230769
98	21	21.906976744186	-0.906976744186046
99	17	17.3333333333333	-0.333333333333332
100	28	21.906976744186	6.09302325581395
101	24	24.3076923076923	-0.307692307692307
102	10	21.906976744186	-11.906976744186
103	20	21.906976744186	-1.90697674418605
104	22	21.906976744186	0.0930232558139537
105	19	21.906976744186	-2.90697674418605
106	22	24.3076923076923	-2.30769230769231
107	22	21.906976744186	0.0930232558139537
108	26	24.3076923076923	1.69230769230769
109	24	21.906976744186	2.09302325581395
110	22	21.906976744186	0.0930232558139537
111	20	21.906976744186	-1.90697674418605
112	20	21.906976744186	-1.90697674418605
113	15	21.906976744186	-6.90697674418605
114	20	21.906976744186	-1.90697674418605
115	20	21.906976744186	-1.90697674418605
116	24	21.906976744186	2.09302325581395
117	22	21.906976744186	0.0930232558139537
118	29	21.906976744186	7.09302325581395
119	23	24.3076923076923	-1.30769230769231
120	24	21.906976744186	2.09302325581395
121	22	21.906976744186	0.0930232558139537
122	16	21.906976744186	-5.90697674418605
123	23	24.3076923076923	-1.30769230769231
124	27	24.3076923076923	2.69230769230769
125	16	17.3333333333333	-1.33333333333333
126	21	24.3076923076923	-3.30769230769231
127	26	21.906976744186	4.09302325581395
128	22	24.3076923076923	-2.30769230769231
129	23	24.3076923076923	-1.30769230769231
130	19	21.906976744186	-2.90697674418605
131	18	21.906976744186	-3.90697674418605
132	24	24.3076923076923	-0.307692307692307
133	24	21.906976744186	2.09302325581395
134	29	24.3076923076923	4.69230769230769
135	22	17.3333333333333	4.66666666666667
136	24	24.3076923076923	-0.307692307692307
137	22	21.906976744186	0.0930232558139537
138	12	21.906976744186	-9.90697674418605
139	26	24.3076923076923	1.69230769230769
140	18	21.906976744186	-3.90697674418605
141	22	24.3076923076923	-2.30769230769231
142	24	21.906976744186	2.09302325581395
143	21	21.906976744186	-0.906976744186046
144	15	21.906976744186	-6.90697674418605
145	23	21.906976744186	1.09302325581395
146	22	21.906976744186	0.0930232558139537
147	22	21.906976744186	0.0930232558139537
148	24	21.906976744186	2.09302325581395
149	23	21.906976744186	1.09302325581395
150	13	17.3333333333333	-4.33333333333333

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292268843xgp94x7kl65rkiy/2mxtb1292268942.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292268843xgp94x7kl65rkiy/2mxtb1292268942.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292268843xgp94x7kl65rkiy/3mxtb1292268942.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292268843xgp94x7kl65rkiy/3mxtb1292268942.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292268843xgp94x7kl65rkiy/4w7te1292268942.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292268843xgp94x7kl65rkiy/4w7te1292268942.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 7 ; par2 = none ; par3 = 2 ; par4 = yes ;

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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