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Recursive Partitioning Leercompetentie (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 16:28:12 +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/t1292257657fhubr7b5h0cqnp8.htm/, Retrieved Mon, 13 Dec 2010 17:27:37 +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/t1292257657fhubr7b5h0cqnp8.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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.4128
R-squared	0.1704
RMSE	2.0633

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	13	16.3611111111111	-3.36111111111111
2	16	16.3611111111111	-0.361111111111111
3	19	15.0862068965517	3.91379310344828
4	15	13.9285714285714	1.07142857142857
5	14	15.0862068965517	-1.08620689655172
6	13	15.0862068965517	-2.08620689655172
7	19	15.0862068965517	3.91379310344828
8	15	16.3611111111111	-1.36111111111111
9	14	15.0862068965517	-1.08620689655172
10	15	13.9285714285714	1.07142857142857
11	16	13.9285714285714	2.07142857142857
12	16	15.0862068965517	0.913793103448276
13	16	13.9285714285714	2.07142857142857
14	17	16.3611111111111	0.638888888888889
15	15	13.9285714285714	1.07142857142857
16	15	15.0862068965517	-0.0862068965517242
17	20	16.3611111111111	3.63888888888889
18	18	15.0862068965517	2.91379310344828
19	16	16.3611111111111	-0.361111111111111
20	16	13.9285714285714	2.07142857142857
21	19	15.0862068965517	3.91379310344828
22	16	13.9285714285714	2.07142857142857
23	17	13.9285714285714	3.07142857142857
24	17	15.0862068965517	1.91379310344828
25	16	15.0862068965517	0.913793103448276
26	15	13.9285714285714	1.07142857142857
27	14	16.3611111111111	-2.36111111111111
28	15	15.0862068965517	-0.0862068965517242
29	12	13.9285714285714	-1.92857142857143
30	14	15.0862068965517	-1.08620689655172
31	16	15.0862068965517	0.913793103448276
32	14	16.3611111111111	-2.36111111111111
33	7	13.9285714285714	-6.92857142857143
34	10	13.9285714285714	-3.92857142857143
35	14	13.9285714285714	0.0714285714285712
36	16	16.3611111111111	-0.361111111111111
37	16	13.9285714285714	2.07142857142857
38	16	13.9285714285714	2.07142857142857
39	14	15.0862068965517	-1.08620689655172
40	20	16.3611111111111	3.63888888888889
41	14	13.9285714285714	0.0714285714285712
42	14	13.9285714285714	0.0714285714285712
43	11	13.9285714285714	-2.92857142857143
44	15	15.0862068965517	-0.0862068965517242
45	16	15.0862068965517	0.913793103448276
46	14	15.0862068965517	-1.08620689655172
47	16	13.9285714285714	2.07142857142857
48	14	15.0862068965517	-1.08620689655172
49	12	15.0862068965517	-3.08620689655172
50	16	13.9285714285714	2.07142857142857
51	9	15.0862068965517	-6.08620689655172
52	14	15.0862068965517	-1.08620689655172
53	16	15.0862068965517	0.913793103448276
54	16	15.0862068965517	0.913793103448276
55	15	15.0862068965517	-0.0862068965517242
56	16	15.0862068965517	0.913793103448276
57	12	13.9285714285714	-1.92857142857143
58	16	16.3611111111111	-0.361111111111111
59	16	16.3611111111111	-0.361111111111111
60	14	16.3611111111111	-2.36111111111111
61	16	13.9285714285714	2.07142857142857
62	17	16.3611111111111	0.638888888888889
63	18	15.0862068965517	2.91379310344828
64	18	13.9285714285714	4.07142857142857
65	12	13.9285714285714	-1.92857142857143
66	16	16.3611111111111	-0.361111111111111
67	10	13.9285714285714	-3.92857142857143
68	14	13.9285714285714	0.0714285714285712
69	18	16.3611111111111	1.63888888888889
70	18	16.3611111111111	1.63888888888889
71	16	16.3611111111111	-0.361111111111111
72	16	16.3611111111111	-0.361111111111111
73	16	13.9285714285714	2.07142857142857
74	13	15.0862068965517	-2.08620689655172
75	16	15.0862068965517	0.913793103448276
76	16	13.9285714285714	2.07142857142857
77	20	16.3611111111111	3.63888888888889
78	16	15.0862068965517	0.913793103448276
79	15	15.0862068965517	-0.0862068965517242
80	15	16.3611111111111	-1.36111111111111
81	16	16.3611111111111	-0.361111111111111
82	14	13.9285714285714	0.0714285714285712
83	15	13.9285714285714	1.07142857142857
84	12	15.0862068965517	-3.08620689655172
85	17	15.0862068965517	1.91379310344828
86	16	16.3611111111111	-0.361111111111111
87	15	13.9285714285714	1.07142857142857
88	13	13.9285714285714	-0.928571428571429
89	16	15.0862068965517	0.913793103448276
90	16	16.3611111111111	-0.361111111111111
91	16	16.3611111111111	-0.361111111111111
92	16	16.3611111111111	-0.361111111111111
93	14	15.0862068965517	-1.08620689655172
94	16	13.9285714285714	2.07142857142857
95	16	13.9285714285714	2.07142857142857
96	20	16.3611111111111	3.63888888888889
97	15	16.3611111111111	-1.36111111111111
98	16	13.9285714285714	2.07142857142857
99	13	15.0862068965517	-2.08620689655172
100	17	16.3611111111111	0.638888888888889
101	16	13.9285714285714	2.07142857142857
102	12	13.9285714285714	-1.92857142857143
103	16	15.0862068965517	0.913793103448276
104	16	13.9285714285714	2.07142857142857
105	17	15.0862068965517	1.91379310344828
106	13	13.9285714285714	-0.928571428571429
107	12	15.0862068965517	-3.08620689655172
108	18	16.3611111111111	1.63888888888889
109	14	13.9285714285714	0.0714285714285712
110	14	13.9285714285714	0.0714285714285712
111	13	13.9285714285714	-0.928571428571429
112	16	15.0862068965517	0.913793103448276
113	13	13.9285714285714	-0.928571428571429
114	16	15.0862068965517	0.913793103448276
115	13	15.0862068965517	-2.08620689655172
116	16	16.3611111111111	-0.361111111111111
117	15	13.9285714285714	1.07142857142857
118	16	13.9285714285714	2.07142857142857
119	15	15.0862068965517	-0.0862068965517242
120	17	16.3611111111111	0.638888888888889
121	15	15.0862068965517	-0.0862068965517242
122	12	15.0862068965517	-3.08620689655172
123	16	15.0862068965517	0.913793103448276
124	10	13.9285714285714	-3.92857142857143
125	16	15.0862068965517	0.913793103448276
126	14	15.0862068965517	-1.08620689655172
127	15	16.3611111111111	-1.36111111111111
128	13	13.9285714285714	-0.928571428571429
129	15	15.0862068965517	-0.0862068965517242
130	11	13.9285714285714	-2.92857142857143
131	12	13.9285714285714	-1.92857142857143
132	8	13.9285714285714	-5.92857142857143
133	16	16.3611111111111	-0.361111111111111
134	15	13.9285714285714	1.07142857142857
135	17	15.0862068965517	1.91379310344828
136	16	16.3611111111111	-0.361111111111111
137	10	13.9285714285714	-3.92857142857143
138	18	15.0862068965517	2.91379310344828
139	13	13.9285714285714	-0.928571428571429
140	15	15.0862068965517	-0.0862068965517242
141	16	15.0862068965517	0.913793103448276
142	16	13.9285714285714	2.07142857142857
143	14	13.9285714285714	0.0714285714285712
144	10	13.9285714285714	-3.92857142857143
145	17	15.0862068965517	1.91379310344828
146	13	15.0862068965517	-2.08620689655172
147	15	15.0862068965517	-0.0862068965517242
148	16	16.3611111111111	-0.361111111111111
149	12	15.0862068965517	-3.08620689655172
150	13	15.0862068965517	-2.08620689655172

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292257657fhubr7b5h0cqnp8/2wupe1292257683.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292257657fhubr7b5h0cqnp8/2wupe1292257683.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292257657fhubr7b5h0cqnp8/3wupe1292257683.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292257657fhubr7b5h0cqnp8/3wupe1292257683.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292257657fhubr7b5h0cqnp8/40vok1292257683.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292257657fhubr7b5h0cqnp8/40vok1292257683.ps (open in new window)

Parameters (Session):

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

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

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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