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 19:39:27 +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/t1292269049wizm7fvgg6sh6rm.htm/, Retrieved Mon, 13 Dec 2010 20:37:31 +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/t1292269049wizm7fvgg6sh6rm.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	0.4527
R-squared	0.2049
RMSE	2.4574

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	9	12.2142857142857	-3.21428571428571
2	9	10.4430379746835	-1.44303797468354
3	9	8.06666666666667	0.933333333333334
4	14	12.2142857142857	1.78571428571429
5	8	10.4430379746835	-2.44303797468354
6	8	10.4430379746835	-2.44303797468354
7	11	12.2142857142857	-1.21428571428571
8	10	10.4430379746835	-0.443037974683545
9	9	10.4430379746835	-1.44303797468354
10	15	12.2142857142857	2.78571428571429
11	14	10.4430379746835	3.55696202531646
12	11	10.4430379746835	0.556962025316455
13	14	12.2142857142857	1.78571428571429
14	6	8.06666666666667	-2.06666666666667
15	20	10.4430379746835	9.55696202531646
16	9	10.4430379746835	-1.44303797468354
17	10	12.2142857142857	-2.21428571428571
18	8	12.2142857142857	-4.21428571428571
19	11	12.2142857142857	-1.21428571428571
20	14	12.2142857142857	1.78571428571429
21	11	12.2142857142857	-1.21428571428571
22	16	12.2142857142857	3.78571428571429
23	14	12.2142857142857	1.78571428571429
24	11	12.2142857142857	-1.21428571428571
25	11	10.4430379746835	0.556962025316455
26	12	10.4430379746835	1.55696202531646
27	9	10.4430379746835	-1.44303797468354
28	7	12.2142857142857	-5.21428571428571
29	13	12.2142857142857	0.785714285714286
30	10	12.2142857142857	-2.21428571428571
31	9	10.4430379746835	-1.44303797468354
32	9	10.4430379746835	-1.44303797468354
33	13	10.4430379746835	2.55696202531646
34	16	12.2142857142857	3.78571428571429
35	12	10.4430379746835	1.55696202531646
36	6	10.4430379746835	-4.44303797468354
37	14	12.2142857142857	1.78571428571429
38	14	10.4430379746835	3.55696202531646
39	10	12.2142857142857	-2.21428571428571
40	4	8.06666666666667	-4.06666666666667
41	12	12.2142857142857	-0.214285714285714
42	12	12.2142857142857	-0.214285714285714
43	14	10.4430379746835	3.55696202531646
44	9	10.4430379746835	-1.44303797468354
45	9	10.4430379746835	-1.44303797468354
46	10	10.4430379746835	-0.443037974683545
47	14	12.2142857142857	1.78571428571429
48	10	10.4430379746835	-0.443037974683545
49	9	10.4430379746835	-1.44303797468354
50	14	12.2142857142857	1.78571428571429
51	8	10.4430379746835	-2.44303797468354
52	9	12.2142857142857	-3.21428571428571
53	8	12.2142857142857	-4.21428571428571
54	9	10.4430379746835	-1.44303797468354
55	9	10.4430379746835	-1.44303797468354
56	9	10.4430379746835	-1.44303797468354
57	15	10.4430379746835	4.55696202531646
58	8	10.4430379746835	-2.44303797468354
59	10	10.4430379746835	-0.443037974683545
60	8	12.2142857142857	-4.21428571428571
61	14	12.2142857142857	1.78571428571429
62	11	12.2142857142857	-1.21428571428571
63	10	8.06666666666667	1.93333333333333
64	12	8.06666666666667	3.93333333333333
65	14	12.2142857142857	1.78571428571429
66	9	10.4430379746835	-1.44303797468354
67	13	10.4430379746835	2.55696202531646
68	15	10.4430379746835	4.55696202531646
69	8	8.06666666666667	-0.0666666666666664
70	7	8.06666666666667	-1.06666666666667
71	10	10.4430379746835	-0.443037974683545
72	10	10.4430379746835	-0.443037974683545
73	13	12.2142857142857	0.785714285714286
74	11	12.2142857142857	-1.21428571428571
75	8	10.4430379746835	-2.44303797468354
76	12	10.4430379746835	1.55696202531646
77	9	8.06666666666667	0.933333333333334
78	10	12.2142857142857	-2.21428571428571
79	11	10.4430379746835	0.556962025316455
80	11	10.4430379746835	0.556962025316455
81	10	10.4430379746835	-0.443037974683545
82	16	12.2142857142857	3.78571428571429
83	16	10.4430379746835	5.55696202531646
84	8	10.4430379746835	-2.44303797468354
85	6	8.06666666666667	-2.06666666666667
86	11	10.4430379746835	0.556962025316455
87	12	10.4430379746835	1.55696202531646
88	14	12.2142857142857	1.78571428571429
89	9	10.4430379746835	-1.44303797468354
90	11	10.4430379746835	0.556962025316455
91	8	10.4430379746835	-2.44303797468354
92	8	12.2142857142857	-4.21428571428571
93	7	10.4430379746835	-3.44303797468354
94	16	12.2142857142857	3.78571428571429
95	13	12.2142857142857	0.785714285714286
96	8	8.06666666666667	-0.0666666666666664
97	11	10.4430379746835	0.556962025316455
98	14	12.2142857142857	1.78571428571429
99	10	10.4430379746835	-0.443037974683545
100	10	8.06666666666667	1.93333333333333
101	14	12.2142857142857	1.78571428571429
102	14	12.2142857142857	1.78571428571429
103	10	10.4430379746835	-0.443037974683545
104	12	12.2142857142857	-0.214285714285714
105	9	12.2142857142857	-3.21428571428571
106	16	10.4430379746835	5.55696202531646
107	8	10.4430379746835	-2.44303797468354
108	9	8.06666666666667	0.933333333333334
109	16	12.2142857142857	3.78571428571429
110	13	10.4430379746835	2.55696202531646
111	13	10.4430379746835	2.55696202531646
112	8	10.4430379746835	-2.44303797468354
113	14	12.2142857142857	1.78571428571429
114	11	10.4430379746835	0.556962025316455
115	9	10.4430379746835	-1.44303797468354
116	8	10.4430379746835	-2.44303797468354
117	13	10.4430379746835	2.55696202531646
118	13	12.2142857142857	0.785714285714286
119	10	10.4430379746835	-0.443037974683545
120	8	8.06666666666667	-0.0666666666666664
121	7	10.4430379746835	-3.44303797468354
122	11	10.4430379746835	0.556962025316455
123	11	10.4430379746835	0.556962025316455
124	14	12.2142857142857	1.78571428571429
125	6	10.4430379746835	-4.44303797468354
126	10	10.4430379746835	-0.443037974683545
127	9	12.2142857142857	-3.21428571428571
128	12	10.4430379746835	1.55696202531646
129	11	10.4430379746835	0.556962025316455
130	14	10.4430379746835	3.55696202531646
131	12	12.2142857142857	-0.214285714285714
132	14	12.2142857142857	1.78571428571429
133	8	12.2142857142857	-4.21428571428571
134	14	12.2142857142857	1.78571428571429
135	8	12.2142857142857	-4.21428571428571
136	11	10.4430379746835	0.556962025316455
137	12	12.2142857142857	-0.214285714285714
138	9	8.06666666666667	0.933333333333334
139	16	12.2142857142857	3.78571428571429
140	11	10.4430379746835	0.556962025316455
141	11	10.4430379746835	0.556962025316455
142	12	10.4430379746835	1.55696202531646
143	15	12.2142857142857	2.78571428571429
144	13	10.4430379746835	2.55696202531646
145	6	8.06666666666667	-2.06666666666667
146	11	12.2142857142857	-1.21428571428571
147	7	10.4430379746835	-3.44303797468354
148	8	10.4430379746835	-2.44303797468354
149	8	10.4430379746835	-2.44303797468354
150	9	10.4430379746835	-1.44303797468354

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292269049wizm7fvgg6sh6rm/21nz81292269159.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292269049wizm7fvgg6sh6rm/21nz81292269159.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292269049wizm7fvgg6sh6rm/31nz81292269159.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292269049wizm7fvgg6sh6rm/31nz81292269159.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292269049wizm7fvgg6sh6rm/4uegb1292269159.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292269049wizm7fvgg6sh6rm/4uegb1292269159.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 4 ; 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')

}

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Software written by Ed van Stee & Patrick Wessa

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