Home » date » 2010 » Dec » 14 »

Recursive Partitioning 10 roken

*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, 14 Dec 2010 21:50:13 +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/14/t1292363589j6cjvot8ja1porg.htm/, Retrieved Tue, 14 Dec 2010 22:53:09 +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/14/t1292363589j6cjvot8ja1porg.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 «

2 1 22 15 16 17 10 1 2 22 23 24 42 9 1 2 22 26 22 39 30 1 2 23 19 21 22 18 1 2 21 19 23 20 16 1 2 21 16 23 31 20 1 1 24 23 21 42 20 2 1 22 22 20 30 18 1 2 21 19 22 33 21 1 2 23 24 20 29 20 1 1 20 19 12 31 20 2 1 23 25 23 39 20 1 1 20 23 23 44 29 1 2 21 31 30 40 14 2 1 22 29 22 42 25 2 2 22 18 21 28 19 2 2 21 17 21 29 19 1 1 20 22 15 35 25 1 1 21 21 22 26 25 1 2 21 24 24 42 19 1 1 20 22 23 26 19 1 1 21 16 15 30 18 1 2 23 22 24 28 24 1 1 23 21 24 24 18 2 1 21 25 21 26 26 1 1 22 22 21 39 26 2 1 20 24 18 33 24 2 1 23 21 20 50 29 2 1 21 25 19 40 26 1 1 21 29 29 49 28 2 1 23 19 20 31 18 2 1 23 29 23 37 19 2 2 22 25 24 29 21 1 1 21 19 27 37 13 1 1 NA 27 28 16 19 1 2 21 25 24 28 26 1 2 21 23 29 29 17 1 1 22 24 24 31 19 2 2 22 23 22 34 28 1 2 22 25 25 30 15 1 1 22 26 24 31 16 1 1 23 23 14 44 18 2 1 NA 22 22 35 25 1 2 22 32 24 47 15 1 2 21 22 24 39 24 1 1 23 18 24 34 24 2 1 21 19 24 15 14 2 2 32 23 22 26 19 2 2 32 24 22 25 20 2 1 21 19 21 30 27 1 1 20 16 21 25 20 1 1 2 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	NA
R-squared	NA
RMSE	0.5031

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	2	1.30921052631579	0.69078947368421
2	1	1.30921052631579	-0.309210526315789
3	1	1.30921052631579	-0.309210526315789
4	1	1.30921052631579	-0.309210526315789
5	1	1.30921052631579	-0.309210526315789
6	1	1.30921052631579	-0.309210526315789
7	1	1.30921052631579	-0.309210526315789
8	2	1.30921052631579	0.69078947368421
9	1	1.30921052631579	-0.309210526315789
10	1	1.30921052631579	-0.309210526315789
11	1	1.30921052631579	-0.309210526315789
12	2	1.30921052631579	0.69078947368421
13	1	1.30921052631579	-0.309210526315789
14	1	1.30921052631579	-0.309210526315789
15	2	1.30921052631579	0.69078947368421
16	2	1.30921052631579	0.69078947368421
17	2	1.30921052631579	0.69078947368421
18	1	1.30921052631579	-0.309210526315789
19	1	1.30921052631579	-0.309210526315789
20	1	1.30921052631579	-0.309210526315789
21	1	1.30921052631579	-0.309210526315789
22	1	1.30921052631579	-0.309210526315789
23	1	1.30921052631579	-0.309210526315789
24	1	1.30921052631579	-0.309210526315789
25	2	1.30921052631579	0.69078947368421
26	1	1.30921052631579	-0.309210526315789
27	2	1.30921052631579	0.69078947368421
28	2	1.30921052631579	0.69078947368421
29	2	1.30921052631579	0.69078947368421
30	1	1.30921052631579	-0.309210526315789
31	2	1.30921052631579	0.69078947368421
32	2	1.30921052631579	0.69078947368421
33	2	1.30921052631579	0.69078947368421
34	1	1.30921052631579	-0.309210526315789
35	1	1.30921052631579	-0.309210526315789
36	1	1.30921052631579	-0.309210526315789
37	1	1.30921052631579	-0.309210526315789
38	1	1.30921052631579	-0.309210526315789
39	2	1.30921052631579	0.69078947368421
40	1	1.30921052631579	-0.309210526315789
41	1	1.30921052631579	-0.309210526315789
42	1	1.30921052631579	-0.309210526315789
43	2	1.30921052631579	0.69078947368421
44	1	1.30921052631579	-0.309210526315789
45	1	1.30921052631579	-0.309210526315789
46	1	1.30921052631579	-0.309210526315789
47	2	1.30921052631579	0.69078947368421
48	2	1.30921052631579	0.69078947368421
49	2	1.30921052631579	0.69078947368421
50	2	1.30921052631579	0.69078947368421
51	1	1.30921052631579	-0.309210526315789
52	1	1.30921052631579	-0.309210526315789
53	1	1.30921052631579	-0.309210526315789
54	1	1.30921052631579	-0.309210526315789
55	1	1.30921052631579	-0.309210526315789
56	1	1.30921052631579	-0.309210526315789
57	1	1.30921052631579	-0.309210526315789
58	1	1.30921052631579	-0.309210526315789
59	2	1.30921052631579	0.69078947368421
60	1	1.30921052631579	-0.309210526315789
61	1	1.30921052631579	-0.309210526315789
62	2	1.30921052631579	0.69078947368421
63	1	1.30921052631579	-0.309210526315789
64	1	1.30921052631579	-0.309210526315789
65	1	1.30921052631579	-0.309210526315789
66	4	1.30921052631579	2.69078947368421
67	1	1.30921052631579	-0.309210526315789
68	1	1.30921052631579	-0.309210526315789
69	2	1.30921052631579	0.69078947368421
70	2	1.30921052631579	0.69078947368421
71	1	1.30921052631579	-0.309210526315789
72	1	1.30921052631579	-0.309210526315789
73	1	1.30921052631579	-0.309210526315789
74	1	1.30921052631579	-0.309210526315789
75	2	1.30921052631579	0.69078947368421
76	2	1.30921052631579	0.69078947368421
77	1	1.30921052631579	-0.309210526315789
78	1	1.30921052631579	-0.309210526315789
79	1	1.30921052631579	-0.309210526315789
80	1	1.30921052631579	-0.309210526315789
81	1	1.30921052631579	-0.309210526315789
82	1	1.30921052631579	-0.309210526315789
83	1	1.30921052631579	-0.309210526315789
84	1	1.30921052631579	-0.309210526315789
85	1	1.30921052631579	-0.309210526315789
86	1	1.30921052631579	-0.309210526315789
87	1	1.30921052631579	-0.309210526315789
88	1	1.30921052631579	-0.309210526315789
89	1	1.30921052631579	-0.309210526315789
90	2	1.30921052631579	0.69078947368421
91	1	1.30921052631579	-0.309210526315789
92	1	1.30921052631579	-0.309210526315789
93	1	1.30921052631579	-0.309210526315789
94	1	1.30921052631579	-0.309210526315789
95	2	1.30921052631579	0.69078947368421
96	1	1.30921052631579	-0.309210526315789
97	1	1.30921052631579	-0.309210526315789
98	1	1.30921052631579	-0.309210526315789
99	1	1.30921052631579	-0.309210526315789
100	2	1.30921052631579	0.69078947368421
101	1	1.30921052631579	-0.309210526315789
102	2	1.30921052631579	0.69078947368421
103	1	1.30921052631579	-0.309210526315789
104	1	1.30921052631579	-0.309210526315789
105	2	1.30921052631579	0.69078947368421
106	2	1.30921052631579	0.69078947368421
107	2	1.30921052631579	0.69078947368421
108	1	1.30921052631579	-0.309210526315789
109	1	1.30921052631579	-0.309210526315789
110	1	1.30921052631579	-0.309210526315789
111	1	1.30921052631579	-0.309210526315789
112	2	1.30921052631579	0.69078947368421
113	1	1.30921052631579	-0.309210526315789
114	1	1.30921052631579	-0.309210526315789
115	1	1.30921052631579	-0.309210526315789
116	2	1.30921052631579	0.69078947368421
117	2	1.30921052631579	0.69078947368421
118	2	1.30921052631579	0.69078947368421
119	2	1.30921052631579	0.69078947368421
120	1	1.30921052631579	-0.309210526315789
121	2	1.30921052631579	0.69078947368421
122	1	1.30921052631579	-0.309210526315789
123	1	1.30921052631579	-0.309210526315789
124	1	1.30921052631579	-0.309210526315789
125	1	1.30921052631579	-0.309210526315789
126	2	1.30921052631579	0.69078947368421
127	2	1.30921052631579	0.69078947368421
128	1	1.30921052631579	-0.309210526315789
129	1	1.30921052631579	-0.309210526315789
130	1	1.30921052631579	-0.309210526315789
131	1	1.30921052631579	-0.309210526315789
132	1	1.30921052631579	-0.309210526315789
133	2	1.30921052631579	0.69078947368421
134	1	1.30921052631579	-0.309210526315789
135	2	1.30921052631579	0.69078947368421
136	1	1.30921052631579	-0.309210526315789
137	1	1.30921052631579	-0.309210526315789
138	1	1.30921052631579	-0.309210526315789
139	1	1.30921052631579	-0.309210526315789
140	1	1.30921052631579	-0.309210526315789
141	1	1.30921052631579	-0.309210526315789
142	1	1.30921052631579	-0.309210526315789
143	2	1.30921052631579	0.69078947368421
144	1	1.30921052631579	-0.309210526315789
145	1	1.30921052631579	-0.309210526315789
146	1	1.30921052631579	-0.309210526315789
147	1	1.30921052631579	-0.309210526315789
148	1	1.30921052631579	-0.309210526315789
149	1	1.30921052631579	-0.309210526315789
150	1	1.30921052631579	-0.309210526315789
151	1	1.30921052631579	-0.309210526315789
152	2	1.30921052631579	0.69078947368421

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292363589j6cjvot8ja1porg/2h8rj1292363403.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292363589j6cjvot8ja1porg/2h8rj1292363403.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292363589j6cjvot8ja1porg/3h8rj1292363403.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292363589j6cjvot8ja1porg/3h8rj1292363403.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292363589j6cjvot8ja1porg/4k9pp1292363403.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292363589j6cjvot8ja1porg/4k9pp1292363403.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par4 = yes ;

Parameters (R input):

par1 = 1 ; par2 = none ; 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

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.

FreeStatistics.org is powered by