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WS 10 Recursive Partitioning 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: Sun, 12 Dec 2010 12:49:07 +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/12/t1292158296imop50fpvs4z4nh.htm/, Retrieved Sun, 12 Dec 2010 13:51:39 +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/12/t1292158296imop50fpvs4z4nh.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 «

1 41 25 15 9 3 1 38 25 15 9 4 1 37 19 14 9 4 1 42 18 10 8 4 1 40 23 18 15 3 1 43 25 14 9 4 1 40 23 11 11 4 1 45 30 17 6 5 1 45 32 21 10 4 1 44 25 7 11 4 1 42 26 18 16 4 1 32 25 13 11 5 1 32 25 13 11 5 1 41 35 18 7 4 1 38 20 12 10 4 1 38 21 9 9 4 1 24 23 11 15 3 1 46 17 11 6 5 1 42 27 16 12 4 1 46 25 12 10 4 1 43 18 14 14 5 1 38 22 13 9 4 1 39 23 17 14 4 1 40 25 13 14 3 1 37 19 13 9 2 1 41 20 12 8 4 1 46 26 12 10 4 1 26 16 12 9 3 1 37 22 9 9 3 1 39 25 17 9 4 1 44 29 18 11 5 1 38 22 12 10 2 1 38 32 12 8 0 1 38 23 9 14 4 1 33 18 13 10 3 1 43 26 11 14 4 1 41 14 13 15 2 1 49 20 6 8 4 1 45 25 11 10 5 1 31 21 18 13 3 1 30 21 18 13 3 1 38 23 15 10 4 1 39 24 11 11 4 1 40 21 14 10 4 1 36 17 12 16 2 1 49 29 8 6 5 1 41 25 11 11 4 1 18 16 10 12 2 1 42 25 17 14 3 1 41 25 16 9 5 1 43 21 13 11 4 1 46 23 15 8 3 1 41 25 16 8 5 1 39 25 7 11 4 1 42 24 16 16 4 1 35 21 13 12 5 1 36 22 15 14 3 1 48 14 12 8 4 1 41 20 12 10 4 1 47 21 24 14 3 1 41 22 15 10 3 1 31 19 8 5 5 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	'George Udny Yule' @ 72.249.76.132

Goodness of Fit
Correlation	0.2793
R-squared	0.078
RMSE	5.0559

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	41	37.7454545454545	3.25454545454546
2	38	40.7802197802198	-2.78021978021978
3	37	40.7802197802198	-3.78021978021978
4	42	40.7802197802198	1.21978021978022
5	40	37.7454545454545	2.25454545454546
6	43	40.7802197802198	2.21978021978022
7	40	40.7802197802198	-0.780219780219781
8	45	40.7802197802198	4.21978021978022
9	45	40.7802197802198	4.21978021978022
10	44	40.7802197802198	3.21978021978022
11	42	40.7802197802198	1.21978021978022
12	32	40.7802197802198	-8.78021978021978
13	32	40.7802197802198	-8.78021978021978
14	41	40.7802197802198	0.219780219780219
15	38	40.7802197802198	-2.78021978021978
16	38	40.7802197802198	-2.78021978021978
17	24	37.7454545454545	-13.7454545454545
18	46	40.7802197802198	5.21978021978022
19	42	40.7802197802198	1.21978021978022
20	46	40.7802197802198	5.21978021978022
21	43	40.7802197802198	2.21978021978022
22	38	40.7802197802198	-2.78021978021978
23	39	40.7802197802198	-1.78021978021978
24	40	37.7454545454545	2.25454545454546
25	37	37.7454545454545	-0.745454545454542
26	41	40.7802197802198	0.219780219780219
27	46	40.7802197802198	5.21978021978022
28	26	37.7454545454545	-11.7454545454545
29	37	37.7454545454545	-0.745454545454542
30	39	40.7802197802198	-1.78021978021978
31	44	40.7802197802198	3.21978021978022
32	38	37.7454545454545	0.254545454545458
33	38	37.7454545454545	0.254545454545458
34	38	40.7802197802198	-2.78021978021978
35	33	37.7454545454545	-4.74545454545454
36	43	40.7802197802198	2.21978021978022
37	41	37.7454545454545	3.25454545454546
38	49	40.7802197802198	8.21978021978022
39	45	40.7802197802198	4.21978021978022
40	31	37.7454545454545	-6.74545454545454
41	30	37.7454545454545	-7.74545454545454
42	38	40.7802197802198	-2.78021978021978
43	39	40.7802197802198	-1.78021978021978
44	40	40.7802197802198	-0.780219780219781
45	36	37.7454545454545	-1.74545454545454
46	49	40.7802197802198	8.21978021978022
47	41	40.7802197802198	0.219780219780219
48	18	37.7454545454545	-19.7454545454545
49	42	37.7454545454545	4.25454545454546
50	41	40.7802197802198	0.219780219780219
51	43	40.7802197802198	2.21978021978022
52	46	37.7454545454545	8.25454545454546
53	41	40.7802197802198	0.219780219780219
54	39	40.7802197802198	-1.78021978021978
55	42	40.7802197802198	1.21978021978022
56	35	40.7802197802198	-5.78021978021978
57	36	37.7454545454545	-1.74545454545454
58	48	40.7802197802198	7.21978021978022
59	41	40.7802197802198	0.219780219780219
60	47	37.7454545454545	9.25454545454546
61	41	37.7454545454545	3.25454545454546
62	31	40.7802197802198	-9.78021978021978
63	36	40.7802197802198	-4.78021978021978
64	46	40.7802197802198	5.21978021978022
65	44	40.7802197802198	3.21978021978022
66	43	37.7454545454545	5.25454545454546
67	40	40.7802197802198	-0.780219780219781
68	40	37.7454545454545	2.25454545454546
69	46	37.7454545454545	8.25454545454546
70	39	40.7802197802198	-1.78021978021978
71	44	40.7802197802198	3.21978021978022
72	38	37.7454545454545	0.254545454545458
73	39	40.7802197802198	-1.78021978021978
74	41	40.7802197802198	0.219780219780219
75	39	37.7454545454545	1.25454545454546
76	40	40.7802197802198	-0.780219780219781
77	44	40.7802197802198	3.21978021978022
78	42	40.7802197802198	1.21978021978022
79	46	40.7802197802198	5.21978021978022
80	44	40.7802197802198	3.21978021978022
81	37	40.7802197802198	-3.78021978021978
82	39	37.7454545454545	1.25454545454546
83	40	37.7454545454545	2.25454545454546
84	42	37.7454545454545	4.25454545454546
85	37	37.7454545454545	-0.745454545454542
86	33	37.7454545454545	-4.74545454545454
87	35	40.7802197802198	-5.78021978021978
88	42	37.7454545454545	4.25454545454546
89	36	37.7454545454545	-1.74545454545454
90	44	40.7802197802198	3.21978021978022
91	45	40.7802197802198	4.21978021978022
92	47	40.7802197802198	6.21978021978022
93	40	40.7802197802198	-0.780219780219781
94	49	40.7802197802198	8.21978021978022
95	48	40.7802197802198	7.21978021978022
96	29	40.7802197802198	-11.7802197802198
97	45	40.7802197802198	4.21978021978022
98	29	37.7454545454545	-8.74545454545454
99	41	40.7802197802198	0.219780219780219
100	34	37.7454545454545	-3.74545454545454
101	38	37.7454545454545	0.254545454545458
102	37	37.7454545454545	-0.745454545454542
103	48	40.7802197802198	7.21978021978022
104	39	40.7802197802198	-1.78021978021978
105	34	40.7802197802198	-6.78021978021978
106	35	37.7454545454545	-2.74545454545454
107	41	37.7454545454545	3.25454545454546
108	43	40.7802197802198	2.21978021978022
109	41	37.7454545454545	3.25454545454546
110	39	37.7454545454545	1.25454545454546
111	36	40.7802197802198	-4.78021978021978
112	32	40.7802197802198	-8.78021978021978
113	46	40.7802197802198	5.21978021978022
114	42	40.7802197802198	1.21978021978022
115	42	37.7454545454545	4.25454545454546
116	45	37.7454545454545	7.25454545454546
117	39	40.7802197802198	-1.78021978021978
118	45	40.7802197802198	4.21978021978022
119	48	40.7802197802198	7.21978021978022
120	28	40.7802197802198	-12.7802197802198
121	35	37.7454545454545	-2.74545454545454
122	38	40.7802197802198	-2.78021978021978
123	42	40.7802197802198	1.21978021978022
124	36	37.7454545454545	-1.74545454545454
125	37	40.7802197802198	-3.78021978021978
126	38	37.7454545454545	0.254545454545458
127	43	40.7802197802198	2.21978021978022
128	35	37.7454545454545	-2.74545454545454
129	36	40.7802197802198	-4.78021978021978
130	33	37.7454545454545	-4.74545454545454
131	39	40.7802197802198	-1.78021978021978
132	32	40.7802197802198	-8.78021978021978
133	45	37.7454545454545	7.25454545454546
134	35	40.7802197802198	-5.78021978021978
135	38	37.7454545454545	0.254545454545458
136	36	37.7454545454545	-1.74545454545454
137	42	37.7454545454545	4.25454545454546
138	41	40.7802197802198	0.219780219780219
139	47	37.7454545454545	9.25454545454546
140	35	37.7454545454545	-2.74545454545454
141	43	37.7454545454545	5.25454545454546
142	40	40.7802197802198	-0.780219780219781
143	46	40.7802197802198	5.21978021978022
144	44	40.7802197802198	3.21978021978022
145	35	37.7454545454545	-2.74545454545454
146	29	40.7802197802198	-11.7802197802198

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292158296imop50fpvs4z4nh/28bn41292158139.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292158296imop50fpvs4z4nh/28bn41292158139.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292158296imop50fpvs4z4nh/38bn41292158139.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292158296imop50fpvs4z4nh/38bn41292158139.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292158296imop50fpvs4z4nh/4jk471292158139.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292158296imop50fpvs4z4nh/4jk471292158139.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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