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WS10

*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 19:38:16 +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/t1292182596949qxj5p88eu2xf.htm/, Retrieved Sun, 12 Dec 2010 20:36:39 +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/t1292182596949qxj5p88eu2xf.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 «

10 11 16 1 24 14 33 12 24 14 11 13 2 25 11 30 8 25 18 15 16 2 17 6 30 8 30 15 9 15 1 18 12 26 8 19 11 17 15 2 16 10 24 7 22 17 16 14 2 20 10 28 4 25 19 9 11 2 16 11 24 11 23 7 12 15 2 18 16 27 7 17 12 14 13 2 17 11 28 7 21 15 4 6 2 30 12 42 10 19 14 13 11 2 23 8 31 10 15 14 12 9 2 18 12 25 8 16 16 13 14 1 12 4 23 4 27 12 15 5 2 21 9 27 9 22 12 10 8 1 15 8 23 8 14 13 9 6 1 20 8 34 7 22 9 11 15 2 27 15 36 9 23 11 15 12 2 21 9 31 13 19 12 10 10 1 31 14 39 8 18 11 9 8 1 19 11 27 8 20 14 15 16 2 16 8 27 9 23 18 12 8 2 20 9 31 6 25 11 12 12 1 21 9 31 9 19 17 14 14 2 17 9 26 6 22 14 16 13 1 22 9 34 9 24 14 5 8 2 26 11 39 5 29 12 10 11 2 25 16 39 16 26 14 9 12 2 25 8 35 7 32 15 14 13 2 17 9 30 9 25 10 5 4 1 33 14 40 6 32 11 12 16 1 32 16 38 6 29 14 14 17 1 13 16 21 5 17 11 16 14 2 32 12 45 12 28 15 11 8 2 22 9 32 9 25 16 6 6 2 17 9 29 5 25 15 11 15 1 33 11 40 6 28 16 9 11 2 31 14 44 11 23 13 16 16 1 20 10 28 8 26 15 13 5 1 15 12 24 8 20 16 10 5 2 29 10 37 8 25 13 6 9 1 23 13 33 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	7 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Goodness of Fit
Correlation	0.5382
R-squared	0.2896
RMSE	2.5528

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	11	13.4444444444444	-2.44444444444444
2	11	13.4444444444444	-2.44444444444444
3	15	13.4444444444444	1.55555555555556
4	9	13.4444444444444	-4.44444444444444
5	17	13.4444444444444	3.55555555555556
6	16	13.4444444444444	2.55555555555556
7	9	10.1587301587302	-1.15873015873016
8	12	13.4444444444444	-1.44444444444444
9	14	13.4444444444444	0.555555555555555
10	4	10.1587301587302	-6.15873015873016
11	13	10.1587301587302	2.84126984126984
12	12	10.1587301587302	1.84126984126984
13	13	13.4444444444444	-0.444444444444445
14	15	10.1587301587302	4.84126984126984
15	10	10.1587301587302	-0.158730158730158
16	9	10.1587301587302	-1.15873015873016
17	11	13.4444444444444	-2.44444444444444
18	15	13.4444444444444	1.55555555555556
19	10	10.1587301587302	-0.158730158730158
20	9	10.1587301587302	-1.15873015873016
21	15	13.4444444444444	1.55555555555556
22	12	10.1587301587302	1.84126984126984
23	12	13.4444444444444	-1.44444444444444
24	14	13.4444444444444	0.555555555555555
25	16	13.4444444444444	2.55555555555556
26	5	10.1587301587302	-5.15873015873016
27	10	10.1587301587302	-0.158730158730158
28	9	13.4444444444444	-4.44444444444444
29	14	13.4444444444444	0.555555555555555
30	5	10.1587301587302	-5.15873015873016
31	12	13.4444444444444	-1.44444444444444
32	14	13.4444444444444	0.555555555555555
33	16	13.4444444444444	2.55555555555556
34	11	10.1587301587302	0.841269841269842
35	6	10.1587301587302	-4.15873015873016
36	11	13.4444444444444	-2.44444444444444
37	9	10.1587301587302	-1.15873015873016
38	16	13.4444444444444	2.55555555555556
39	13	10.1587301587302	2.84126984126984
40	10	10.1587301587302	-0.158730158730158
41	6	10.1587301587302	-4.15873015873016
42	12	10.1587301587302	1.84126984126984
43	15	13.4444444444444	1.55555555555556
44	15	13.4444444444444	1.55555555555556
45	11	10.1587301587302	0.841269841269842
46	16	13.4444444444444	2.55555555555556
47	12	10.1587301587302	1.84126984126984
48	11	10.1587301587302	0.841269841269842
49	14	13.4444444444444	0.555555555555555
50	7	10.1587301587302	-3.15873015873016
51	11	13.4444444444444	-2.44444444444444
52	13	13.4444444444444	-0.444444444444445
53	16	13.4444444444444	2.55555555555556
54	17	13.4444444444444	3.55555555555556
55	12	13.4444444444444	-1.44444444444444
56	14	13.4444444444444	0.555555555555555
57	6	13.4444444444444	-7.44444444444444
58	8	10.1587301587302	-2.15873015873016
59	8	10.1587301587302	-2.15873015873016
60	14	13.4444444444444	0.555555555555555
61	12	13.4444444444444	-1.44444444444444
62	13	10.1587301587302	2.84126984126984
63	9	10.1587301587302	-1.15873015873016
64	12	10.1587301587302	1.84126984126984
65	13	13.4444444444444	-0.444444444444445
66	15	13.4444444444444	1.55555555555556
67	11	10.1587301587302	0.841269841269842
68	14	13.4444444444444	0.555555555555555
69	16	13.4444444444444	2.55555555555556
70	14	10.1587301587302	3.84126984126984
71	8	10.1587301587302	-2.15873015873016
72	16	13.4444444444444	2.55555555555556
73	13	13.4444444444444	-0.444444444444445
74	4	10.1587301587302	-6.15873015873016
75	11	10.1587301587302	0.841269841269842
76	16	13.4444444444444	2.55555555555556
77	8	10.1587301587302	-2.15873015873016
78	14	13.4444444444444	0.555555555555555
79	16	13.4444444444444	2.55555555555556
80	12	10.1587301587302	1.84126984126984
81	16	13.4444444444444	2.55555555555556
82	7	13.4444444444444	-6.44444444444444
83	14	13.4444444444444	0.555555555555555
84	13	10.1587301587302	2.84126984126984
85	12	10.1587301587302	1.84126984126984
86	7	10.1587301587302	-3.15873015873016
87	14	13.4444444444444	0.555555555555555
88	14	13.4444444444444	0.555555555555555
89	11	10.1587301587302	0.841269841269842
90	14	13.4444444444444	0.555555555555555
91	13	13.4444444444444	-0.444444444444445
92	15	13.4444444444444	1.55555555555556
93	12	10.1587301587302	1.84126984126984
94	14	10.1587301587302	3.84126984126984
95	14	13.4444444444444	0.555555555555555
96	16	13.4444444444444	2.55555555555556
97	12	10.1587301587302	1.84126984126984
98	16	13.4444444444444	2.55555555555556
99	11	13.4444444444444	-2.44444444444444
100	10	13.4444444444444	-3.44444444444444
101	11	10.1587301587302	0.841269841269842
102	12	13.4444444444444	-1.44444444444444
103	13	10.1587301587302	2.84126984126984
104	14	13.4444444444444	0.555555555555555
105	11	13.4444444444444	-2.44444444444444
106	11	10.1587301587302	0.841269841269842
107	12	13.4444444444444	-1.44444444444444
108	15	13.4444444444444	1.55555555555556
109	10	10.1587301587302	-0.158730158730158
110	12	13.4444444444444	-1.44444444444444
111	8	10.1587301587302	-2.15873015873016
112	15	13.4444444444444	1.55555555555556
113	13	13.4444444444444	-0.444444444444445
114	12	13.4444444444444	-1.44444444444444
115	12	13.4444444444444	-1.44444444444444
116	10	10.1587301587302	-0.158730158730158
117	11	13.4444444444444	-2.44444444444444
118	10	13.4444444444444	-3.44444444444444
119	8	10.1587301587302	-2.15873015873016
120	8	10.1587301587302	-2.15873015873016
121	12	10.1587301587302	1.84126984126984
122	9	13.4444444444444	-4.44444444444444
123	15	13.4444444444444	1.55555555555556
124	16	13.4444444444444	2.55555555555556
125	13	13.4444444444444	-0.444444444444445
126	7	13.4444444444444	-6.44444444444444
127	8	10.1587301587302	-2.15873015873016
128	8	10.1587301587302	-2.15873015873016
129	9	10.1587301587302	-1.15873015873016
130	16	10.1587301587302	5.84126984126984
131	16	13.4444444444444	2.55555555555556
132	9	10.1587301587302	-1.15873015873016
133	8	10.1587301587302	-2.15873015873016
134	14	10.1587301587302	3.84126984126984
135	16	13.4444444444444	2.55555555555556
136	12	13.4444444444444	-1.44444444444444
137	10	10.1587301587302	-0.158730158730158
138	10	10.1587301587302	-0.158730158730158
139	12	10.1587301587302	1.84126984126984
140	19	13.4444444444444	5.55555555555556
141	12	13.4444444444444	-1.44444444444444
142	15	13.4444444444444	1.55555555555556
143	15	10.1587301587302	4.84126984126984
144	15	13.4444444444444	1.55555555555556

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182596949qxj5p88eu2xf/202rb1292182688.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182596949qxj5p88eu2xf/202rb1292182688.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182596949qxj5p88eu2xf/302rb1292182688.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182596949qxj5p88eu2xf/302rb1292182688.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182596949qxj5p88eu2xf/4bt8e1292182688.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292182596949qxj5p88eu2xf/4bt8e1292182688.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par4 = no ;

Parameters (R input):

par1 = 2 ; par2 = none ; par3 = 2 ; 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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