Home » date » 2010 » Dec » 12 »

WS10 RP

*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 20:07:21 +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/t1292184340ykgkveojywlmh0u.htm/, Retrieved Sun, 12 Dec 2010 21:05:40 +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/t1292184340ykgkveojywlmh0u.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 «

6 73 62 66 4 58 54 54 5 68 41 82 4 62 49 61 4 65 49 65 6 81 72 77 6 73 78 66 4 64 58 66 4 68 58 66 6 51 23 48 4 68 39 57 6 61 63 80 5 69 46 60 4 73 58 70 6 61 39 85 3 62 44 59 5 63 49 72 6 69 57 70 4 47 76 74 6 66 63 70 2 58 18 51 7 63 40 70 5 69 59 71 2 59 62 72 4 59 70 50 4 63 65 69 6 65 56 73 6 65 45 66 5 71 57 73 6 60 50 58 6 81 40 78 4 67 58 83 6 66 49 76 6 62 49 77 6 63 27 79 2 73 51 71 4 55 75 79 5 59 65 60 3 64 47 73 7 63 49 70 5 64 65 42 3 73 61 74 8 54 46 68 8 76 69 83 5 74 55 62 6 63 78 79 3 73 58 61 5 67 34 86 4 68 67 64 5 66 45 75 5 62 68 59 6 71 49 82 5 63 19 61 6 75 72 69 6 77 59 60 4 62 46 59 8 74 56 81 6 67 45 65 4 56 53 60 6 60 67 60 5 58 73 45 5 65 46 75 6 49 70 84 6 61 38 77 6 66 54 64 6 64 46 54 6 65 46 72 6 46 45 56 7 65 47 67 4 81 25 81 4 72 63 73 3 65 46 67 6 74 69 72 5 59 43 69 5 69 49 71 3 58 39 77 5 71 65 63 4 79 54 49 3 68 50 74 7 66 42 76 4 62 45 65 4 69 50 65 5 63 55 69 6 62 38 71 2 61 40 68 2 65 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	1.3897

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	6	5.14383561643836	0.856164383561643
2	4	5.14383561643836	-1.14383561643836
3	5	5.14383561643836	-0.143835616438357
4	4	5.14383561643836	-1.14383561643836
5	4	5.14383561643836	-1.14383561643836
6	6	5.14383561643836	0.856164383561643
7	6	5.14383561643836	0.856164383561643
8	4	5.14383561643836	-1.14383561643836
9	4	5.14383561643836	-1.14383561643836
10	6	5.14383561643836	0.856164383561643
11	4	5.14383561643836	-1.14383561643836
12	6	5.14383561643836	0.856164383561643
13	5	5.14383561643836	-0.143835616438357
14	4	5.14383561643836	-1.14383561643836
15	6	5.14383561643836	0.856164383561643
16	3	5.14383561643836	-2.14383561643836
17	5	5.14383561643836	-0.143835616438357
18	6	5.14383561643836	0.856164383561643
19	4	5.14383561643836	-1.14383561643836
20	6	5.14383561643836	0.856164383561643
21	2	5.14383561643836	-3.14383561643836
22	7	5.14383561643836	1.85616438356164
23	5	5.14383561643836	-0.143835616438357
24	2	5.14383561643836	-3.14383561643836
25	4	5.14383561643836	-1.14383561643836
26	4	5.14383561643836	-1.14383561643836
27	6	5.14383561643836	0.856164383561643
28	6	5.14383561643836	0.856164383561643
29	5	5.14383561643836	-0.143835616438357
30	6	5.14383561643836	0.856164383561643
31	6	5.14383561643836	0.856164383561643
32	4	5.14383561643836	-1.14383561643836
33	6	5.14383561643836	0.856164383561643
34	6	5.14383561643836	0.856164383561643
35	6	5.14383561643836	0.856164383561643
36	2	5.14383561643836	-3.14383561643836
37	4	5.14383561643836	-1.14383561643836
38	5	5.14383561643836	-0.143835616438357
39	3	5.14383561643836	-2.14383561643836
40	7	5.14383561643836	1.85616438356164
41	5	5.14383561643836	-0.143835616438357
42	3	5.14383561643836	-2.14383561643836
43	8	5.14383561643836	2.85616438356164
44	8	5.14383561643836	2.85616438356164
45	5	5.14383561643836	-0.143835616438357
46	6	5.14383561643836	0.856164383561643
47	3	5.14383561643836	-2.14383561643836
48	5	5.14383561643836	-0.143835616438357
49	4	5.14383561643836	-1.14383561643836
50	5	5.14383561643836	-0.143835616438357
51	5	5.14383561643836	-0.143835616438357
52	6	5.14383561643836	0.856164383561643
53	5	5.14383561643836	-0.143835616438357
54	6	5.14383561643836	0.856164383561643
55	6	5.14383561643836	0.856164383561643
56	4	5.14383561643836	-1.14383561643836
57	8	5.14383561643836	2.85616438356164
58	6	5.14383561643836	0.856164383561643
59	4	5.14383561643836	-1.14383561643836
60	6	5.14383561643836	0.856164383561643
61	5	5.14383561643836	-0.143835616438357
62	5	5.14383561643836	-0.143835616438357
63	6	5.14383561643836	0.856164383561643
64	6	5.14383561643836	0.856164383561643
65	6	5.14383561643836	0.856164383561643
66	6	5.14383561643836	0.856164383561643
67	6	5.14383561643836	0.856164383561643
68	6	5.14383561643836	0.856164383561643
69	7	5.14383561643836	1.85616438356164
70	4	5.14383561643836	-1.14383561643836
71	4	5.14383561643836	-1.14383561643836
72	3	5.14383561643836	-2.14383561643836
73	6	5.14383561643836	0.856164383561643
74	5	5.14383561643836	-0.143835616438357
75	5	5.14383561643836	-0.143835616438357
76	3	5.14383561643836	-2.14383561643836
77	5	5.14383561643836	-0.143835616438357
78	4	5.14383561643836	-1.14383561643836
79	3	5.14383561643836	-2.14383561643836
80	7	5.14383561643836	1.85616438356164
81	4	5.14383561643836	-1.14383561643836
82	4	5.14383561643836	-1.14383561643836
83	5	5.14383561643836	-0.143835616438357
84	6	5.14383561643836	0.856164383561643
85	2	5.14383561643836	-3.14383561643836
86	2	5.14383561643836	-3.14383561643836
87	6	5.14383561643836	0.856164383561643
88	4	5.14383561643836	-1.14383561643836
89	5	5.14383561643836	-0.143835616438357
90	6	5.14383561643836	0.856164383561643
91	7	5.14383561643836	1.85616438356164
92	8	5.14383561643836	2.85616438356164
93	6	5.14383561643836	0.856164383561643
94	6	5.14383561643836	0.856164383561643
95	3	5.14383561643836	-2.14383561643836
96	7	5.14383561643836	1.85616438356164
97	3	5.14383561643836	-2.14383561643836
98	6	5.14383561643836	0.856164383561643
99	4	5.14383561643836	-1.14383561643836
100	4	5.14383561643836	-1.14383561643836
101	6	5.14383561643836	0.856164383561643
102	6	5.14383561643836	0.856164383561643
103	6	5.14383561643836	0.856164383561643
104	4	5.14383561643836	-1.14383561643836
105	7	5.14383561643836	1.85616438356164
106	5	5.14383561643836	-0.143835616438357
107	7	5.14383561643836	1.85616438356164
108	4	5.14383561643836	-1.14383561643836
109	6	5.14383561643836	0.856164383561643
110	6	5.14383561643836	0.856164383561643
111	6	5.14383561643836	0.856164383561643
112	5	5.14383561643836	-0.143835616438357
113	5	5.14383561643836	-0.143835616438357
114	6	5.14383561643836	0.856164383561643
115	7	5.14383561643836	1.85616438356164
116	4	5.14383561643836	-1.14383561643836
117	4	5.14383561643836	-1.14383561643836
118	8	5.14383561643836	2.85616438356164
119	6	5.14383561643836	0.856164383561643
120	3	5.14383561643836	-2.14383561643836
121	4	5.14383561643836	-1.14383561643836
122	5	5.14383561643836	-0.143835616438357
123	5	5.14383561643836	-0.143835616438357
124	6	5.14383561643836	0.856164383561643
125	8	5.14383561643836	2.85616438356164
126	2	5.14383561643836	-3.14383561643836
127	4	5.14383561643836	-1.14383561643836
128	7	5.14383561643836	1.85616438356164
129	5	5.14383561643836	-0.143835616438357
130	6	5.14383561643836	0.856164383561643
131	6	5.14383561643836	0.856164383561643
132	4	5.14383561643836	-1.14383561643836
133	5	5.14383561643836	-0.143835616438357
134	6	5.14383561643836	0.856164383561643
135	6	5.14383561643836	0.856164383561643
136	6	5.14383561643836	0.856164383561643
137	6	5.14383561643836	0.856164383561643
138	5	5.14383561643836	-0.143835616438357
139	5	5.14383561643836	-0.143835616438357
140	6	5.14383561643836	0.856164383561643
141	4	5.14383561643836	-1.14383561643836
142	6	5.14383561643836	0.856164383561643
143	3	5.14383561643836	-2.14383561643836
144	6	5.14383561643836	0.856164383561643
145	8	5.14383561643836	2.85616438356164
146	4	5.14383561643836	-1.14383561643836

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184340ykgkveojywlmh0u/2aqkd1292184433.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184340ykgkveojywlmh0u/2aqkd1292184433.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184340ykgkveojywlmh0u/3aqkd1292184433.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184340ykgkveojywlmh0u/3aqkd1292184433.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184340ykgkveojywlmh0u/4lzjg1292184433.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184340ykgkveojywlmh0u/4lzjg1292184433.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 1 ; 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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