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*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 11:18:41 +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/t129232544824fdagf8xustmgk.htm/, Retrieved Tue, 14 Dec 2010 12:17:35 +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/t129232544824fdagf8xustmgk.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 «

3 3 4 2 5 3 3 2 2 4 4 4 2 4 4 3 3 2 3 3 3 2 2 3 4 3 3 2 1 4 3 4 5 4 4 2 2 4 2 4 3 3 4 2 4 3 4 2 2 2 3 3 4 4 1 3 3 2 3 4 3 4 4 3 3 2 2 4 2 5 3 2 1 3 5 3 3 4 2 5 2 2 3 3 5 3 4 4 3 3 2 2 1 2 4 1 1 2 2 3 2 3 1 1 3 3 4 4 4 3 3 2 4 3 4 3 3 1 2 4 3 3 4 3 4 3 4 5 3 4 2 3 4 4 4 3 3 4 3 3 3 4 4 3 3 4 4 2 2 4 3 4 2 2 4 3 3 4 4 3 3 4 4 4 4 3 3 2 2 4 2 2 3 2 4 3 4 4 3 4 3 3 3 3 4 3 2 2 3 2 3 4 4 3 4 4 4 4 4 3 3 4 3 3 4 3 4 1 1 3 1 2 2 2 5 2 2 4 2 4 3 3 2 3 4 4 4 3 4 3 4 5 4 3 3 2 2 1 2 5 1 3 3 2 4 3 3 4 3 4 3 2 1 3 4 1 2 4 4 4 3 3 3 3 4 2 2 1 2 4 3 4 1 2 4 3 3 4 2 4 2 3 4 2 3 4 4 4 3 3 1 1 2 1 5 3 4 4 5 3 2 2 2 2 4 4 4 4 3 4 3 4 5 4 3 4 4 3 4 4 3 2 2 2 3 3 4 4 3 4 3 2 4 3 4 3 4 2 3 4 3 4 3 3 4 1 1 1 1 5 3 4 4 3 4 3 4 4 3 4 3 3 4 3 4 2 3 4 4 2 3 3 3 3 3 3 3 4 4 4 3 3 3 3 4 2 3 3 3 3 3 4 2 2 4 2 1 1 1 4 2 3 2 2 4 3 4 4 3 3 3 3 3 3 4 2 3 5 2 4 2 4 1 2 5 3 3 3 3 4 2 2 2 2 5 3 3 3 3 3 4 4 4 4 3 2 3 3 2 4 3 4 3 3 4 2 3 4 3 4 4 4 4 4 4 3 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	'Gwilym Jenkins' @ 72.249.127.135

Goodness of Fit
Correlation	0.6752
R-squared	0.4559
RMSE	0.5751

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	3	2.75862068965517	0.241379310344827
2	3	2.75862068965517	0.241379310344827
3	4	3.55	0.45
4	3	2.75862068965517	0.241379310344827
5	3	2.72727272727273	0.272727272727273
6	3	2.75862068965517	0.241379310344827
7	3	3.55	-0.55
8	2	1.81481481481481	0.185185185185185
9	3	2.75862068965517	0.241379310344827
10	3	3.175	-0.175
11	3	2.75862068965517	0.241379310344827
12	3	2.75862068965517	0.241379310344827
13	3	3.175	-0.175
14	2	1.81481481481481	0.185185185185185
15	3	2.72727272727273	0.272727272727273
16	3	2.75862068965517	0.241379310344827
17	2	2.72727272727273	-0.727272727272727
18	3	3.175	-0.175
19	2	1.81481481481481	0.185185185185185
20	1	1.81481481481481	-0.814814814814815
21	2	2.75862068965517	-0.758620689655173
22	3	3.55	-0.55
23	3	2.72727272727273	0.272727272727273
24	3	2.75862068965517	0.241379310344827
25	3	2.75862068965517	0.241379310344827
26	3	3.175	-0.175
27	2	2.75862068965517	-0.758620689655173
28	3	2.75862068965517	0.241379310344827
29	3	3.175	-0.175
30	4	3.175	0.825
31	3	3.175	-0.175
32	3	2.75862068965517	0.241379310344827
33	3	3.55	-0.55
34	3	2.75862068965517	0.241379310344827
35	2	1.81481481481481	0.185185185185185
36	3	3.175	-0.175
37	3	2.75862068965517	0.241379310344827
38	3	2.72727272727273	0.272727272727273
39	3	3.175	-0.175
40	4	3.55	0.45
41	3	3.175	-0.175
42	3	3.175	-0.175
43	1	1.81481481481481	-0.814814814814815
44	2	1.81481481481481	0.185185185185185
45	3	2.75862068965517	0.241379310344827
46	4	3.55	0.45
47	4	3.175	0.825
48	2	1.81481481481481	0.185185185185185
49	1	2.75862068965517	-1.75862068965517
50	3	2.75862068965517	0.241379310344827
51	3	2.72727272727273	0.272727272727273
52	1	2.72727272727273	-1.72727272727273
53	3	2.75862068965517	0.241379310344827
54	2	1.81481481481481	0.185185185185185
55	3	3.175	-0.175
56	3	2.75862068965517	0.241379310344827
57	2	2.75862068965517	-0.758620689655173
58	4	3.175	0.825
59	1	1.81481481481481	-0.814814814814815
60	3	3.55	-0.55
61	2	1.81481481481481	0.185185185185185
62	4	3.175	0.825
63	3	3.55	-0.55
64	4	3.55	0.45
65	3	1.81481481481481	1.18518518518519
66	3	3.175	-0.175
67	3	2.72727272727273	0.272727272727273
68	3	3.175	-0.175
69	3	3.175	-0.175
70	1	1.81481481481481	-0.814814814814815
71	3	3.175	-0.175
72	3	3.175	-0.175
73	3	2.75862068965517	0.241379310344827
74	2	2.75862068965517	-0.758620689655173
75	3	2.75862068965517	0.241379310344827
76	3	2.75862068965517	0.241379310344827
77	3	2.75862068965517	0.241379310344827
78	2	2.75862068965517	-0.758620689655173
79	3	3.175	-0.175
80	2	1.81481481481481	0.185185185185185
81	2	2.75862068965517	-0.758620689655173
82	3	3.175	-0.175
83	3	2.75862068965517	0.241379310344827
84	2	2.75862068965517	-0.758620689655173
85	2	3.175	-1.175
86	3	2.75862068965517	0.241379310344827
87	2	1.81481481481481	0.185185185185185
88	3	2.75862068965517	0.241379310344827
89	4	3.55	0.45
90	2	2.75862068965517	-0.758620689655173
91	3	3.175	-0.175
92	2	2.75862068965517	-0.758620689655173
93	4	3.55	0.45
94	3	3.175	-0.175
95	3	2.75862068965517	0.241379310344827
96	3	1.81481481481481	1.18518518518519
97	3	2.72727272727273	0.272727272727273
98	2	1.81481481481481	0.185185185185185
99	3	2.72727272727273	0.272727272727273
100	4	2.75862068965517	1.24137931034483
101	4	3.55	0.45
102	4	3.55	0.45
103	3	2.75862068965517	0.241379310344827
104	3	2.75862068965517	0.241379310344827
105	1	1.81481481481481	-0.814814814814815
106	4	2.75862068965517	1.24137931034483
107	1	2.75862068965517	-1.75862068965517
108	3	3.55	-0.55
109	2	1.81481481481481	0.185185185185185
110	2	1.81481481481481	0.185185185185185
111	3	2.75862068965517	0.241379310344827
112	3	2.75862068965517	0.241379310344827
113	2	2.75862068965517	-0.758620689655173
114	3	3.175	-0.175
115	3	3.55	-0.55
116	4	3.175	0.825
117	4	3.175	0.825
118	3	2.72727272727273	0.272727272727273
119	3	2.75862068965517	0.241379310344827
120	3	3.175	-0.175
121	3	2.75862068965517	0.241379310344827
122	3	3.175	-0.175
123	1	1.81481481481481	-0.814814814814815
124	2	3.55	-1.55
125	4	3.175	0.825
126	3	2.75862068965517	0.241379310344827
127	4	3.55	0.45
128	3	2.75862068965517	0.241379310344827
129	2	2.75862068965517	-0.758620689655173
130	1	1.81481481481481	-0.814814814814815
131	4	3.55	0.45
132	3	3.175	-0.175
133	3	1.81481481481481	1.18518518518519
134	3	2.75862068965517	0.241379310344827
135	4	3.175	0.825
136	3	2.75862068965517	0.241379310344827
137	3	3.175	-0.175
138	1	1.81481481481481	-0.814814814814815
139	4	3.175	0.825
140	2	2.75862068965517	-0.758620689655173
141	2	3.175	-1.175
142	3	2.75862068965517	0.241379310344827
143	3	3.175	-0.175
144	2	1.81481481481481	0.185185185185185
145	3	2.75862068965517	0.241379310344827
146	3	2.75862068965517	0.241379310344827
147	2	1.81481481481481	0.185185185185185
148	2	2.75862068965517	-0.758620689655173
149	3	3.175	-0.175
150	4	3.55	0.45
151	4	2.75862068965517	1.24137931034483
152	4	3.55	0.45
153	2	1.81481481481481	0.185185185185185
154	3	3.175	-0.175
155	3	3.175	-0.175
156	3	2.75862068965517	0.241379310344827

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t129232544824fdagf8xustmgk/2g5hb1292325513.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129232544824fdagf8xustmgk/2g5hb1292325513.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129232544824fdagf8xustmgk/3g5hb1292325513.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129232544824fdagf8xustmgk/3g5hb1292325513.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129232544824fdagf8xustmgk/4qfzw1292325513.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129232544824fdagf8xustmgk/4qfzw1292325513.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

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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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