Home » date » 2010 » Dec » 14 »

WS 10 - Recursive Partitionin

*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 08:57:51 +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/t1292316962omo77gjztkqrcrd.htm/, Retrieved Tue, 14 Dec 2010 09:56:02 +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/t1292316962omo77gjztkqrcrd.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 «

100.44 1576.23 29.29 710.45 100.88 1546.37 28.99 720 101.42 1545.05 28.91 720 99.97 1552.34 29.29 720 100.56 1594.3 30.96 754.78 99.51 1605.78 30.57 802.73 98.96 1673.21 30.59 845.24 100.85 1612.94 31.39 893.91 100.66 1566.34 31.28 931.43 100.22 1530.17 31.1 940 100.30 1582.54 31.7 947.73 100.73 1702.16 32.57 960 101.46 1701.93 32.49 996.96 101.35 1811.15 32.46 1000 101.14 1924.2 32.3 1000 101.68 2034.25 32.97 1000 101.47 2011.13 32.9 1013.04 100.59 2013.04 32.93 1095.24 101.18 2151.67 33.72 1159.09 100.87 1902.09 33.33 1200 99.79 1944.01 33.44 1200 100.74 1916.67 33.89 1282.61 99.34 1967.31 34.34 1513.64 100.07 2119.88 33.56 1669.05 103.68 2216.38 32.67 1700 103.52 2522.83 32.57 1700 104.68 2647.64 33.23 1700 103.75 2631.23 32.85 1665.91 103.70 2693.41 32.61 1650 102.98 3021.76 32.57 1650 106.30 2953.67 32.98 1619.57 107.21 2796.8 31.33 1599.05 106.83 2672.05 29.8 1572.73 105.60 2251.23 28.06 1470 104.30 2046.08 25.47 1268 104.43 2420.04 24.65 1217.39 104.36 2608.89 23.94 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	4 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.6612
R-squared	0.4372
RMSE	3.2822

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	100.44	100.852307692308	-0.412307692307692
2	100.88	100.852307692308	0.0276923076923055
3	101.42	100.852307692308	0.567692307692312
4	99.97	100.852307692308	-0.882307692307691
5	100.56	100.852307692308	-0.292307692307688
6	99.51	100.852307692308	-1.34230769230768
7	98.96	100.852307692308	-1.89230769230770
8	100.85	100.852307692308	-0.00230769230769567
9	100.66	100.852307692308	-0.192307692307693
10	100.22	100.852307692308	-0.632307692307691
11	100.3	100.852307692308	-0.552307692307693
12	100.73	100.852307692308	-0.122307692307686
13	101.46	100.852307692308	0.607692307692304
14	101.35	100.852307692308	0.497692307692304
15	101.14	100.852307692308	0.287692307692311
16	101.68	100.852307692308	0.827692307692317
17	101.47	100.852307692308	0.617692307692309
18	100.59	100.852307692308	-0.262307692307687
19	101.18	100.852307692308	0.327692307692317
20	100.87	100.852307692308	0.0176923076923146
21	99.79	100.852307692308	-1.06230769230768
22	100.74	100.852307692308	-0.112307692307695
23	99.34	100.852307692308	-1.51230769230769
24	100.07	100.852307692308	-0.782307692307697
25	103.68	100.852307692308	2.82769230769232
26	103.52	106.66	-3.14
27	104.68	106.66	-1.97999999999999
28	103.75	106.66	-2.91000000000000
29	103.7	106.66	-2.95999999999999
30	102.98	106.66	-3.67999999999999
31	106.3	106.66	-0.359999999999999
32	107.21	106.66	0.549999999999997
33	106.83	106.66	0.170000000000002
34	105.6	106.66	-1.06000000000000
35	104.3	100.852307692308	3.44769230769231
36	104.43	106.66	-2.22999999999999
37	104.36	106.66	-2.30000000000000
38	106.21	106.66	-0.450000000000003
39	107.34	106.66	0.680000000000007
40	106.92	106.66	0.260000000000005
41	104.8	106.66	-1.86
42	103.85	106.66	-2.81
43	103.39	106.66	-3.27000000000000
44	103.38	106.66	-3.28
45	103.93	106.66	-2.72999999999999
46	104.41	106.66	-2.25
47	104.47	106.66	-2.19000000000000
48	103.84	106.66	-2.81999999999999
49	103.65	106.66	-3.00999999999999
50	103.17	106.66	-3.48999999999999
51	103.4	106.66	-3.25999999999999
52	112.72	106.66	6.06
53	114.77	106.66	8.11
54	116.18	106.66	9.52000000000001
55	116.93	106.66	10.27
56	115.19	106.66	8.53
57	114.55	106.66	7.89

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292316962omo77gjztkqrcrd/2wctm1292317064.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292316962omo77gjztkqrcrd/2wctm1292317064.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292316962omo77gjztkqrcrd/3wctm1292317064.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292316962omo77gjztkqrcrd/3wctm1292317064.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292316962omo77gjztkqrcrd/4o3s71292317064.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292316962omo77gjztkqrcrd/4o3s71292317064.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 0 ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = 0 ; 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.

FreeStatistics.org is powered by