Home » date » 2010 » Dec » 22 »

Recursive Partitioning

*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: Wed, 22 Dec 2010 08:24:10 +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/22/t1293006152awjuu3ofrdyhwsc.htm/, Retrieved Wed, 22 Dec 2010 09:22:35 +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/22/t1293006152awjuu3ofrdyhwsc.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 «

97.06 21454 631923 130678 97.73 23899 654294 120877 98 24939 671833 137114 97.76 23580 586840 134406 97.48 24562 600969 120262 97.77 24696 625568 130846 97.96 23785 558110 120343 98.22 23812 630577 98881 98.51 21917 628654 115678 98.19 19713 603184 120796 98.37 19282 656255 94261 98.31 18788 600730 89151 98.6 21453 670326 119880 98.96 24482 678423 131468 99.11 27474 641502 155089 99.64 27264 625311 149581 100.02 27349 628177 122788 99.98 30632 589767 143900 100.32 29429 582471 112115 100.44 30084 636248 109600 100.51 26290 599885 117446 101 24379 621694 118456 100.88 23335 637406 101901 100.55 21346 595994 89940 100.82 21106 696308 129143 101.5 24514 674201 126102 102.15 28353 648861 143048 102.39 30805 649605 142258 102.54 31348 672392 131011 102.85 34556 598396 146471 103.47 33855 613177 114073 103.56 34787 638104 114642 103.69 32529 615632 118226 103.49 29998 634465 111338 103.47 29257 638686 108701 103.45 28155 604243 80512 103.48 30466 706669 146865 103.93 35704 67718 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	'Gwilym Jenkins' @ 72.249.127.135

Goodness of Fit
Correlation	0.9131
R-squared	0.8337
RMSE	3413.2969

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	21454	24214	-2760
2	23899	24214	-315
3	24939	24214	725
4	23580	24214	-634
5	24562	24214	348
6	24696	24214	482
7	23785	24214	-429
8	23812	24214	-402
9	21917	24214	-2297
10	19713	24214	-4501
11	19282	24214	-4932
12	18788	24214	-5426
13	21453	24214	-2761
14	24482	24214	268
15	27474	24214	3260
16	27264	24214	3050
17	27349	24214	3135
18	30632	24214	6418
19	29429	24214	5215
20	30084	24214	5870
21	26290	24214	2076
22	24379	24214	165
23	23335	24214	-879
24	21346	24214	-2868
25	21106	24214	-3108
26	24514	24214	300
27	28353	30754.7777777778	-2401.77777777778
28	30805	30754.7777777778	50.2222222222226
29	31348	30754.7777777778	593.222222222223
30	34556	30754.7777777778	3801.22222222222
31	33855	30754.7777777778	3100.22222222222
32	34787	38065.5483870968	-3278.54838709677
33	32529	38065.5483870968	-5536.54838709677
34	29998	30754.7777777778	-756.777777777777
35	29257	30754.7777777778	-1497.77777777778
36	28155	30754.7777777778	-2599.77777777778
37	30466	30754.7777777778	-288.777777777777
38	35704	38065.5483870968	-2361.54838709677
39	39327	38065.5483870968	1261.45161290323
40	39351	38065.5483870968	1285.45161290323
41	42234	44042.6666666667	-1808.66666666666
42	43630	44042.6666666667	-412.666666666664
43	43722	44042.6666666667	-320.666666666664
44	43121	44042.6666666667	-921.666666666664
45	37985	38065.5483870968	-80.5483870967728
46	37135	38065.5483870968	-930.548387096773
47	34646	38065.5483870968	-3419.54838709677
48	33026	38065.5483870968	-5039.54838709677
49	35087	38065.5483870968	-2978.54838709677
50	38846	38065.5483870968	780.451612903227
51	42013	38065.5483870968	3947.45161290323
52	43908	38065.5483870968	5842.45161290323
53	42868	44042.6666666667	-1174.66666666666
54	44423	44042.6666666667	380.333333333336
55	44167	44042.6666666667	124.333333333336
56	43636	44042.6666666667	-406.666666666664
57	44382	38065.5483870968	6316.45161290323
58	42142	44042.6666666667	-1900.66666666666
59	43452	44042.6666666667	-590.666666666664
60	36912	44042.6666666667	-7130.66666666666
61	42413	38065.5483870968	4347.45161290323
62	45344	38065.5483870968	7278.45161290323
63	44873	38065.5483870968	6807.45161290323
64	47510	44042.6666666667	3467.33333333334
65	49554	44042.6666666667	5511.33333333334
66	47369	44042.6666666667	3326.33333333334
67	45998	44042.6666666667	1955.33333333334
68	48140	38065.5483870968	10074.4516129032
69	48441	44042.6666666667	4398.33333333334
70	44928	44042.6666666667	885.333333333336
71	40454	38065.5483870968	2388.45161290323
72	38661	44042.6666666667	-5381.66666666666
73	37246	38065.5483870968	-819.548387096773
74	36843	38065.5483870968	-1222.54838709677
75	36424	38065.5483870968	-1641.54838709677
76	37594	38065.5483870968	-471.548387096773
77	38144	38065.5483870968	78.4516129032272
78	38737	38065.5483870968	671.451612903227
79	34560	38065.5483870968	-3505.54838709677
80	36080	38065.5483870968	-1985.54838709677
81	33508	38065.5483870968	-4557.54838709677
82	35462	38065.5483870968	-2603.54838709677
83	33374	38065.5483870968	-4691.54838709677
84	32110	38065.5483870968	-5955.54838709677

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293006152awjuu3ofrdyhwsc/2w1oe1293006243.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293006152awjuu3ofrdyhwsc/2w1oe1293006243.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293006152awjuu3ofrdyhwsc/3w1oe1293006243.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293006152awjuu3ofrdyhwsc/3w1oe1293006243.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293006152awjuu3ofrdyhwsc/4osnz1293006243.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293006152awjuu3ofrdyhwsc/4osnz1293006243.ps (open in new window)

Parameters (Session):

par1 = kendall ;

Parameters (R input):

par1 = 2 ; par2 = none ; par3 = 4 ; 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