Home » date » 2010 » Dec » 22 »

Regression tree

*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 08:15:55 +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/t129300569926p3lf26r0znr7q.htm/, Retrieved Wed, 22 Dec 2010 09:14:59 +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/t129300569926p3lf26r0znr7q.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 «

21454 -11,5 0,012095933 8,02 8,3 20780 1 0 0 0 0 0 0 0 0 0 0 0 1 23899 -11 0,017384968 8,03 8,2 19815 0 1 0 0 0 0 0 0 0 0 0 0 2 24939 -14,9 0,017547503 8,45 8 19761 0 0 1 0 0 0 0 0 0 0 0 0 3 23580 -16,2 0,014844804 7,74 7,9 21454 0 0 0 1 0 0 0 0 0 0 0 0 4 24562 -14,4 0,010364842 7,26 7,6 23899 0 0 0 0 1 0 0 0 0 0 0 0 5 24696 -17,3 0,016214531 7,9 7,6 24939 0 0 0 0 0 1 0 0 0 0 0 0 6 23785 -15,7 0,014814047 7,34 8,3 23580 0 0 0 0 0 0 1 0 0 0 0 0 7 23812 -12,6 0,017823834 6,91 8,4 24562 0 0 0 0 0 0 0 1 0 0 0 0 8 21917 -9,4 0,017980779 7,22 8,4 24696 0 0 0 0 0 0 0 0 1 0 0 0 9 19713 -8,1 0,015828678 7,47 8,4 23785 0 0 0 0 0 0 0 0 0 1 0 0 10 19282 -5,4 0,018533858 7,16 8,4 23812 0 0 0 0 0 0 0 0 0 0 1 0 11 18788 -4,6 0,017385905 8,09 8,6 21917 0 0 0 0 0 0 0 0 0 0 0 1 12 21453 -4,9 0,015866474 7,91 8,9 19713 1 0 0 0 0 0 0 0 0 0 0 0 13 24482 -4 0,012585695 7,74 8,8 19282 0 1 0 0 0 0 0 0 0 0 0 0 14 27474 -3,1 0,011326531 8,01 8,3 18788 0 0 1 0 0 0 0 0 0 0 0 0 15 27264 -1,3 0,019230769 7,56 7,5 21453 0 0 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	8 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.903
R-squared	0.8155
RMSE	3535.6063

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	21454	24367.2962962963	-2913.29629629630
2	23899	24367.2962962963	-468.296296296296
3	24939	24367.2962962963	571.703703703704
4	23580	24367.2962962963	-787.296296296296
5	24562	24367.2962962963	194.703703703704
6	24696	24367.2962962963	328.703703703704
7	23785	24367.2962962963	-582.296296296296
8	23812	24367.2962962963	-555.296296296296
9	21917	24367.2962962963	-2450.29629629630
10	19713	24367.2962962963	-4654.2962962963
11	19282	24367.2962962963	-5085.2962962963
12	18788	24367.2962962963	-5579.2962962963
13	21453	24367.2962962963	-2914.29629629630
14	24482	24367.2962962963	114.703703703704
15	27474	24367.2962962963	3106.70370370370
16	27264	24367.2962962963	2896.70370370370
17	27349	24367.2962962963	2981.70370370370
18	30632	24367.2962962963	6264.7037037037
19	29429	24367.2962962963	5061.7037037037
20	30084	24367.2962962963	5716.7037037037
21	26290	24367.2962962963	1922.70370370370
22	24379	24367.2962962963	11.7037037037044
23	23335	24367.2962962963	-1032.29629629630
24	21346	24367.2962962963	-3021.29629629630
25	21106	24367.2962962963	-3261.29629629630
26	24514	24367.2962962963	146.703703703704
27	28353	24367.2962962963	3985.70370370370
28	30805	34508.1428571429	-3703.14285714286
29	31348	34508.1428571429	-3160.14285714286
30	34556	34508.1428571429	47.8571428571449
31	33855	36645.0333333333	-2790.03333333333
32	34787	36645.0333333333	-1858.03333333333
33	32529	36645.0333333333	-4116.03333333333
34	29998	36645.0333333333	-6647.03333333333
35	29257	36645.0333333333	-7388.03333333333
36	28155	36645.0333333333	-8490.03333333333
37	30466	34508.1428571429	-4042.14285714286
38	35704	34508.1428571429	1195.85714285714
39	39327	34508.1428571429	4818.85714285714
40	39351	34508.1428571429	4842.85714285714
41	42234	36645.0333333333	5588.96666666667
42	43630	41307.1818181818	2322.81818181818
43	43722	41307.1818181818	2414.81818181818
44	43121	41307.1818181818	1813.81818181818
45	37985	41307.1818181818	-3322.18181818182
46	37135	41307.1818181818	-4172.18181818182
47	34646	41307.1818181818	-6661.18181818182
48	33026	36645.0333333333	-3619.03333333333
49	35087	36645.0333333333	-1558.03333333333
50	38846	36645.0333333333	2200.96666666667
51	42013	36645.0333333333	5367.96666666667
52	43908	36645.0333333333	7262.96666666667
53	42868	41307.1818181818	1560.81818181818
54	44423	41307.1818181818	3115.81818181818
55	44167	41307.1818181818	2859.81818181818
56	43636	41307.1818181818	2328.81818181818
57	44382	41307.1818181818	3074.81818181818
58	42142	41307.1818181818	834.818181818184
59	43452	41307.1818181818	2144.81818181818
60	36912	41307.1818181818	-4395.18181818182
61	42413	41307.1818181818	1105.81818181818
62	45344	47160.5	-1816.5
63	44873	36645.0333333333	8227.96666666667
64	47510	47160.5	349.5
65	49554	47160.5	2393.5
66	47369	47160.5	208.5
67	45998	47160.5	-1162.5
68	48140	47160.5	979.5
69	48441	47160.5	1280.5
70	44928	47160.5	-2232.5
71	40454	41307.1818181818	-853.181818181816
72	38661	41307.1818181818	-2646.18181818182
73	37246	41307.1818181818	-4061.18181818182
74	36843	41307.1818181818	-4464.18181818182
75	36424	36645.0333333333	-221.033333333333
76	37594	36645.0333333333	948.966666666667
77	38144	36645.0333333333	1498.96666666667
78	38737	36645.0333333333	2091.96666666667
79	34560	36645.0333333333	-2085.03333333333
80	36080	36645.0333333333	-565.033333333333
81	33508	36645.0333333333	-3137.03333333333
82	35462	36645.0333333333	-1183.03333333333
83	33374	36645.0333333333	-3271.03333333333
84	32110	36645.0333333333	-4535.03333333333
85	35533	36645.0333333333	-1112.03333333333
86	35532	36645.0333333333	-1113.03333333333
87	37903	36645.0333333333	1257.96666666667
88	36763	36645.0333333333	117.966666666667
89	40399	36645.0333333333	3753.96666666667
90	44164	36645.0333333333	7518.96666666667
91	44496	36645.0333333333	7850.96666666667
92	43110	41307.1818181818	1802.81818181818
93	43880	41307.1818181818	2572.81818181818
94	43930	41307.1818181818	2622.81818181818

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/22/t129300569926p3lf26r0znr7q/2q7451293005745.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t129300569926p3lf26r0znr7q/2q7451293005745.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t129300569926p3lf26r0znr7q/3q7451293005745.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t129300569926p3lf26r0znr7q/3q7451293005745.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t129300569926p3lf26r0znr7q/4ig381293005745.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t129300569926p3lf26r0znr7q/4ig381293005745.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.

FreeStatistics.org is powered by