Home » date » 2010 » Dec » 24 »

workshop 10 - recursive partitioning 2 (jonas poels)

*Unverified author*

R Software Module: /rwasp_regression_trees1.wasp (opens new window with default values)

Title produced by software: Recursive Partitioning (Regression Trees)

Date of computation: Fri, 24 Dec 2010 19:19:20 +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/24/t1293218277csdvkm9rjm0e9iz.htm/, Retrieved Fri, 24 Dec 2010 20:18:00 +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/24/t1293218277csdvkm9rjm0e9iz.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 «

1 162556 162556 1081 1081 213118 213118 230380558 6282929 1 29790 29790 309 309 81767 81767 25266003 4324047 1 87550 87550 458 458 153198 153198 70164684 4108272 0 84738 0 588 0 -26007 0 -15292116 -1212617 1 54660 54660 299 299 126942 126942 37955658 1485329 1 42634 42634 156 156 157214 157214 24525384 1779876 0 40949 0 481 0 129352 0 62218312 1367203 1 42312 42312 323 323 234817 234817 75845891 2519076 1 37704 37704 452 452 60448 60448 27322496 912684 1 16275 16275 109 109 47818 47818 5212162 1443586 0 25830 0 115 0 245546 0 28237790 1220017 0 12679 0 110 0 48020 0 5282200 984885 1 18014 18014 239 239 -1710 -1710 -408690 1457425 0 43556 0 247 0 32648 0 8064056 -572920 1 24524 24524 497 497 95350 95350 47388950 929144 0 6532 0 103 0 151352 0 15589256 1151176 0 7123 0 109 0 288170 0 31410530 790090 1 20813 20813 502 502 114337 114337 57397174 774497 1 37597 37597 248 248 37884 37884 9395232 990576 0 17821 0 373 0 122844 0 45820812 454195 1 12988 12988 119 119 82340 82340 9798460 876607 1 2 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
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Goodness of Fit
Correlation	0.7693
R-squared	0.5918
RMSE	13879.611

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	162556	66430.4285714286	96125.5714285714
2	29790	26096.4285714286	3693.57142857143
3	87550	66430.4285714286	21119.5714285714
4	84738	26096.4285714286	58641.5714285714
5	54660	66430.4285714286	-11770.4285714286
6	42634	66430.4285714286	-23796.4285714286
7	40949	26096.4285714286	14852.5714285714
8	42312	66430.4285714286	-24118.4285714286
9	37704	66430.4285714286	-28726.4285714286
10	16275	12787.1	3487.9
11	25830	12787.1	13042.9
12	12679	12787.1	-108.100000000000
13	18014	26096.4285714286	-8082.42857142857
14	43556	26096.4285714286	17459.5714285714
15	24524	26096.4285714286	-1572.42857142857
16	6532	12787.1	-6255.1
17	7123	12787.1	-5664.1
18	20813	26096.4285714286	-5283.42857142857
19	37597	66430.4285714286	-28833.4285714286
20	17821	26096.4285714286	-8275.42857142857
21	12988	12787.1	200.900000000000
22	22330	12787.1	9542.9
23	13326	12787.1	538.9
24	16189	26096.4285714286	-9907.42857142857
25	7146	4006.52173913043	3139.47826086957
26	15824	4006.52173913043	11817.4782608696
27	26088	26096.4285714286	-8.42857142857247
28	11326	4006.52173913043	7319.47826086957
29	8568	12787.1	-4219.1
30	14416	26096.4285714286	-11680.4285714286
31	3369	4006.52173913043	-637.521739130435
32	11819	4006.52173913043	7812.47826086957
33	6620	4006.52173913043	2613.47826086957
34	4519	4006.52173913043	512.478260869565
35	2220	12787.1	-10567.1
36	18562	4006.52173913043	14555.4782608696
37	10327	4006.52173913043	6320.47826086957
38	5336	4006.52173913043	1329.47826086957
39	2365	4006.52173913043	-1641.52173913043
40	4069	4006.52173913043	62.478260869565
41	7710	26096.4285714286	-18386.4285714286
42	13718	4006.52173913043	9711.47826086956
43	4525	4006.52173913043	518.478260869565
44	6869	4006.52173913043	2862.47826086957
45	4628	4006.52173913043	621.478260869565
46	3653	4006.52173913043	-353.521739130435
47	1265	4006.52173913043	-2741.52173913043
48	7489	26096.4285714286	-18607.4285714286
49	4901	4006.52173913043	894.478260869565
50	2284	4006.52173913043	-1722.52173913043
51	3160	4006.52173913043	-846.521739130435
52	4150	4006.52173913043	143.478260869565
53	7285	4006.52173913043	3278.47826086957
54	1134	4006.52173913043	-2872.52173913043
55	4658	4006.52173913043	651.478260869565
56	2384	4006.52173913043	-1622.52173913043
57	3748	4006.52173913043	-258.521739130435
58	5371	4006.52173913043	1364.47826086957
59	1285	4006.52173913043	-2721.52173913043
60	9327	4006.52173913043	5320.47826086957
61	5565	4006.52173913043	1558.47826086957
62	1528	4006.52173913043	-2478.52173913043
63	3122	4006.52173913043	-884.521739130435
64	7317	4006.52173913043	3310.47826086957
65	2675	4006.52173913043	-1331.52173913043
66	13253	26096.4285714286	-12843.4285714286
67	880	4006.52173913043	-3126.52173913043
68	2053	4006.52173913043	-1953.52173913043
69	1424	4006.52173913043	-2582.52173913043
70	4036	4006.52173913043	29.478260869565
71	3045	4006.52173913043	-961.521739130435
72	5119	4006.52173913043	1112.47826086957
73	1431	4006.52173913043	-2575.52173913043
74	554	4006.52173913043	-3452.52173913043
75	1975	4006.52173913043	-2031.52173913043
76	1286	4006.52173913043	-2720.52173913043
77	1012	4006.52173913043	-2994.52173913043
78	810	4006.52173913043	-3196.52173913043
79	1280	4006.52173913043	-2726.52173913043
80	666	4006.52173913043	-3340.52173913043
81	1380	4006.52173913043	-2626.52173913043
82	4608	4006.52173913043	601.478260869565
83	876	4006.52173913043	-3130.52173913043
84	814	4006.52173913043	-3192.52173913043
85	514	4006.52173913043	-3492.52173913043
86	5692	4006.52173913043	1685.47826086957
87	3642	4006.52173913043	-364.521739130435
88	540	4006.52173913043	-3466.52173913043
89	2099	4006.52173913043	-1907.52173913043
90	567	4006.52173913043	-3439.52173913043
91	2001	4006.52173913043	-2005.52173913043
92	2949	4006.52173913043	-1057.52173913043
93	2253	4006.52173913043	-1753.52173913043
94	6533	4006.52173913043	2526.47826086957
95	1889	4006.52173913043	-2117.52173913043
96	3055	4006.52173913043	-951.521739130435
97	272	4006.52173913043	-3734.52173913043
98	1414	4006.52173913043	-2592.52173913043
99	2564	4006.52173913043	-1442.52173913043
100	1383	4006.52173913043	-2623.52173913043

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293218277csdvkm9rjm0e9iz/2c4cy1293218353.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293218277csdvkm9rjm0e9iz/2c4cy1293218353.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293218277csdvkm9rjm0e9iz/3c4cy1293218353.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293218277csdvkm9rjm0e9iz/3c4cy1293218353.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293218277csdvkm9rjm0e9iz/44wc11293218353.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293218277csdvkm9rjm0e9iz/44wc11293218353.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = none ; par3 = 2 ; par4 = yes ;

Parameters (R input):

par1 = 2 ; par2 = none ; par3 = 2 ; par4 = yes ;

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