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recursive partitioning 1 - werkloosheid

*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: Sun, 26 Dec 2010 15:52:31 +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/26/t1293378636j81i6riahz8y61l.htm/, Retrieved Sun, 26 Dec 2010 16:50:39 +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/26/t1293378636j81i6riahz8y61l.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 «

7361 493 797 48 1.5 105.0 508643 7391 514 840 49 1.6 104.0 527568 7420 522 988 59 1.8 109.8 520008 7406 490 819 56 1.5 98.6 498484 7439 484 831 47 1.3 93.5 523917 7512 506 904 56 1.6 98.2 553522 7579 501 814 50 1.6 88.0 558901 7520 462 798 54 1.8 85.3 548933 7453 465 828 79 1.8 96.8 567013 7462 454 789 50 1.6 98.8 551085 7472 464 930 54 1.8 110.3 588245 7443 427 744 56 2 111.6 605010 7439 460 832 50 1.3 111.2 631572 7460 473 826 46 1.1 106.9 639180 7482 465 907 47 1 117.6 653847 7442 422 776 43 1.2 97.0 657073 7454 415 835 52 1.2 97.3 626291 7536 413 715 48 1.3 98.4 625616 7616 420 729 36 1.3 87.6 633352 7548 363 733 41 1.4 87.4 672820 7507 376 736 34 1.1 94.7 691369 7515 380 712 37 0.9 101.5 702595 7549 384 711 37 1 110.4 692241 7540 346 667 34 1.1 108.4 718722 7525 389 799 55 1.4 109.7 732297 7575 407 661 37 1.5 105.2 721798 7621 393 692 27 1.8 111.1 766192 7589 346 649 38 1.8 96.2 788456 7606 348 729 43 1.8 97.3 806132 7722 353 622 26 1.7 98.9 813944 7788 364 671 32 1.5 91.7 7 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	5 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	0.8996
R-squared	0.8093
RMSE	30.1733

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	493	471.058823529412	21.9411764705882
2	514	471.058823529412	42.9411764705882
3	522	471.058823529412	50.9411764705882
4	490	471.058823529412	18.9411764705882
5	484	471.058823529412	12.9411764705882
6	506	471.058823529412	34.9411764705882
7	501	471.058823529412	29.9411764705882
8	462	471.058823529412	-9.05882352941177
9	465	471.058823529412	-6.05882352941177
10	454	471.058823529412	-17.0588235294118
11	464	471.058823529412	-7.05882352941177
12	427	471.058823529412	-44.0588235294118
13	460	471.058823529412	-11.0588235294118
14	473	471.058823529412	1.94117647058823
15	465	471.058823529412	-6.05882352941177
16	422	383.375	38.625
17	415	471.058823529412	-56.0588235294118
18	413	471.058823529412	-58.0588235294118
19	420	369.055555555556	50.9444444444445
20	363	383.375	-20.375
21	376	383.375	-7.375
22	380	383.375	-3.375
23	384	383.375	0.625
24	346	383.375	-37.375
25	389	383.375	5.625
26	407	383.375	23.625
27	393	369.055555555556	23.9444444444445
28	346	369.055555555556	-23.0555555555555
29	348	369.055555555556	-21.0555555555555
30	353	369.055555555556	-16.0555555555555
31	364	369.055555555556	-5.05555555555554
32	305	369.055555555556	-64.0555555555555
33	307	369.055555555556	-62.0555555555555
34	312	369.055555555556	-57.0555555555555
35	312	307.647058823529	4.35294117647061
36	286	307.647058823529	-21.6470588235294
37	324	307.647058823529	16.3529411764706
38	336	307.647058823529	28.3529411764706
39	327	307.647058823529	19.3529411764706
40	302	307.647058823529	-5.64705882352939
41	299	307.647058823529	-8.6470588235294
42	311	307.647058823529	3.35294117647061
43	315	307.647058823529	7.35294117647061
44	264	307.647058823529	-43.6470588235294
45	278	307.647058823529	-29.6470588235294
46	278	307.647058823529	-29.6470588235294
47	287	307.647058823529	-20.6470588235294
48	279	307.647058823529	-28.6470588235294
49	324	307.647058823529	16.3529411764706
50	354	307.647058823529	46.3529411764706
51	354	307.647058823529	46.3529411764706
52	360	369.055555555556	-9.05555555555554
53	363	369.055555555556	-6.05555555555554
54	385	369.055555555556	15.9444444444445
55	412	369.055555555556	42.9444444444445
56	370	369.055555555556	0.944444444444457
57	389	369.055555555556	19.9444444444445
58	395	369.055555555556	25.9444444444445
59	417	369.055555555556	47.9444444444445
60	404	369.055555555556	34.9444444444445

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293378636j81i6riahz8y61l/2cf351293378745.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293378636j81i6riahz8y61l/2cf351293378745.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293378636j81i6riahz8y61l/3cf351293378745.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293378636j81i6riahz8y61l/3cf351293378745.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293378636j81i6riahz8y61l/4462p1293378745.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293378636j81i6riahz8y61l/4462p1293378745.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 2 ; 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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