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*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, 21 Dec 2010 15:30:20 +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/21/t1292945279hl7gwg07b28jwcz.htm/, Retrieved Tue, 21 Dec 2010 16:27:59 +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/21/t1292945279hl7gwg07b28jwcz.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 «

4,031636 0,5215052 9,166456 1,303763 3,702076 0,4248284 7,970589 1,416094 3,056176 0,4250311 7,104091 1,052458 3,280707 0,4771938 6,621064 1,312283 2,984728 0,8280212 7,529215 1,309429 3,693712 0,6156186 8,170938 1,492409 3,226317 0,366627 8,15745 1,026556 2,190349 0,4308883 7,378962 1,005406 2,599515 0,2810287 7,921496 1,334886 3,080288 0,4646245 8,15674 1,393873 2,929672 0,2693951 8,856365 1,128092 2,922548 0,5779049 8,817177 1,122787 3,234943 0,5661151 8,734347 1,213104 2,983081 0,5077584 9,345927 1,253528 3,284389 0,7507175 8,99297 1,094796 3,806511 0,6808395 10,78512 0,9129438 3,784579 0,7661091 8,886867 1,19513 2,645654 0,4561473 8,818847 0,9274994 3,092081 0,4977496 8,823744 0,9653326 3,204859 0,4193273 9,165298 1,198078 3,107225 0,6095514 8,652657 0,966362 3,466909 0,457337 8,173054 0,9736851 2,984404 0,5705478 7,563416 0,9948013 3,218072 0,3478996 7,595809 0,8262616 2,82731 0,3874993 8,381467 0,6888877 3,182049 0,5824285 7,216432 0,7813066 2,236319 0,2391033 6,540178 0,60479 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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.7435
R-squared	0.5528
RMSE	0.4527

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	4.031636	3.16013398113208	0.871502018867924
2	3.702076	2.66054976190476	1.04152623809524
3	3.056176	2.66054976190476	0.395626238095238
4	3.280707	3.16013398113208	0.120573018867924
5	2.984728	3.16013398113208	-0.175405981132076
6	3.693712	3.16013398113208	0.533578018867924
7	3.226317	2.66054976190476	0.565767238095238
8	2.190349	2.66054976190476	-0.470200761904762
9	2.599515	1.81510301875	0.78441198125
10	3.080288	3.16013398113208	-0.0798459811320757
11	2.929672	1.81510301875	1.11456898125
12	2.922548	3.16013398113208	-0.237585981132076
13	3.234943	3.16013398113208	0.0748090188679242
14	2.983081	3.16013398113208	-0.177052981132076
15	3.284389	3.16013398113208	0.124255018867924
16	3.806511	3.16013398113208	0.646377018867924
17	3.784579	3.16013398113208	0.624445018867924
18	2.645654	2.66054976190476	-0.0148957619047621
19	3.092081	3.16013398113208	-0.0680529811320758
20	3.204859	2.66054976190476	0.544309238095238
21	3.107225	3.16013398113208	-0.0529089811320755
22	3.466909	3.16013398113208	0.306775018867924
23	2.984404	3.16013398113208	-0.175729981132076
24	3.218072	2.66054976190476	0.557522238095238
25	2.82731	2.66054976190476	0.166760238095238
26	3.182049	3.16013398113208	0.0219150188679245
27	2.236319	1.81510301875	0.42121598125
28	2.033218	1.81510301875	0.21811498125
29	1.644804	1.81510301875	-0.170299018750000
30	1.627971	2.66054976190476	-1.03257876190476
31	1.677559	2.66054976190476	-0.982990761904762
32	2.330828	2.66054976190476	-0.329721761904762
33	2.493615	2.66054976190476	-0.166934761904762
34	2.257172	2.66054976190476	-0.403377761904762
35	2.655517	1.81510301875	0.84041398125
36	2.298655	1.81510301875	0.48355198125
37	2.600402	2.66054976190476	-0.0601477619047621
38	3.04523	3.16013398113208	-0.114903981132076
39	2.790583	2.66054976190476	0.130033238095238
40	3.227052	2.66054976190476	0.566502238095238
41	2.967479	2.66054976190476	0.306929238095238
42	2.938817	2.66054976190476	0.278267238095238
43	3.277961	3.16013398113208	0.117827018867924
44	3.423985	3.16013398113208	0.263851018867924
45	3.072646	3.16013398113208	-0.0874879811320755
46	2.754253	3.16013398113208	-0.405880981132076
47	2.910431	3.16013398113208	-0.249702981132076
48	3.174369	3.16013398113208	0.0142350188679243
49	3.068387	3.16013398113208	-0.0917469811320757
50	3.089543	3.16013398113208	-0.0705909811320757
51	2.906654	3.16013398113208	-0.253479981132076
52	2.931161	3.16013398113208	-0.228972981132076
53	3.02566	3.16013398113208	-0.134473981132076
54	2.939551	3.16013398113208	-0.220582981132076
55	2.691019	2.66054976190476	0.0304692380952378
56	3.19812	3.16013398113208	0.0379860188679242
57	3.07639	3.16013398113208	-0.0837439811320757
58	2.863873	3.16013398113208	-0.296260981132076
59	3.013802	3.16013398113208	-0.146331981132076
60	3.053364	3.16013398113208	-0.106769981132075
61	2.864753	3.16013398113208	-0.295380981132076
62	3.057062	3.16013398113208	-0.103071981132075
63	2.959365	3.16013398113208	-0.200768981132076
64	3.252258	3.16013398113208	0.0921240188679242
65	3.602988	3.16013398113208	0.442854018867924
66	3.497704	3.16013398113208	0.337570018867924
67	3.296867	3.16013398113208	0.136733018867925
68	3.602417	3.16013398113208	0.442283018867924
69	3.3001	3.16013398113208	0.139966018867924
70	3.40193	3.16013398113208	0.241796018867924
71	3.502591	3.16013398113208	0.342457018867924
72	3.402348	3.16013398113208	0.242214018867924
73	3.498551	3.16013398113208	0.338417018867924
74	3.199823	3.16013398113208	0.0396890188679242
75	2.700064	3.16013398113208	-0.460069981132076
76	2.801034	3.16013398113208	-0.359099981132076
77	2.898628	3.16013398113208	-0.261505981132076
78	2.800854	3.16013398113208	-0.359279981132075
79	2.399942	2.66054976190476	-0.260607761904762
80	2.402724	1.81510301875	0.58762098125
81	2.202331	1.81510301875	0.38722798125
82	2.102594	3.16013398113208	-1.05753998113208
83	1.798293	2.66054976190476	-0.862256761904762
84	1.202484	1.81510301875	-0.61261901875
85	1.400201	1.81510301875	-0.41490201875
86	1.200832	1.81510301875	-0.61427101875
87	1.298083	1.81510301875	-0.51702001875
88	1.099742	1.81510301875	-0.71536101875
89	1.001377	1.81510301875	-0.81372601875
90	0.8361743	1.81510301875	-0.97892871875

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292945279hl7gwg07b28jwcz/21acz1292945412.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292945279hl7gwg07b28jwcz/21acz1292945412.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292945279hl7gwg07b28jwcz/31acz1292945412.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292945279hl7gwg07b28jwcz/31acz1292945412.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292945279hl7gwg07b28jwcz/4u1uk1292945412.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292945279hl7gwg07b28jwcz/4u1uk1292945412.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 4 ; par4 = no ;

Parameters (R input):

par1 = 1 ; 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

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