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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: Wed, 15 Dec 2010 09:42:08 +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/15/t1292406033jajq8nuumx7oyfa.htm/, Retrieved Wed, 15 Dec 2010 10:40:33 +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/15/t1292406033jajq8nuumx7oyfa.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 «

10102 3.4 8863 8626 8366 12008 8463 4.8 10102 8863 8626 9169 9114 6.5 8463 10102 8863 8788 8563 8.5 9114 8463 10102 8417 8872 15.1 8563 9114 8463 8247 8301 15.7 8872 8563 9114 8197 8301 18.7 8301 8872 8563 8236 8278 19.2 8301 8301 8872 8253 7736 12.9 8278 8301 8301 7733 7973 14.4 7736 8278 8301 8366 8268 6.2 7973 7736 8278 8626 9476 3.3 8268 7973 7736 8863 11100 4.6 9476 8268 7973 10102 8962 7.1 11100 9476 8268 8463 9173 7.8 8962 11100 9476 9114 8738 9.9 9173 8962 11100 8563 8459 13.6 8738 9173 8962 8872 8078 17.1 8459 8738 9173 8301 8411 17.8 8078 8459 8738 8301 8291 18.6 8411 8078 8459 8278 7810 14.7 8291 8411 8078 7736 8616 10.5 7810 8291 8411 7973 8312 8.6 8616 7810 8291 8268 9692 4.4 8312 8616 7810 9476 9911 2.3 9692 8312 8616 11100 8915 2.8 9911 9692 8312 8962 9452 8.8 8915 9911 9692 9173 9112 10.7 9452 8915 9911 8738 8472 13.9 9112 9452 8915 8459 8230 19.3 8472 9112 9452 8078 8384 19.5 8230 8472 9112 8411 8625 20.4 8384 8230 8472 8291 8221 15.3 8625 8384 8230 7810 8649 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	0.8172
R-squared	0.6678
RMSE	433.9677

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	10102	9978.45454545455	123.545454545454
2	8463	8814.53846153846	-351.538461538461
3	9114	8814.53846153846	299.461538461539
4	8563	8364.33333333333	198.666666666666
5	8872	8364.33333333333	507.666666666666
6	8301	8364.33333333333	-63.333333333334
7	8301	8364.33333333333	-63.333333333334
8	8278	8364.33333333333	-86.333333333334
9	7736	7996.17391304348	-260.173913043478
10	7973	7996.17391304348	-23.1739130434780
11	8268	8814.53846153846	-546.538461538461
12	9476	8814.53846153846	661.461538461539
13	11100	9978.45454545455	1121.54545454545
14	8962	8364.33333333333	597.666666666666
15	9173	8814.53846153846	358.461538461539
16	8738	8814.53846153846	-76.538461538461
17	8459	8814.53846153846	-355.538461538461
18	8078	8364.33333333333	-286.333333333334
19	8411	7996.17391304348	414.826086956522
20	8291	8364.33333333333	-73.333333333334
21	7810	7996.17391304348	-186.173913043478
22	8616	7996.17391304348	619.826086956522
23	8312	8364.33333333333	-52.3333333333339
24	9692	9978.45454545455	-286.454545454546
25	9911	9978.45454545455	-67.454545454546
26	8915	8814.53846153846	100.461538461539
27	9452	8814.53846153846	637.461538461539
28	9112	8814.53846153846	297.461538461539
29	8472	8364.33333333333	107.666666666666
30	8230	8364.33333333333	-134.333333333334
31	8384	7996.17391304348	387.826086956522
32	8625	8364.33333333333	260.666666666666
33	8221	8364.33333333333	-143.333333333334
34	8649	8814.53846153846	-165.538461538461
35	8625	8364.33333333333	260.666666666666
36	10443	9978.45454545455	464.545454545454
37	10357	9978.45454545455	378.545454545454
38	8586	8814.53846153846	-228.538461538461
39	8892	8814.53846153846	77.461538461539
40	8329	8814.53846153846	-485.538461538461
41	8101	8364.33333333333	-263.333333333334
42	7922	7996.17391304348	-74.173913043478
43	8120	7996.17391304348	123.826086956522
44	7838	8814.53846153846	-976.538461538461
45	7735	7996.17391304348	-261.173913043478
46	8406	8814.53846153846	-408.538461538461
47	8209	8814.53846153846	-605.538461538461
48	9451	9978.45454545455	-527.454545454546
49	10041	9978.45454545455	62.545454545454
50	9411	8814.53846153846	596.461538461539
51	10405	8814.53846153846	1590.46153846154
52	8467	8364.33333333333	102.666666666666
53	8464	8364.33333333333	99.666666666666
54	8102	8364.33333333333	-262.333333333334
55	7627	7996.17391304348	-369.173913043478
56	7513	7996.17391304348	-483.173913043478
57	7510	7996.17391304348	-486.173913043478
58	8291	7996.17391304348	294.826086956522
59	8064	7996.17391304348	67.826086956522
60	9383	8814.53846153846	568.461538461539
61	9706	9978.45454545455	-272.454545454546
62	8579	8814.53846153846	-235.538461538461
63	9474	9978.45454545455	-504.454545454546
64	8318	8364.33333333333	-46.3333333333339
65	8213	8364.33333333333	-151.333333333334
66	8059	7996.17391304348	62.826086956522
67	9111	7996.17391304348	1114.82608695652
68	7708	8364.33333333333	-656.333333333334
69	7680	7996.17391304348	-316.173913043478
70	8014	7996.17391304348	17.8260869565220
71	8007	7996.17391304348	10.8260869565220
72	8718	8814.53846153846	-96.538461538461
73	9486	9978.45454545455	-492.454545454546
74	9113	8814.53846153846	298.461538461539
75	9025	8814.53846153846	210.461538461539
76	8476	8364.33333333333	111.666666666666
77	7952	8364.33333333333	-412.333333333334
78	7759	7996.17391304348	-237.173913043478
79	7835	8814.53846153846	-979.53846153846
80	7600	7996.17391304348	-396.173913043478
81	7651	7996.17391304348	-345.173913043478
82	8319	7996.17391304348	322.826086956522
83	8812	8364.33333333333	447.666666666666
84	8630	8814.53846153846	-184.538461538461

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292406033jajq8nuumx7oyfa/21ztm1292406121.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292406033jajq8nuumx7oyfa/21ztm1292406121.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292406033jajq8nuumx7oyfa/31ztm1292406121.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292406033jajq8nuumx7oyfa/31ztm1292406121.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292406033jajq8nuumx7oyfa/4t9bp1292406121.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292406033jajq8nuumx7oyfa/4t9bp1292406121.ps (open in new window)

Parameters (Session):

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

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