Home » date » 2010 » Dec » 26 »

Paper Recursive Partitionin (boom)

*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: Sun, 26 Dec 2010 15:33:18 +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/t1293377503gsqqhwukmctkf0c.htm/, Retrieved Sun, 26 Dec 2010 16:31:43 +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/t1293377503gsqqhwukmctkf0c.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 «

61,2 2,08 83,9 10554,27 62 2,09 85,6 10532,54 65,1 2,07 87,5 10324,31 63,2 2,04 88,5 10695,25 66,3 2,35 91 10827,81 61,9 2,33 90,6 10872,48 62,1 2,37 91,2 10971,19 66,3 2,59 93,2 11145,65 72 2,62 90,1 11234,68 65,3 2,6 95 11333,88 67,6 2,83 95,4 10997,97 70,5 2,78 93,7 11036,89 74,2 3,01 93,9 11257,35 77,8 3,06 92,5 11533,59 78,5 3,33 89,2 11963,12 77,8 3,32 93,3 12185,15 81,4 3,6 93 12377,62 84,5 3,57 96,1 12512,89 88 3,57 96,7 12631,48 93,9 3,83 97,6 12268,53 98,9 3,84 102,6 12754,8 96,7 3,8 107,6 13407,75 98,9 4,07 103,5 13480,21 102,2 4,05 100,8 13673,28 105,4 4,272 94,5 13239,71 105,1 3,858 100,1 13557,69 116,6 4,067 97,4 13901,28 112 3,964 103 13200,58 108,8 3,782 100,2 13406,97 106,9 4,114 100,2 12538,12 109,5 4,009 99 12419,57 106,7 4,025 102,4 12193,88 118,9 4,082 99 12656,63 117,5 4,044 103,7 12812,48 113,7 3,916 103,4 12056,67 119,6 4,289 95,3 11322,38 120,6 4,296 93,6 11530,75 117,5 4,193 102,4 11114,08 120,3 3,48 110,5 9181,73 119,8 2,934 109,1 8614,55 108 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	0.9238
R-squared	0.8534
RMSE	9.2027

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	61.2	65.5066666666667	-4.30666666666666
2	62	65.5066666666667	-3.50666666666666
3	65.1	65.5066666666667	-0.406666666666666
4	63.2	65.5066666666667	-2.30666666666666
5	66.3	65.5066666666667	0.793333333333337
6	61.9	65.5066666666667	-3.60666666666666
7	62.1	65.5066666666667	-3.40666666666666
8	66.3	65.5066666666667	0.793333333333337
9	72	65.5066666666667	6.49333333333334
10	65.3	65.5066666666667	-0.206666666666663
11	67.6	65.5066666666667	2.09333333333333
12	70.5	65.5066666666667	4.99333333333334
13	74.2	92.3538461538461	-18.1538461538461
14	77.8	92.3538461538461	-14.5538461538462
15	78.5	92.3538461538461	-13.8538461538461
16	77.8	92.3538461538461	-14.5538461538462
17	81.4	92.3538461538461	-10.9538461538461
18	84.5	92.3538461538461	-7.85384615384615
19	88	92.3538461538461	-4.35384615384615
20	93.9	92.3538461538461	1.54615384615386
21	98.9	92.3538461538461	6.54615384615386
22	96.7	92.3538461538461	4.34615384615385
23	98.9	111.406666666667	-12.5066666666667
24	102.2	111.406666666667	-9.20666666666666
25	105.4	111.406666666667	-6.00666666666666
26	105.1	111.406666666667	-6.30666666666667
27	116.6	111.406666666667	5.19333333333333
28	112	111.406666666667	0.593333333333334
29	108.8	92.3538461538461	16.4461538461538
30	106.9	111.406666666667	-4.50666666666666
31	109.5	111.406666666667	-1.90666666666667
32	106.7	111.406666666667	-4.70666666666666
33	118.9	111.406666666667	7.49333333333334
34	117.5	111.406666666667	6.09333333333333
35	113.7	111.406666666667	2.29333333333334
36	119.6	111.406666666667	8.19333333333333
37	120.6	111.406666666667	9.19333333333333
38	117.5	111.406666666667	6.09333333333333
39	120.3	92.3538461538461	27.9461538461538
40	119.8	92.3538461538461	27.4461538461538
41	108	87.1375	20.8625
42	98.8	87.1375	11.6625
43	94.6	87.1375	7.46249999999999
44	84.6	87.1375	-2.53750000000001
45	84.4	87.1375	-2.73750000000000
46	79.1	87.1375	-8.03750000000001
47	73.3	87.1375	-13.8375
48	74.3	87.1375	-12.8375
49	67.8	65.5066666666667	2.29333333333334
50	64.8	65.5066666666667	-0.706666666666663
51	66.5	65.5066666666667	0.99333333333334
52	57.7	48.18	9.52
53	53.8	48.18	5.62
54	51.8	48.18	3.62
55	50.9	48.18	2.72
56	49	48.18	0.82
57	48.1	48.18	-0.0799999999999983
58	42.6	48.18	-5.58
59	40.9	48.18	-7.28
60	43.3	48.18	-4.88
61	43.7	48.18	-4.48

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293377503gsqqhwukmctkf0c/2d2wf1293377591.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293377503gsqqhwukmctkf0c/2d2wf1293377591.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293377503gsqqhwukmctkf0c/3d2wf1293377591.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293377503gsqqhwukmctkf0c/3d2wf1293377591.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293377503gsqqhwukmctkf0c/4ocw01293377591.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/26/t1293377503gsqqhwukmctkf0c/4ocw01293377591.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

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