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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 12:17:34 +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/t1292933743ew843xb37gfd7lk.htm/, Retrieved Tue, 21 Dec 2010 13:15:44 +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/t1292933743ew843xb37gfd7lk.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 «

112,3 112,9 88,7 105,1 117,3 130,5 94,6 114,9 111,1 137,9 98,7 106,4 102,2 115 84,2 104,5 104,3 116,8 87,7 121,6 122,9 140,9 103,3 141,4 107,6 120,7 88,2 99 121,3 134,2 93,4 126,7 131,5 147,3 106,3 134,1 89 112,4 73,1 81,3 104,4 107,1 78,6 88,6 128,9 128,4 101,6 132,7 135,9 137,7 101,4 132,9 133,3 135 98,5 134,4 121,3 151 99 103,7 120,5 137,4 89,5 119,7 120,4 132,4 83,5 115 137,9 161,3 97,4 132,9 126,1 139,8 87,8 108,5 133,2 146 90,4 113,9 151,1 166,5 101,6 142 105 143,3 80 97,7 119 121 81,7 92,2 140,4 152,6 96,4 128,8 156,6 154,4 110,2 134,9 137,1 154,6 101,1 128,2 122,7 158 89,3 114,8 125,8 142,6 90 117,9 139,3 153,4 95,4 119,1 134,9 163,4 100,3 120,7 149,2 167,3 99,5 129,1 132,3 154,8 93,9 117,6 149 165,7 100,6 129,2 117,2 144,7 84,7 100 119,6 120,9 81,6 87 152 152,8 109 128 149,4 160,2 99 127,7 127,3 128,3 81,1 93,4 114,1 150,5 81,8 84,1 102,1 117 66,5 71,7 107,7 116 66,4 83,2 104,4 133,3 86,3 89,1 102,1 116,4 73,6 79,6 96 104 71,5 62,8 109,3 126,6 87,2 95,1 90 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	4 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Goodness of Fit
Correlation	0.9229
R-squared	0.8518
RMSE	8.6348

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	105.1	108.914285714286	-3.81428571428572
2	114.9	108.914285714286	5.9857142857143
3	106.4	108.914285714286	-2.51428571428571
4	104.5	92.8466666666667	11.6533333333333
5	121.6	108.914285714286	12.6857142857143
6	141.4	131.946153846154	9.45384615384617
7	99	108.914285714286	-9.9142857142857
8	126.7	108.914285714286	17.7857142857143
9	134.1	131.946153846154	2.15384615384616
10	81.3	71.5181818181818	9.78181818181818
11	88.6	92.8466666666667	-4.24666666666667
12	132.7	131.946153846154	0.753846153846155
13	132.9	131.946153846154	0.953846153846172
14	134.4	131.946153846154	2.45384615384617
15	103.7	108.914285714286	-5.21428571428571
16	119.7	108.914285714286	10.7857142857143
17	115	92.8466666666667	22.1533333333333
18	132.9	119.814285714286	13.0857142857143
19	108.5	119.814285714286	-11.3142857142857
20	113.9	119.814285714286	-5.91428571428571
21	142	131.946153846154	10.0538461538462
22	97.7	92.8466666666667	4.85333333333334
23	92.2	92.8466666666667	-0.646666666666661
24	128.8	119.814285714286	8.9857142857143
25	134.9	131.946153846154	2.95384615384617
26	128.2	131.946153846154	-3.74615384615385
27	114.8	108.914285714286	5.88571428571429
28	117.9	119.814285714286	-1.91428571428571
29	119.1	119.814285714286	-0.714285714285722
30	120.7	131.946153846154	-11.2461538461538
31	129.1	131.946153846154	-2.84615384615384
32	117.6	119.814285714286	-2.21428571428572
33	129.2	131.946153846154	-2.74615384615385
34	100	92.8466666666667	7.15333333333334
35	87	92.8466666666667	-5.84666666666666
36	128	131.946153846154	-3.94615384615383
37	127.7	131.946153846154	-4.24615384615383
38	93.4	92.8466666666667	0.553333333333342
39	84.1	92.8466666666667	-8.74666666666667
40	71.7	71.5181818181818	0.181818181818187
41	83.2	92.8466666666667	-9.64666666666666
42	89.1	92.8466666666667	-3.74666666666667
43	79.6	71.5181818181818	8.08181818181818
44	62.8	71.5181818181818	-8.71818181818182
45	95.1	92.8466666666667	2.25333333333333
46	63.6	71.5181818181818	-7.91818181818181
47	61.4	71.5181818181818	-10.1181818181818
48	98.2	108.914285714286	-10.7142857142857
49	95.3	92.8466666666667	2.45333333333333
50	81.5	92.8466666666667	-11.3466666666667
51	85.5	108.914285714286	-23.4142857142857
52	71.1	71.5181818181818	-0.418181818181822
53	78.1	71.5181818181818	6.58181818181818
54	103	108.914285714286	-5.91428571428571
55	86	92.8466666666667	-6.84666666666666
56	86.2	71.5181818181818	14.6818181818182
57	105.7	108.914285714286	-3.21428571428571
58	57.2	71.5181818181818	-14.3181818181818
59	73.7	71.5181818181818	2.18181818181819
60	120.5	108.914285714286	11.5857142857143

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933743ew843xb37gfd7lk/2jaea1292933848.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933743ew843xb37gfd7lk/2jaea1292933848.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933743ew843xb37gfd7lk/3jaea1292933848.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933743ew843xb37gfd7lk/3jaea1292933848.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933743ew843xb37gfd7lk/4tkdv1292933848.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292933743ew843xb37gfd7lk/4tkdv1292933848.ps (open in new window)

Parameters (Session):

par1 = 4 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 4 ; 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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