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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: Fri, 24 Dec 2010 10:02:30 +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/24/t1293184802mrya2xdxgzwnd5s.htm/, Retrieved Fri, 24 Dec 2010 11:00:06 +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/24/t1293184802mrya2xdxgzwnd5s.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 «

172.69 104.31 172.98 103.88 172.98 103.88 172.89 103.86 173.38 103.89 173.20 103.98 173.24 103.98 172.86 104.29 172.86 104.29 172.74 104.24 172.28 103.98 171.05 103.54 171.07 103.44 171.07 103.32 171.07 103.30 171.11 103.26 170.72 103.14 170.49 103.11 170.48 102.91 170.48 103.23 170.48 103.23 170.57 103.14 170.39 102.91 170.04 102.42 169.67 102.10 169.57 102.07 169.57 102.06 169.53 101.98 169.24 101.83 169.29 101.75 169.21 101.56 168.58 101.66 168.58 101.65 168.55 101.61 168.46 101.52 167.39 101.31 167.16 101.19 167.16 101.11 167.16 101.10 167.17 101.07 166.52 100.98 166.35 100.93 166.19 100.92 166.19 101.02 166.19 101.01 166.07 100.97 166.64 100.89 166.26 100.62 166.44 100.53 166.27 100.48 166.27 100.48 166.30 100.47 165.97 100.52 164.58 100.49 164.28 100.47 163.93 100.44

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	7 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Goodness of Fit
Correlation	0.9676
R-squared	0.9362
RMSE	0.6665

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	172.69	172.918181818182	-0.228181818181810
2	172.98	172.918181818182	0.0618181818181824
3	172.98	172.918181818182	0.0618181818181824
4	172.89	172.918181818182	-0.0281818181818210
5	173.38	172.918181818182	0.461818181818188
6	173.2	172.918181818182	0.281818181818181
7	173.24	172.918181818182	0.321818181818202
8	172.86	172.918181818182	-0.0581818181817937
9	172.86	172.918181818182	-0.0581818181817937
10	172.74	172.918181818182	-0.178181818181798
11	172.28	172.918181818182	-0.638181818181806
12	171.05	170.31	0.73999999999998
13	171.07	170.31	0.759999999999962
14	171.07	170.31	0.759999999999962
15	171.07	170.31	0.759999999999962
16	171.11	170.31	0.799999999999983
17	170.72	170.31	0.409999999999968
18	170.49	170.31	0.179999999999978
19	170.48	170.31	0.169999999999959
20	170.48	170.31	0.169999999999959
21	170.48	170.31	0.169999999999959
22	170.57	170.31	0.259999999999962
23	170.39	170.31	0.0799999999999557
24	170.04	170.31	-0.270000000000039
25	169.67	170.31	-0.640000000000043
26	169.57	170.31	-0.740000000000038
27	169.57	170.31	-0.740000000000038
28	169.53	170.31	-0.78000000000003
29	169.24	170.31	-1.07000000000002
30	169.29	170.31	-1.02000000000004
31	169.21	167.942	1.26799999999997
32	168.58	167.942	0.637999999999977
33	168.58	167.942	0.637999999999977
34	168.55	167.942	0.607999999999976
35	168.46	167.942	0.517999999999972
36	167.39	167.942	-0.552000000000049
37	167.16	167.942	-0.782000000000039
38	167.16	167.942	-0.782000000000039
39	167.16	167.942	-0.782000000000039
40	167.17	167.942	-0.772000000000048
41	166.52	165.903125	0.616875000000022
42	166.35	165.903125	0.446875000000006
43	166.19	165.903125	0.286875000000009
44	166.19	165.903125	0.286875000000009
45	166.19	165.903125	0.286875000000009
46	166.07	165.903125	0.166875000000005
47	166.64	165.903125	0.736874999999998
48	166.26	165.903125	0.356875000000002
49	166.44	165.903125	0.536875000000009
50	166.27	165.903125	0.366875000000022
51	166.27	165.903125	0.366875000000022
52	166.3	165.903125	0.396875000000023
53	165.97	165.903125	0.0668750000000102
54	164.58	165.903125	-1.32312499999998
55	164.28	165.903125	-1.62312499999999
56	163.93	165.903125	-1.97312499999998

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293184802mrya2xdxgzwnd5s/2xvcr1293184942.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293184802mrya2xdxgzwnd5s/2xvcr1293184942.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293184802mrya2xdxgzwnd5s/374tu1293184942.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293184802mrya2xdxgzwnd5s/374tu1293184942.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293184802mrya2xdxgzwnd5s/4ivax1293184942.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293184802mrya2xdxgzwnd5s/4ivax1293184942.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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