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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 16:55:18 +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/24/t12932100611g13dzllbhzxhkl.htm/, Retrieved Fri, 24 Dec 2010 18:01:04 +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/t12932100611g13dzllbhzxhkl.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 «

1.35 75.53 1.91 75.75 1.31 76.57 1.19 77.59 1.3 77.15 1.14 79.08 1.1 80.29 1.02 79.94 1.11 80.19 1.18 79.70 1.24 79.14 1.36 78.23 1.29 77.16 1.73 76.77 1.41 76.19 1.15 74.83 1.31 74.33 1.15 72.71 1.08 71.32 1.1 71.88 1.14 71.78 1.24 71.77 1.33 72.17 1.49 70.84 1.38 70.64 1.96 70.85 1.36 71.43 1.24 78.52 1.35 81.12 1.23 84.16 1.09 84.36 1.08 84.13 1.33 83.59 1.35 82.13 1.38 83.03 1.5 83.91 1.47 83.01 2.09 82.36 1.52 82.01 1.29 81.83 1.52 80.89 1.27 82.86 1.35 83.28 1.29 82.63 1.41 81.52 1.39 82.20 1.45 81.97 1.53 81.60 1.45 82.36 2.11 82.55 1.53 81.27 1.38 79.89 1.54 74.44 1.35 73.47 1.29 73.16 1.33 73.16 1.47 72.94 1.47 72.89 1.54 73.26 1.59 73.93 1.5 72.58 2 72.00 1.51 72.79 1.4 71.86 1.62 69.74 1.44 69.73 1.29 69.05 1.28 69.63 1.4 70.48 1.39 72.49 1.46 72.66 1.49 74.77

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	11 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Goodness of Fit
Correlation	NA
R-squared	NA
RMSE	0.2235

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	1.35	1.39291666666667	-0.0429166666666672
2	1.91	1.39291666666667	0.517083333333333
3	1.31	1.39291666666667	-0.0829166666666672
4	1.19	1.39291666666667	-0.202916666666667
5	1.3	1.39291666666667	-0.0929166666666672
6	1.14	1.39291666666667	-0.252916666666667
7	1.1	1.39291666666667	-0.292916666666667
8	1.02	1.39291666666667	-0.372916666666667
9	1.11	1.39291666666667	-0.282916666666667
10	1.18	1.39291666666667	-0.212916666666667
11	1.24	1.39291666666667	-0.152916666666667
12	1.36	1.39291666666667	-0.0329166666666671
13	1.29	1.39291666666667	-0.102916666666667
14	1.73	1.39291666666667	0.337083333333333
15	1.41	1.39291666666667	0.0170833333333327
16	1.15	1.39291666666667	-0.242916666666667
17	1.31	1.39291666666667	-0.0829166666666672
18	1.15	1.39291666666667	-0.242916666666667
19	1.08	1.39291666666667	-0.312916666666667
20	1.1	1.39291666666667	-0.292916666666667
21	1.14	1.39291666666667	-0.252916666666667
22	1.24	1.39291666666667	-0.152916666666667
23	1.33	1.39291666666667	-0.0629166666666672
24	1.49	1.39291666666667	0.0970833333333327
25	1.38	1.39291666666667	-0.0129166666666674
26	1.96	1.39291666666667	0.567083333333333
27	1.36	1.39291666666667	-0.0329166666666671
28	1.24	1.39291666666667	-0.152916666666667
29	1.35	1.39291666666667	-0.0429166666666672
30	1.23	1.39291666666667	-0.162916666666667
31	1.09	1.39291666666667	-0.302916666666667
32	1.08	1.39291666666667	-0.312916666666667
33	1.33	1.39291666666667	-0.0629166666666672
34	1.35	1.39291666666667	-0.0429166666666672
35	1.38	1.39291666666667	-0.0129166666666674
36	1.5	1.39291666666667	0.107083333333333
37	1.47	1.39291666666667	0.0770833333333327
38	2.09	1.39291666666667	0.697083333333333
39	1.52	1.39291666666667	0.127083333333333
40	1.29	1.39291666666667	-0.102916666666667
41	1.52	1.39291666666667	0.127083333333333
42	1.27	1.39291666666667	-0.122916666666667
43	1.35	1.39291666666667	-0.0429166666666672
44	1.29	1.39291666666667	-0.102916666666667
45	1.41	1.39291666666667	0.0170833333333327
46	1.39	1.39291666666667	-0.00291666666666734
47	1.45	1.39291666666667	0.0570833333333327
48	1.53	1.39291666666667	0.137083333333333
49	1.45	1.39291666666667	0.0570833333333327
50	2.11	1.39291666666667	0.717083333333333
51	1.53	1.39291666666667	0.137083333333333
52	1.38	1.39291666666667	-0.0129166666666674
53	1.54	1.39291666666667	0.147083333333333
54	1.35	1.39291666666667	-0.0429166666666672
55	1.29	1.39291666666667	-0.102916666666667
56	1.33	1.39291666666667	-0.0629166666666672
57	1.47	1.39291666666667	0.0770833333333327
58	1.47	1.39291666666667	0.0770833333333327
59	1.54	1.39291666666667	0.147083333333333
60	1.59	1.39291666666667	0.197083333333333
61	1.5	1.39291666666667	0.107083333333333
62	2	1.39291666666667	0.607083333333333
63	1.51	1.39291666666667	0.117083333333333
64	1.4	1.39291666666667	0.00708333333333266
65	1.62	1.39291666666667	0.227083333333333
66	1.44	1.39291666666667	0.0470833333333327
67	1.29	1.39291666666667	-0.102916666666667
68	1.28	1.39291666666667	-0.112916666666667
69	1.4	1.39291666666667	0.00708333333333266
70	1.39	1.39291666666667	-0.00291666666666734
71	1.46	1.39291666666667	0.0670833333333327
72	1.49	1.39291666666667	0.0970833333333327

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t12932100611g13dzllbhzxhkl/2zxyq1293209704.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t12932100611g13dzllbhzxhkl/2zxyq1293209704.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t12932100611g13dzllbhzxhkl/3a7yb1293209704.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t12932100611g13dzllbhzxhkl/3a7yb1293209704.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t12932100611g13dzllbhzxhkl/4kgfw1293209704.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t12932100611g13dzllbhzxhkl/4kgfw1293209704.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 1 ; par2 = none ; 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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