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Workshop 10; PLC: Recursive partitioning (no categorization)

*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: Mon, 13 Dec 2010 21:44:07 +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/13/t1292276561g9ogweuyd4qv2zw.htm/, Retrieved Mon, 13 Dec 2010 22:42:41 +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/13/t1292276561g9ogweuyd4qv2zw.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 «
107.11 236.67 8.92 1 122.23 258.1 9.32 2 134.69 241.52 8.9 3 128.79 190.71 8.53 4 126.16 200.32 8.51 5 119.98 223.41 9.03 6 108.45 201.38 9.6 7 108.43 211.83 9.88 8 98.17 224.41 10.81 9 106.09 211.57 11.61 10 108.81 194.77 11.81 11 103.03 201.86 13.93 12 124.36 225 16.19 13 118.52 278.9 18.05 14 112.2 259.74 17.08 15 114.71 230.45 17.46 16 107.96 238.26 16.9 17 101.21 250.14 15.69 18 102.77 263.81 15.86 19 112.13 247.22 12.98 20 109.36 229.81 12.31 21 110.91 224.27 11.51 22 123.57 213.23 11.73 23 129.95 239.57 11.7 24 124.46 249.7 10.9 25 122.34 212.5 10.57 26 116.61 203.27 10.37 27 114.59 192.05 9.59 28 112.52 190.04 9.09 29 118.67 202.05 9.26 30 116.8 211.91 9.9 31 123.63 210.39 9.61 32 128.04 231.25 9.85 33 134.57 224.3 9.99 34 130.33 209.64 9.9 35 136.47 206.05 10.45 36 139.05 229.7 11.66 37 158.21 264.67 13.61 38 148.07 246.29 12.88 39 137.74 260.91 12.52 40 139.74 265.14 10.93 41 144.08 284.52 12.07 42 145.35 287.48 13.21 43 145.77 321.9 13.68 44 140.56 321.59 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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Goodness of Fit
Correlation0.8348
R-squared0.6969
RMSE8.3808


Actuals, Predictions, and Residuals
#ActualsForecastsResiduals
1107.11115.2878125-8.1778125
2122.23115.28781256.9421875
3134.69115.287812519.4021875
4128.79115.287812513.5021875
5126.16115.287812510.8721875
6119.98115.28781254.6921875
7108.45115.2878125-6.8378125
8108.43115.2878125-6.8578125
998.17115.2878125-17.1178125
10106.09115.2878125-9.1978125
11108.81115.2878125-6.4778125
12103.03115.2878125-12.2578125
13124.36115.28781259.0721875
14118.52115.28781253.23218749999999
15112.2115.2878125-3.0878125
16114.71115.2878125-0.577812500000007
17107.96115.2878125-7.32781250000001
18101.21115.2878125-14.0778125
19102.77115.2878125-12.5178125
20112.13115.2878125-3.15781250000001
21109.36115.2878125-5.9278125
22110.91115.2878125-4.37781250000000
23123.57115.28781258.2821875
24129.95115.287812514.6621875
25124.46115.28781259.1721875
26122.34115.28781257.0521875
27116.61115.28781251.32218750000000
28114.59115.2878125-0.697812499999998
29112.52115.2878125-2.76781250000001
30118.67115.28781253.3821875
31116.8115.28781251.51218750000000
32123.63115.28781258.3421875
33128.04133.008235294118-4.96823529411765
34134.57133.0082352941181.56176470588235
35130.33133.008235294118-2.67823529411763
36136.47133.0082352941183.46176470588236
37139.05133.0082352941186.04176470588237
38158.21147.71090909090910.4990909090909
39148.07133.00823529411815.0617647058824
40137.74133.0082352941184.73176470588237
41139.74133.0082352941186.73176470588237
42144.08133.00823529411811.0717647058824
43145.35133.00823529411812.3417647058824
44145.77147.710909090909-1.94090909090909
45140.56147.710909090909-7.1509090909091
46121.41133.008235294118-11.5982352941176
47120.44133.008235294118-12.5682352941176
48116.97133.008235294118-16.0382352941176
49128.03133.008235294118-4.97823529411764
50128.51133.008235294118-4.49823529411765
51127.76133.008235294118-5.24823529411763
52134.58133.0082352941181.57176470588237
53147.64147.710909090909-0.0709090909091117
54144.46147.710909090909-3.25090909090909
55137.6147.710909090909-10.1109090909091
56146.87147.710909090909-0.840909090909093
57145.67147.710909090909-2.04090909090911
58151.95147.7109090909094.23909090909089
59150.23147.7109090909092.51909090909089
60155.86147.7109090909098.14909090909092
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292276561g9ogweuyd4qv2zw/2znog1292276640.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292276561g9ogweuyd4qv2zw/2znog1292276640.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/13/t1292276561g9ogweuyd4qv2zw/3znog1292276640.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292276561g9ogweuyd4qv2zw/3znog1292276640.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/13/t1292276561g9ogweuyd4qv2zw/4ren01292276640.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292276561g9ogweuyd4qv2zw/4ren01292276640.ps (open in new window)


 
Parameters (Session):
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')
}
 




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