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recursive partitioning

*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 10:20:08 +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/t129318590592mqlv1diwchptr.htm/, Retrieved Fri, 24 Dec 2010 11:18:26 +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/t129318590592mqlv1diwchptr.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 «

3 4 2 4 4 3 3 2 4 2 4 4 4 3 2 3 3 3 2 2 3 3 3 3 2 3 4 1 4 2 3 4 4 5 5 2 3 2 4 4 3 4 2 4 4 3 3 2 2 2 3 4 4 2 4 3 3 3 4 2 3 4 3 4 4 2 3 2 5 4 3 3 3 5 1 3 4 2 4 4 2 3 3 3 3 3 4 3 4 4 2 3 2 4 1 1 3 2 2 2 2 4 1 4 1 3 4 4 4 4 3 3 3 4 4 3 3 2 4 1 3 4 3 4 4 3 4 3 4 5 2 4 4 4 4 3 4 3 3 4 3 4 3 3 4 4 4 2 4 2 3 2 2 3 2 3 4 4 4 4 3 4 4 4 4 3 4 2 4 2 2 3 2 4 3 3 4 3 4 4 3 4 3 4 3 3 3 3 3 2 3 4 3 4 4 4 4 4 4 4 3 3 3 4 3 3 4 1 3 1 1 3 2 5 2 2 3 2 4 4 3 3 3 4 2 4 3 4 4 3 4 4 3 4 4 2 2 2 5 1 1 3 2 4 3 3 4 3 4 4 3 3 3 4 1 1 3 4 3 4 3 4 3 4 3 2 3 2 4 1 3 4 2 5 1 3 4 2 4 4 2 4 2 3 4 4 5 3 3 4 1 4 1 4 2 3 4 5 4 4 2 3 2 4 2 4 3 3 4 4 3 3 4 3 5 4 4 4 3 3 3 3 2 3 2 3 4 3 4 4 3 3 3 4 4 3 3 3 4 2 3 3 3 4 3 1 1 1 5 1 3 4 3 3 4 3 4 3 4 4 3 4 3 3 4 2 4 4 2 4 3 4 3 4 3 3 4 4 4 4 3 3 3 5 3 2 3 3 2 3 3 3 2 4 2 2 3 1 4 1 2 4 2 4 2 3 3 3 NA 4 3 4 3 4 3 2 4 2 3 5 2 2 2 4 1 3 3 3 5 3 2 3 2 2 2 3 4 3 2 3 4 4 4 4 4 2 4 2 4 3 3 4 3 3 3 2 4 3 4 4 4 4 4 4 4 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	'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Goodness of Fit
Correlation	0.5944
R-squared	0.3533
RMSE	0.9182

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	4	2.85714285714286	1.14285714285714
2	2	2.62962962962963	-0.62962962962963
3	2	3.74242424242424	-1.74242424242424
4	2	2.62962962962963	-0.62962962962963
5	2	2.62962962962963	-0.62962962962963
6	2	2.85714285714286	-0.857142857142857
7	5	3.74242424242424	1.25757575757576
8	4	2.62962962962963	1.37037037037037
9	4	2.85714285714286	1.14285714285714
10	2	2.62962962962963	-0.62962962962963
11	4	3.74242424242424	0.257575757575758
12	2	2.62962962962963	-0.62962962962963
13	4	3.74242424242424	0.257575757575758
14	4	2.62962962962963	1.37037037037037
15	1	2.62962962962963	-1.62962962962963
16	4	2.85714285714286	1.14285714285714
17	3	2.62962962962963	0.37037037037037
18	4	3.74242424242424	0.257575757575758
19	1	2.62962962962963	-1.62962962962963
20	2	2.62962962962963	-0.62962962962963
21	1	2.85714285714286	-1.85714285714286
22	4	3.74242424242424	0.257575757575758
23	4	2.62962962962963	1.37037037037037
24	1	2.62962962962963	-1.62962962962963
25	4	3.74242424242424	0.257575757575758
26	5	3.74242424242424	1.25757575757576
27	4	3.74242424242424	0.257575757575758
28	4	3.74242424242424	0.257575757575758
29	4	3.74242424242424	0.257575757575758
30	2	2.85714285714286	-0.857142857142857
31	2	1.125	0.875
32	4	3.74242424242424	0.257575757575758
33	4	3.74242424242424	0.257575757575758
34	2	2.85714285714286	-0.857142857142857
35	3	2.62962962962963	0.37037037037037
36	4	3.74242424242424	0.257575757575758
37	3	3.74242424242424	-0.742424242424242
38	2	2.62962962962963	-0.62962962962963
39	4	3.74242424242424	0.257575757575758
40	4	3.74242424242424	0.257575757575758
41	3	2.62962962962963	0.37037037037037
42	1	2.85714285714286	-1.85714285714286
43	2	2.62962962962963	-0.62962962962963
44	4	2.62962962962963	1.37037037037037
45	2	2.62962962962963	-0.62962962962963
46	3	2.62962962962963	0.37037037037037
47	4	3.74242424242424	0.257575757575758
48	1	1.125	-0.125
49	3	2.62962962962963	0.37037037037037
50	4	3.74242424242424	0.257575757575758
51	1	2.62962962962963	-1.62962962962963
52	4	2.62962962962963	1.37037037037037
53	3	3.74242424242424	-0.742424242424242
54	1	2.62962962962963	-1.62962962962963
55	1	2.85714285714286	-1.85714285714286
56	4	2.85714285714286	1.14285714285714
57	4	2.85714285714286	1.14285714285714
58	4	3.74242424242424	0.257575757575758
59	2	2.85714285714286	-0.857142857142857
60	4	3.74242424242424	0.257575757575758
61	2	2.62962962962963	-0.62962962962963
62	4	2.62962962962963	1.37037037037037
63	5	2.62962962962963	2.37037037037037
64	3	3.74242424242424	-0.742424242424242
65	2	2.62962962962963	-0.62962962962963
66	4	3.74242424242424	0.257575757575758
67	4	2.62962962962963	1.37037037037037
68	2	2.62962962962963	-0.62962962962963
69	3	2.62962962962963	0.37037037037037
70	1	1.125	-0.125
71	4	3.74242424242424	0.257575757575758
72	4	3.74242424242424	0.257575757575758
73	4	3.74242424242424	0.257575757575758
74	4	3.74242424242424	0.257575757575758
75	3	3.74242424242424	-0.742424242424242
76	4	3.74242424242424	0.257575757575758
77	3	2.62962962962963	0.37037037037037
78	3	2.62962962962963	0.37037037037037
79	2	2.62962962962963	-0.62962962962963
80	1	2.62962962962963	-1.62962962962963
81	2	2.85714285714286	-0.857142857142857
82	4	2.62962962962963	1.37037037037037
83	3	3.74242424242424	-0.742424242424242
84	5	2.85714285714286	2.14285714285714
85	1	1.125	-0.125
86	3	2.62962962962963	0.37037037037037
87	2	2.62962962962963	-0.62962962962963
88	3	3.74242424242424	-0.742424242424242
89	4	3.74242424242424	0.257575757575758
90	3	2.85714285714286	0.142857142857143
91	3	3.74242424242424	-0.742424242424242
92	4	3.74242424242424	0.257575757575758
93	4	3.74242424242424	0.257575757575758
94	4	3.74242424242424	0.257575757575758
95	2	2.62962962962963	-0.62962962962963
96	2	2.62962962962963	-0.62962962962963
97	1	1.125	-0.125
98	1	1.125	-0.125
99	4	3.74242424242424	0.257575757575758
100	2	2.62962962962963	-0.62962962962963
101	4	3.74242424242424	0.257575757575758
102	3	3.74242424242424	-0.742424242424242
103	5	3.74242424242424	1.25757575757576
104	1	2.62962962962963	-1.62962962962963
105	1	1.125	-0.125
106	2	3.74242424242424	-1.74242424242424
107	4	2.62962962962963	1.37037037037037
108	4	3.74242424242424	0.257575757575758
109	3	2.85714285714286	0.142857142857143
110	4	2.85714285714286	1.14285714285714
111	4	3.74242424242424	0.257575757575758
112	5	3.74242424242424	1.25757575757576
113	1	2.62962962962963	-1.62962962962963
114	4	3.74242424242424	0.257575757575758
115	4	3.74242424242424	0.257575757575758
116	4	2.62962962962963	1.37037037037037
117	2	2.62962962962963	-0.62962962962963
118	2	3.74242424242424	-1.74242424242424
119	4	3.74242424242424	0.257575757575758
120	3	2.62962962962963	0.37037037037037
121	3	2.62962962962963	0.37037037037037
122	4	2.62962962962963	1.37037037037037
123	2	2.85714285714286	-0.857142857142857
124	4	3.74242424242424	0.257575757575758
125	4	2.62962962962963	1.37037037037037
126	1	2.62962962962963	-1.62962962962963
127	4	3.74242424242424	0.257575757575758
128	4	3.74242424242424	0.257575757575758
129	3	2.85714285714286	0.142857142857143
130	1	1.125	-0.125
131	4	3.74242424242424	0.257575757575758
132	3	3.74242424242424	-0.742424242424242
133	2	2.85714285714286	-0.857142857142857
134	3	2.85714285714286	0.142857142857143
135	4	3.74242424242424	0.257575757575758
136	2	2.85714285714286	-0.857142857142857
137	4	3.74242424242424	0.257575757575758
138	4	2.85714285714286	1.14285714285714
139	3	3.74242424242424	-0.742424242424242
140	4	3.74242424242424	0.257575757575758
141	4	2.85714285714286	1.14285714285714
142	4	3.74242424242424	0.257575757575758
143	4	2.85714285714286	1.14285714285714
144	3	2.85714285714286	0.142857142857143
145	3	2.85714285714286	0.142857142857143
146	1	3.74242424242424	-2.74242424242424
147	2	2.85714285714286	-0.857142857142857
148	4	3.74242424242424	0.257575757575758
149	4	3.74242424242424	0.257575757575758
150	3	3.74242424242424	-0.742424242424242
151	3	3.74242424242424	-0.742424242424242
152	4	3.74242424242424	0.257575757575758
153	3	2.62962962962963	0.37037037037037
154	4	3.74242424242424	0.257575757575758
155	4	2.62962962962963	1.37037037037037
156	4	2.62962962962963	1.37037037037037

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t129318590592mqlv1diwchptr/2pm1c1293186000.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t129318590592mqlv1diwchptr/2pm1c1293186000.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t129318590592mqlv1diwchptr/3pm1c1293186000.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t129318590592mqlv1diwchptr/3pm1c1293186000.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t129318590592mqlv1diwchptr/4ie0g1293186000.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t129318590592mqlv1diwchptr/4ie0g1293186000.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = none ; par3 = 4 ; par4 = no ;

Parameters (R input):

par1 = 5 ; par2 = none ; par3 = 4 ; 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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