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Ws 10 - 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: Tue, 14 Dec 2010 22:27:38 +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/14/t129236558995994lcusd28a33.htm/, Retrieved Tue, 14 Dec 2010 23:26:30 +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/14/t129236558995994lcusd28a33.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 «

2 24 14 11 12 24 26 2 25 11 7 8 25 23 2 17 6 17 8 30 25 1 18 12 10 8 19 23 2 18 8 12 9 22 19 2 16 10 12 7 22 29 2 20 10 11 4 25 25 2 16 11 11 11 23 21 2 18 16 12 7 17 22 2 17 11 13 7 21 25 1 23 13 14 12 19 24 2 30 12 16 10 19 18 1 23 8 11 10 15 22 2 18 12 10 8 16 15 2 15 11 11 8 23 22 1 12 4 15 4 27 28 1 21 9 9 9 22 20 2 15 8 11 8 14 12 1 20 8 17 7 22 24 2 31 14 17 11 23 20 1 27 15 11 9 23 21 2 34 16 18 11 21 20 2 21 9 14 13 19 21 2 31 14 10 8 18 23 1 19 11 11 8 20 28 2 16 8 15 9 23 24 1 20 9 15 6 25 24 2 21 9 13 9 19 24 2 22 9 16 9 24 23 1 17 9 13 6 22 23 2 24 10 9 6 25 29 1 25 16 18 16 26 24 2 26 11 18 5 29 18 2 25 8 12 7 32 25 1 17 9 17 9 25 21 1 32 16 9 6 29 26 1 33 11 9 6 28 22 1 13 16 12 5 17 22 2 32 12 18 12 28 22 1 25 12 12 7 29 23 1 29 14 18 10 26 30 2 22 9 14 9 25 23 1 18 10 15 8 14 17 1 17 9 16 5 25 23 2 20 10 10 8 26 23 2 15 12 11 8 20 25 2 20 14 14 10 18 24 2 33 14 9 6 32 24 2 29 10 12 8 25 23 1 23 14 17 7 25 21 2 26 16 5 4 23 24 1 18 9 12 8 21 2 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

Goodness of Fit
Correlation	0.5955
R-squared	0.3546
RMSE	2.1679

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	12	8.87037037037037	3.12962962962963
2	8	6.52941176470588	1.47058823529412
3	8	7.47272727272727	0.527272727272727
4	8	6.52941176470588	1.47058823529412
5	9	7.47272727272727	1.52727272727273
6	7	7.47272727272727	-0.472727272727273
7	4	7.47272727272727	-3.47272727272727
8	11	7.47272727272727	3.52727272727273
9	7	7.47272727272727	-0.472727272727273
10	7	7.47272727272727	-0.472727272727273
11	12	8.87037037037037	3.12962962962963
12	10	8.87037037037037	1.12962962962963
13	10	8.87037037037037	1.12962962962963
14	8	6.52941176470588	1.47058823529412
15	8	7.47272727272727	0.527272727272727
16	4	7.47272727272727	-3.47272727272727
17	9	6.52941176470588	2.47058823529412
18	8	7.47272727272727	0.527272727272727
19	7	7.47272727272727	-0.472727272727273
20	11	8.87037037037037	2.12962962962963
21	9	8.87037037037037	0.12962962962963
22	11	12.25	-1.25
23	13	8.87037037037037	4.12962962962963
24	8	6.52941176470588	1.47058823529412
25	8	7.47272727272727	0.527272727272727
26	9	7.47272727272727	1.52727272727273
27	6	7.47272727272727	-1.47272727272727
28	9	8.87037037037037	0.12962962962963
29	9	8.87037037037037	0.12962962962963
30	6	7.47272727272727	-1.47272727272727
31	6	6.52941176470588	-0.529411764705882
32	16	12.25	3.75
33	5	12.25	-7.25
34	7	8.87037037037037	-1.87037037037037
35	9	7.47272727272727	1.52727272727273
36	6	6.52941176470588	-0.529411764705882
37	6	6.52941176470588	-0.529411764705882
38	5	7.47272727272727	-2.47272727272727
39	12	12.25	-0.25
40	7	8.87037037037037	-1.87037037037037
41	10	12.25	-2.25
42	9	8.87037037037037	0.12962962962963
43	8	7.47272727272727	0.527272727272727
44	5	7.47272727272727	-2.47272727272727
45	8	6.52941176470588	1.47058823529412
46	8	7.47272727272727	0.527272727272727
47	10	7.47272727272727	2.52727272727273
48	6	6.52941176470588	-0.529411764705882
49	8	8.87037037037037	-0.87037037037037
50	7	8.87037037037037	-1.87037037037037
51	4	6.52941176470588	-2.52941176470588
52	8	7.47272727272727	0.527272727272727
53	8	7.47272727272727	0.527272727272727
54	4	6.52941176470588	-2.52941176470588
55	20	12.25	7.75
56	8	8.87037037037037	-0.87037037037037
57	8	8.87037037037037	-0.87037037037037
58	6	7.47272727272727	-1.47272727272727
59	4	6.52941176470588	-2.52941176470588
60	8	7.47272727272727	0.527272727272727
61	9	7.47272727272727	1.52727272727273
62	6	8.87037037037037	-2.87037037037037
63	7	7.47272727272727	-0.472727272727273
64	9	6.52941176470588	2.47058823529412
65	5	7.47272727272727	-2.47272727272727
66	5	6.52941176470588	-1.52941176470588
67	8	8.87037037037037	-0.87037037037037
68	8	7.47272727272727	0.527272727272727
69	6	6.52941176470588	-0.529411764705882
70	8	7.47272727272727	0.527272727272727
71	7	8.87037037037037	-1.87037037037037
72	7	6.52941176470588	0.470588235294118
73	9	8.87037037037037	0.12962962962963
74	11	12.25	-1.25
75	6	8.87037037037037	-2.87037037037037
76	8	8.87037037037037	-0.87037037037037
77	6	7.47272727272727	-1.47272727272727
78	9	8.87037037037037	0.12962962962963
79	8	6.52941176470588	1.47058823529412
80	6	8.87037037037037	-2.87037037037037
81	10	8.87037037037037	1.12962962962963
82	8	7.47272727272727	0.527272727272727
83	8	8.87037037037037	-0.87037037037037
84	10	7.47272727272727	2.52727272727273
85	5	6.52941176470588	-1.52941176470588
86	7	8.87037037037037	-1.87037037037037
87	5	8.87037037037037	-3.87037037037037
88	8	6.52941176470588	1.47058823529412
89	14	8.87037037037037	5.12962962962963
90	7	7.47272727272727	-0.472727272727273
91	8	8.87037037037037	-0.87037037037037
92	6	6.52941176470588	-0.529411764705882
93	5	6.52941176470588	-1.52941176470588
94	6	8.87037037037037	-2.87037037037037
95	10	7.47272727272727	2.52727272727273
96	12	12.25	-0.25
97	9	8.87037037037037	0.12962962962963
98	12	12.25	-0.25
99	7	7.47272727272727	-0.472727272727273
100	8	8.87037037037037	-0.87037037037037
101	10	8.87037037037037	1.12962962962963
102	6	7.47272727272727	-1.47272727272727
103	10	12.25	-2.25
104	10	7.47272727272727	2.52727272727273
105	10	8.87037037037037	1.12962962962963
106	5	8.87037037037037	-3.87037037037037
107	7	6.52941176470588	0.470588235294118
108	10	8.87037037037037	1.12962962962963
109	11	8.87037037037037	2.12962962962963
110	6	7.47272727272727	-1.47272727272727
111	7	7.47272727272727	-0.472727272727273
112	12	8.87037037037037	3.12962962962963
113	11	6.52941176470588	4.47058823529412
114	11	12.25	-1.25
115	11	6.52941176470588	4.47058823529412
116	5	7.47272727272727	-2.47272727272727
117	8	7.47272727272727	0.527272727272727
118	6	6.52941176470588	-0.529411764705882
119	9	8.87037037037037	0.12962962962963
120	4	6.52941176470588	-2.52941176470588
121	4	6.52941176470588	-2.52941176470588
122	7	7.47272727272727	-0.472727272727273
123	11	8.87037037037037	2.12962962962963
124	6	6.52941176470588	-0.529411764705882
125	7	6.52941176470588	0.470588235294118
126	8	7.47272727272727	0.527272727272727
127	4	6.52941176470588	-2.52941176470588
128	8	7.47272727272727	0.527272727272727
129	9	7.47272727272727	1.52727272727273
130	8	8.87037037037037	-0.87037037037037
131	11	8.87037037037037	2.12962962962963
132	8	7.47272727272727	0.527272727272727
133	5	7.47272727272727	-2.47272727272727
134	4	6.52941176470588	-2.52941176470588
135	8	8.87037037037037	-0.87037037037037
136	10	12.25	-2.25
137	6	7.47272727272727	-1.47272727272727
138	9	7.47272727272727	1.52727272727273
139	9	7.47272727272727	1.52727272727273
140	13	8.87037037037037	4.12962962962963
141	9	6.52941176470588	2.47058823529412
142	10	8.87037037037037	1.12962962962963
143	20	12.25	7.75
144	5	6.52941176470588	-1.52941176470588
145	11	8.87037037037037	2.12962962962963
146	6	8.87037037037037	-2.87037037037037
147	9	8.87037037037037	0.12962962962963
148	7	7.47272727272727	-0.472727272727273
149	9	7.47272727272727	1.52727272727273
150	10	8.87037037037037	1.12962962962963
151	9	7.47272727272727	1.52727272727273
152	8	8.87037037037037	-0.87037037037037
153	7	12.25	-5.25
154	6	8.87037037037037	-2.87037037037037
155	13	12.25	0.75
156	6	7.47272727272727	-1.47272727272727
157	8	8.87037037037037	-0.87037037037037
158	10	8.87037037037037	1.12962962962963
159	16	12.25	3.75

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t129236558995994lcusd28a33/2h5341292365651.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129236558995994lcusd28a33/2h5341292365651.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129236558995994lcusd28a33/3h5341292365651.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129236558995994lcusd28a33/3h5341292365651.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129236558995994lcusd28a33/4seko1292365651.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t129236558995994lcusd28a33/4seko1292365651.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 5 ; 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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