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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: Wed, 22 Dec 2010 17:28:30 +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/22/t1293038899t4kpfjfu6lt60u8.htm/, Retrieved Wed, 22 Dec 2010 18:28:19 +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/22/t1293038899t4kpfjfu6lt60u8.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 «

41 38 13 12 14 12 39 32 16 11 18 11 30 35 19 15 11 14 31 33 15 6 12 12 34 37 14 13 16 21 35 29 13 10 18 12 39 31 19 12 14 22 34 36 15 14 14 11 36 35 14 12 15 10 37 38 15 6 15 13 38 31 16 10 17 10 36 34 16 12 19 8 38 35 16 12 10 15 39 38 16 11 16 14 33 37 17 15 18 10 32 33 15 12 14 14 36 32 15 10 14 14 38 38 20 12 17 11 39 38 18 11 14 10 32 32 16 12 16 13 32 33 16 11 18 7 31 31 16 12 11 14 39 38 19 13 14 12 37 39 16 11 12 14 39 32 17 9 17 11 41 32 17 13 9 9 36 35 16 10 16 11 33 37 15 14 14 15 33 33 16 12 15 14 34 33 14 10 11 13 31 28 15 12 16 9 27 32 12 8 13 15 37 31 14 10 17 10 34 37 16 12 15 11 34 30 14 12 14 13 32 33 7 7 16 8 29 31 10 6 9 20 36 33 14 12 15 12 29 31 16 10 17 10 35 33 16 10 13 10 37 32 16 10 15 9 34 33 14 12 16 14 38 32 20 15 16 8 35 33 14 10 12 14 38 28 14 10 12 11 37 35 11 12 11 13 38 39 14 13 15 9 33 34 15 11 15 11 36 38 16 11 17 15 38 32 14 12 13 11 32 38 16 14 16 10 32 30 14 10 14 14 32 33 12 12 11 18 34 38 16 13 12 14 32 32 9 5 12 11 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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.5325
R-squared	0.2835
RMSE	1.9725

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	14	14.8039215686275	-0.803921568627452
2	18	14.8039215686275	3.19607843137255
3	11	13.5238095238095	-2.52380952380952
4	12	14.8039215686275	-2.80392156862745
5	16	10.8888888888889	5.11111111111111
6	18	14.8039215686275	3.19607843137255
7	14	10.8888888888889	3.11111111111111
8	14	14.8039215686275	-0.803921568627452
9	15	14.8039215686275	0.196078431372548
10	15	14.8039215686275	0.196078431372548
11	17	14.8039215686275	2.19607843137255
12	19	14.8039215686275	4.19607843137255
13	10	13.5238095238095	-3.52380952380952
14	16	13.5238095238095	2.47619047619048
15	18	14.8039215686275	3.19607843137255
16	14	13.5238095238095	0.476190476190476
17	14	13.5238095238095	0.476190476190476
18	17	14.8039215686275	2.19607843137255
19	14	14.8039215686275	-0.803921568627452
20	16	14.8039215686275	1.19607843137255
21	18	14.8039215686275	3.19607843137255
22	11	13.5238095238095	-2.52380952380952
23	14	14.8039215686275	-0.803921568627452
24	12	13.5238095238095	-1.52380952380952
25	17	14.8039215686275	2.19607843137255
26	9	14.8039215686275	-5.80392156862745
27	16	14.8039215686275	1.19607843137255
28	14	13.5238095238095	0.476190476190476
29	15	13.5238095238095	1.47619047619048
30	11	14.8039215686275	-3.80392156862745
31	16	14.8039215686275	1.19607843137255
32	13	13.5238095238095	-0.523809523809524
33	17	14.8039215686275	2.19607843137255
34	15	14.8039215686275	0.196078431372548
35	14	14.8039215686275	-0.803921568627452
36	16	14.8039215686275	1.19607843137255
37	9	10.8888888888889	-1.88888888888889
38	15	14.8039215686275	0.196078431372548
39	17	14.8039215686275	2.19607843137255
40	13	14.8039215686275	-1.80392156862745
41	15	14.8039215686275	0.196078431372548
42	16	13.5238095238095	2.47619047619048
43	16	14.8039215686275	1.19607843137255
44	12	13.5238095238095	-1.52380952380952
45	12	14.8039215686275	-2.80392156862745
46	11	14.8039215686275	-3.80392156862745
47	15	14.8039215686275	0.196078431372548
48	15	14.8039215686275	0.196078431372548
49	17	13.5238095238095	3.47619047619048
50	13	14.8039215686275	-1.80392156862745
51	16	14.8039215686275	1.19607843137255
52	14	13.5238095238095	0.476190476190476
53	11	10.8888888888889	0.111111111111111
54	12	13.5238095238095	-1.52380952380952
55	12	14.8039215686275	-2.80392156862745
56	15	14.8039215686275	0.196078431372548
57	16	14.8039215686275	1.19607843137255
58	15	14.8039215686275	0.196078431372548
59	12	14.8039215686275	-2.80392156862745
60	12	13.5238095238095	-1.52380952380952
61	8	10.8888888888889	-2.88888888888889
62	13	14.8039215686275	-1.80392156862745
63	11	14.8039215686275	-3.80392156862745
64	14	13.5238095238095	0.476190476190476
65	15	14.8039215686275	0.196078431372548
66	10	14.8039215686275	-4.80392156862745
67	11	10.8888888888889	0.111111111111111
68	12	14.8039215686275	-2.80392156862745
69	15	14.8039215686275	0.196078431372548
70	15	13.5238095238095	1.47619047619048
71	14	14.8039215686275	-0.803921568627452
72	16	13.5238095238095	2.47619047619048
73	15	14.8039215686275	0.196078431372548
74	15	14.8039215686275	0.196078431372548
75	13	14.8039215686275	-1.80392156862745
76	12	10.8888888888889	1.11111111111111
77	17	14.8039215686275	2.19607843137255
78	13	10.8888888888889	2.11111111111111
79	15	14.8039215686275	0.196078431372548
80	13	14.8039215686275	-1.80392156862745
81	15	14.8039215686275	0.196078431372548
82	16	14.8039215686275	1.19607843137255
83	15	14.8039215686275	0.196078431372548
84	16	14.8039215686275	1.19607843137255
85	15	14.8039215686275	0.196078431372548
86	14	14.8039215686275	-0.803921568627452
87	15	14.8039215686275	0.196078431372548
88	14	14.8039215686275	-0.803921568627452
89	13	13.5238095238095	-0.523809523809524
90	7	10.8888888888889	-3.88888888888889
91	17	14.8039215686275	2.19607843137255
92	13	13.5238095238095	-0.523809523809524
93	15	14.8039215686275	0.196078431372548
94	14	13.5238095238095	0.476190476190476
95	13	14.8039215686275	-1.80392156862745
96	16	14.8039215686275	1.19607843137255
97	12	13.5238095238095	-1.52380952380952
98	14	14.8039215686275	-0.803921568627452
99	17	14.8039215686275	2.19607843137255
100	15	14.8039215686275	0.196078431372548
101	17	14.8039215686275	2.19607843137255
102	12	13.5238095238095	-1.52380952380952
103	16	14.8039215686275	1.19607843137255
104	11	14.8039215686275	-3.80392156862745
105	15	13.5238095238095	1.47619047619048
106	9	10.8888888888889	-1.88888888888889
107	16	14.8039215686275	1.19607843137255
108	15	13.5238095238095	1.47619047619048
109	10	13.5238095238095	-3.52380952380952
110	10	10.8888888888889	-0.88888888888889
111	15	13.5238095238095	1.47619047619048
112	11	13.5238095238095	-2.52380952380952
113	13	14.8039215686275	-1.80392156862745
114	14	13.5238095238095	0.476190476190476
115	18	14.8039215686275	3.19607843137255
116	16	14.8039215686275	1.19607843137255
117	14	13.5238095238095	0.476190476190476
118	14	14.8039215686275	-0.803921568627452
119	14	14.8039215686275	-0.803921568627452
120	14	13.5238095238095	0.476190476190476
121	12	13.5238095238095	-1.52380952380952
122	14	13.5238095238095	0.476190476190476
123	15	14.8039215686275	0.196078431372548
124	15	14.8039215686275	0.196078431372548
125	15	14.8039215686275	0.196078431372548
126	13	14.8039215686275	-1.80392156862745
127	17	14.8039215686275	2.19607843137255
128	17	14.8039215686275	2.19607843137255
129	19	14.8039215686275	4.19607843137255
130	15	14.8039215686275	0.196078431372548
131	13	14.8039215686275	-1.80392156862745
132	9	10.8888888888889	-1.88888888888889
133	15	14.8039215686275	0.196078431372548
134	15	13.5238095238095	1.47619047619048
135	15	14.8039215686275	0.196078431372548
136	16	13.5238095238095	2.47619047619048
137	11	10.8888888888889	0.111111111111111
138	14	13.5238095238095	0.476190476190476
139	11	13.5238095238095	-2.52380952380952
140	15	14.8039215686275	0.196078431372548
141	13	14.8039215686275	-1.80392156862745
142	15	14.8039215686275	0.196078431372548
143	16	13.5238095238095	2.47619047619048
144	14	14.8039215686275	-0.803921568627452
145	15	14.8039215686275	0.196078431372548
146	16	14.8039215686275	1.19607843137255
147	16	14.8039215686275	1.19607843137255
148	11	14.8039215686275	-3.80392156862745
149	12	14.8039215686275	-2.80392156862745
150	9	10.8888888888889	-1.88888888888889
151	16	14.8039215686275	1.19607843137255
152	13	10.8888888888889	2.11111111111111
153	16	14.8039215686275	1.19607843137255
154	12	14.8039215686275	-2.80392156862745
155	9	10.8888888888889	-1.88888888888889
156	13	10.8888888888889	2.11111111111111
157	13	13.5238095238095	-0.523809523809524
158	14	13.5238095238095	0.476190476190476
159	19	14.8039215686275	4.19607843137255
160	13	14.8039215686275	-1.80392156862745
161	12	10.8888888888889	1.11111111111111
162	13	13.5238095238095	-0.523809523809524

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038899t4kpfjfu6lt60u8/2m7y41293038902.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038899t4kpfjfu6lt60u8/2m7y41293038902.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038899t4kpfjfu6lt60u8/3m7y41293038902.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038899t4kpfjfu6lt60u8/3m7y41293038902.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038899t4kpfjfu6lt60u8/487xa1293038902.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293038899t4kpfjfu6lt60u8/487xa1293038902.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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