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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 11:07:38 +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/24/t1293188725f8yobs3p820i6tr.htm/, Retrieved Fri, 24 Dec 2010 12:05:26 +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/t1293188725f8yobs3p820i6tr.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 «

4 1 27 5 26 49 35 4 1 36 4 25 45 34 5 1 25 4 17 54 13 2 1 27 3 37 36 35 3 2 25 3 35 36 28 5 2 44 3 15 53 32 4 1 50 4 27 46 35 4 1 41 4 36 42 36 4 1 48 5 25 41 27 4 2 43 4 30 45 29 5 2 47 2 27 47 27 4 2 41 3 33 42 28 3 1 44 2 29 45 29 4 2 47 5 30 40 28 3 2 40 3 25 45 30 3 2 46 3 23 40 25 4 1 28 3 26 42 15 3 1 56 3 24 45 33 4 2 49 4 35 47 31 2 2 25 4 39 31 37 4 2 41 4 23 46 37 3 2 26 3 32 34 34 4 1 50 5 29 43 32 4 1 47 4 26 45 21 3 1 52 2 21 42 25 3 2 37 5 35 51 32 2 2 41 3 23 44 28 4 1 45 4 21 47 22 5 2 26 4 28 47 25 4 1 3 30 41 26 2 1 52 4 21 44 34 5 1 46 2 29 51 34 4 1 58 3 28 46 36 3 1 54 5 19 47 36 4 1 29 3 26 46 26 2 2 50 3 33 38 26 3 1 43 2 34 50 34 3 2 30 3 33 48 33 3 2 47 2 40 36 31 5 1 45 3 24 51 33 2 48 1 35 35 22 4 2 48 3 35 49 29 4 2 26 4 32 38 24 4 1 46 5 20 47 37 2 2 3 35 36 32 4 2 50 3 35 47 23 3 1 25 4 21 46 29 4 1 47 2 33 43 35 1 2 47 2 40 53 20 2 1 41 3 22 55 28 2 2 45 2 35 39 26 4 2 41 4 20 55 36 3 2 45 5 28 41 26 4 2 40 3 46 33 33 3 1 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	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Goodness of Fit
Correlation	0.9374
R-squared	0.8787
RMSE	4.2336

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	4	3.63013698630137	0.36986301369863
2	4	3.63013698630137	0.36986301369863
3	5	3.63013698630137	1.36986301369863
4	2	3.63013698630137	-1.63013698630137
5	3	3.63013698630137	-0.63013698630137
6	5	3.63013698630137	1.36986301369863
7	4	3.63013698630137	0.36986301369863
8	4	3.63013698630137	0.36986301369863
9	4	3.63013698630137	0.36986301369863
10	4	3.63013698630137	0.36986301369863
11	5	3.63013698630137	1.36986301369863
12	4	3.63013698630137	0.36986301369863
13	3	3.63013698630137	-0.63013698630137
14	4	3.63013698630137	0.36986301369863
15	3	3.63013698630137	-0.63013698630137
16	3	3.63013698630137	-0.63013698630137
17	4	3.63013698630137	0.36986301369863
18	3	3.63013698630137	-0.63013698630137
19	4	3.63013698630137	0.36986301369863
20	2	3.63013698630137	-1.63013698630137
21	4	3.63013698630137	0.36986301369863
22	3	3.63013698630137	-0.63013698630137
23	4	3.63013698630137	0.36986301369863
24	4	3.63013698630137	0.36986301369863
25	3	3.63013698630137	-0.63013698630137
26	3	3.63013698630137	-0.63013698630137
27	2	3.63013698630137	-1.63013698630137
28	4	3.63013698630137	0.36986301369863
29	5	3.63013698630137	1.36986301369863
30	4	1.54545454545455	2.45454545454545
31	1	1.54545454545455	-0.545454545454545
32	1	1.54545454545455	-0.545454545454545
33	1	1.54545454545455	-0.545454545454545
34	1	1.54545454545455	-0.545454545454545
35	1	1.54545454545455	-0.545454545454545
36	2	1.54545454545455	0.454545454545455
37	1	1.54545454545455	-0.545454545454545
38	2	1.54545454545455	0.454545454545455
39	2	1.54545454545455	0.454545454545455
40	1	1.54545454545455	-0.545454545454545
41	48	40.1428571428571	7.85714285714285
42	48	40.1428571428571	7.85714285714285
43	26	40.1428571428571	-14.1428571428571
44	46	40.1428571428571	5.85714285714285
45	3	3.63013698630137	-0.63013698630137
46	3	3.63013698630137	-0.63013698630137
47	4	3.63013698630137	0.36986301369863
48	2	3.63013698630137	-1.63013698630137
49	2	3.63013698630137	-1.63013698630137
50	3	3.63013698630137	-0.63013698630137
51	2	3.63013698630137	-1.63013698630137
52	4	3.63013698630137	0.36986301369863
53	5	3.63013698630137	1.36986301369863
54	3	3.63013698630137	-0.63013698630137
55	4	3.63013698630137	0.36986301369863
56	5	3.63013698630137	1.36986301369863
57	5	3.63013698630137	1.36986301369863
58	3	3.63013698630137	-0.63013698630137
59	4	3.63013698630137	0.36986301369863
60	3	3.63013698630137	-0.63013698630137
61	3	3.63013698630137	-0.63013698630137
62	2	3.63013698630137	-1.63013698630137
63	3	3.63013698630137	-0.63013698630137
64	4	3.63013698630137	0.36986301369863
65	4	3.63013698630137	0.36986301369863
66	4	3.63013698630137	0.36986301369863
67	4	3.63013698630137	0.36986301369863
68	3	3.63013698630137	-0.63013698630137
69	3	3.63013698630137	-0.63013698630137
70	3	3.63013698630137	-0.63013698630137
71	2	3.63013698630137	-1.63013698630137
72	3	3.63013698630137	-0.63013698630137
73	3	3.63013698630137	-0.63013698630137
74	3	3.63013698630137	-0.63013698630137
75	3	3.63013698630137	-0.63013698630137
76	5	3.63013698630137	1.36986301369863
77	3	3.63013698630137	-0.63013698630137
78	5	3.63013698630137	1.36986301369863
79	4	3.63013698630137	0.36986301369863
80	4	3.63013698630137	0.36986301369863
81	4	3.63013698630137	0.36986301369863
82	5	3.63013698630137	1.36986301369863
83	4	3.63013698630137	0.36986301369863
84	5	3.63013698630137	1.36986301369863
85	3	3.63013698630137	-0.63013698630137
86	3	3.63013698630137	-0.63013698630137
87	2	3.63013698630137	-1.63013698630137
88	3	3.63013698630137	-0.63013698630137
89	4	3.63013698630137	0.36986301369863
90	5	3.63013698630137	1.36986301369863
91	5	3.63013698630137	1.36986301369863
92	3	3.63013698630137	-0.63013698630137
93	2	3.63013698630137	-1.63013698630137
94	3	3.63013698630137	-0.63013698630137
95	4	3.63013698630137	0.36986301369863
96	1	3.63013698630137	-2.63013698630137
97	4	3.63013698630137	0.36986301369863
98	3	3.63013698630137	-0.63013698630137
99	3	3.63013698630137	-0.63013698630137
100	4	3.63013698630137	0.36986301369863
101	3	3.63013698630137	-0.63013698630137
102	4	3.63013698630137	0.36986301369863
103	2	3.63013698630137	-1.63013698630137
104	3	3.63013698630137	-0.63013698630137
105	3	3.63013698630137	-0.63013698630137
106	3	3.63013698630137	-0.63013698630137
107	2	3.63013698630137	-1.63013698630137
108	5	3.63013698630137	1.36986301369863
109	5	3.63013698630137	1.36986301369863
110	4	3.63013698630137	0.36986301369863
111	2	3.63013698630137	-1.63013698630137
112	3	3.63013698630137	-0.63013698630137
113	3	3.63013698630137	-0.63013698630137
114	3	3.63013698630137	-0.63013698630137
115	4	3.63013698630137	0.36986301369863
116	5	3.63013698630137	1.36986301369863
117	4	3.63013698630137	0.36986301369863
118	22	29.5	-7.5
119	16	29.5	-13.5
120	36	29.5	6.5
121	35	40.1428571428571	-5.14285714285715
122	25	29.5	-4.5
123	27	40.1428571428571	-13.1428571428571
124	32	29.5	2.5
125	36	29.5	6.5
126	51	40.1428571428571	10.8571428571429
127	30	29.5	0.5
128	20	29.5	-9.5
129	29	29.5	-0.5
130	26	29.5	-3.5
131	20	29.5	-9.5
132	40	29.5	10.5
133	29	29.5	-0.5
134	32	29.5	2.5
135	33	29.5	3.5
136	32	29.5	2.5
137	34	29.5	4.5
138	24	29.5	-5.5
139	25	29.5	-4.5
140	41	29.5	11.5
141	39	29.5	9.5
142	21	29.5	-8.5
143	38	29.5	8.5
144	28	29.5	-1.5
145	37	3.63013698630137	33.3698630136986
146	46	31.7142857142857	14.2857142857143
147	39	31.7142857142857	7.28571428571428
148	21	31.7142857142857	-10.7142857142857
149	31	31.7142857142857	-0.714285714285715
150	25	31.7142857142857	-6.71428571428572
151	29	31.7142857142857	-2.71428571428572
152	31	31.7142857142857	-0.714285714285715
153	3	3.63013698630137	-0.63013698630137
154	4	3.63013698630137	0.36986301369863
155	1	3.63013698630137	-2.63013698630137
156	1	3.63013698630137	-2.63013698630137
157	5	3.63013698630137	1.36986301369863
158	4	3.63013698630137	0.36986301369863
159	3	3.63013698630137	-0.63013698630137
160	3	3.63013698630137	-0.63013698630137
161	4	3.63013698630137	0.36986301369863
162	3	3.63013698630137	-0.63013698630137
163	2	3.63013698630137	-1.63013698630137
164	1	3.63013698630137	-2.63013698630137
165	1	3.63013698630137	-2.63013698630137
166	5	3.63013698630137	1.36986301369863
167	4	3.63013698630137	0.36986301369863
168	3	3.63013698630137	-0.63013698630137
169	4	3.63013698630137	0.36986301369863
170	5	3.63013698630137	1.36986301369863
171	4	3.63013698630137	0.36986301369863
172	4	3.63013698630137	0.36986301369863
173	2	3.63013698630137	-1.63013698630137
174	3	3.63013698630137	-0.63013698630137
175	4	3.63013698630137	0.36986301369863
176	3	3.63013698630137	-0.63013698630137
177	4	3.63013698630137	0.36986301369863
178	3	3.63013698630137	-0.63013698630137
179	4	3.63013698630137	0.36986301369863
180	1	3.63013698630137	-2.63013698630137
181	2	3.63013698630137	-1.63013698630137
182	3	3.63013698630137	-0.63013698630137
183	3	3.63013698630137	-0.63013698630137
184	5	3.63013698630137	1.36986301369863
185	4	3.63013698630137	0.36986301369863
186	3	3.63013698630137	-0.63013698630137
187	3	3.63013698630137	-0.63013698630137
188	3	3.63013698630137	-0.63013698630137
189	3	3.63013698630137	-0.63013698630137
190	4	3.63013698630137	0.36986301369863
191	3	3.63013698630137	-0.63013698630137
192	2	3.63013698630137	-1.63013698630137
193	4	3.63013698630137	0.36986301369863
194	2	3.63013698630137	-1.63013698630137
195	4	3.63013698630137	0.36986301369863

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293188725f8yobs3p820i6tr/2b6z71293188850.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293188725f8yobs3p820i6tr/2b6z71293188850.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293188725f8yobs3p820i6tr/3b6z71293188850.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293188725f8yobs3p820i6tr/3b6z71293188850.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293188725f8yobs3p820i6tr/4lgga1293188850.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293188725f8yobs3p820i6tr/4lgga1293188850.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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