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*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, 20 Dec 2010 13:21:40 +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/20/t1292851163kav42jf9jev7i29.htm/, Retrieved Mon, 20 Dec 2010 14:19:23 +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/20/t1292851163kav42jf9jev7i29.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 5 2 3 3 4 4 2 4 2 4 3 4 4 4 4 2 4 2 5 4 2 4 2 2 2 2 4 3 2 2 2 3 2 4 4 5 1 3 2 4 5 3 5 1 2 1 4 4 3 4 3 3 3 4 3 3 3 2 3 2 4 4 2 4 1 3 2 2 4 4 4 4 3 3 3 4 4 2 2 4 2 4 4 3 3 3 2 2 3 4 3 3 2 2 2 4 2 4 4 1 1 3 4 3 4 5 1 1 1 4 4 3 4 2 3 3 4 3 3 2 2 2 2 2 2 3 4 2 2 3 4 4 4 4 2 3 4 4 3 2 4 1 4 2 4 3 5 4 2 4 3 3 4 4 4 4 3 5 2 3 2 4 2 2 2 4 3 3 5 2 3 2 2 4 4 4 2 4 3 3 4 4 4 2 3 2 4 4 3 4 2 2 2 3 4 4 4 3 1 2 4 4 4 4 2 3 2 4 4 1 4 1 2 3 4 5 4 4 4 4 4 4 4 5 2 1 4 1 4 4 2 4 2 5 3 4 4 4 4 2 2 3 4 3 3 5 2 4 2 5 4 2 5 2 4 1 4 3 4 4 2 2 1 2 4 5 3 2 4 2 4 4 4 4 2 4 2 4 3 4 5 2 2 2 5 5 4 4 2 3 1 4 4 3 4 2 2 2 2 3 4 5 2 4 1 4 3 2 4 2 3 2 4 3 2 5 1 1 2 4 4 4 4 2 2 4 2 4 2 4 1 5 2 5 4 4 4 2 2 2 4 4 4 3 1 4 2 4 4 1 4 1 4 1 4 4 4 4 2 2 2 4 4 2 4 2 2 2 4 5 1 2 1 2 1 3 3 4 3 5 4 5 5 3 3 5 2 3 2 4 5 2 4 2 4 2 4 5 4 4 1 2 2 4 4 3 5 1 3 1 4 4 2 3 2 2 3 2 3 2 5 2 2 1 4 4 3 4 1 3 1 4 4 2 5 1 2 2 4 5 1 4 2 3 3 4 4 3 4 1 2 2 3 4 2 5 1 4 2 4 5 3 4 2 2 2 2 4 3 4 1 5 4 4 3 3 5 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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.3717
R-squared	0.1382
RMSE	0.7361

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	4	4.1	-0.0999999999999996
2	4	3.63157894736842	0.368421052631579
3	4	3.63157894736842	0.368421052631579
4	4	3.63157894736842	0.368421052631579
5	4	2.93333333333333	1.06666666666667
6	5	4.1	0.9
7	4	4.1	-0.0999999999999996
8	3	3.63157894736842	-0.631578947368421
9	4	3.63157894736842	0.368421052631579
10	4	3.63157894736842	0.368421052631579
11	4	3.63157894736842	0.368421052631579
12	4	2.93333333333333	1.06666666666667
13	4	3.63157894736842	0.368421052631579
14	2	3.63157894736842	-1.63157894736842
15	3	3.63157894736842	-0.631578947368421
16	4	4.1	-0.0999999999999996
17	3	3.63157894736842	-0.631578947368421
18	2	2.93333333333333	-0.933333333333333
19	4	3.63157894736842	0.368421052631579
20	3	3.63157894736842	-0.631578947368421
21	3	3.63157894736842	-0.631578947368421
22	4	3.63157894736842	0.368421052631579
23	3	3.63157894736842	-0.631578947368421
24	3	3.63157894736842	-0.631578947368421
25	4	4.1	-0.0999999999999996
26	4	3.63157894736842	0.368421052631579
27	4	3.63157894736842	0.368421052631579
28	4	3.63157894736842	0.368421052631579
29	4	3.63157894736842	0.368421052631579
30	4	3.63157894736842	0.368421052631579
31	5	3.63157894736842	1.36842105263158
32	4	3.63157894736842	0.368421052631579
33	4	2.93333333333333	1.06666666666667
34	4	3.63157894736842	0.368421052631579
35	3	3.63157894736842	-0.631578947368421
36	4	4.1	-0.0999999999999996
37	3	4.1	-1.1
38	4	3.63157894736842	0.368421052631579
39	4	3.63157894736842	0.368421052631579
40	3	3.63157894736842	-0.631578947368421
41	5	4.1	0.9
42	4	3.63157894736842	0.368421052631579
43	3	3.63157894736842	-0.631578947368421
44	3	4.1	-1.1
45	3	3.63157894736842	-0.631578947368421
46	4	4.1	-0.0999999999999996
47	4	3.63157894736842	0.368421052631579
48	4	3.63157894736842	0.368421052631579
49	4	3.63157894736842	0.368421052631579
50	4	3.63157894736842	0.368421052631579
51	4	3.63157894736842	0.368421052631579
52	4	3.63157894736842	0.368421052631579
53	5	3.63157894736842	1.36842105263158
54	3	2.93333333333333	0.0666666666666669
55	3	3.63157894736842	-0.631578947368421
56	5	4.1	0.9
57	5	3.63157894736842	1.36842105263158
58	4	3.63157894736842	0.368421052631579
59	4	4.1	-0.0999999999999996
60	3	3.63157894736842	-0.631578947368421
61	4	4.1	-0.0999999999999996
62	4	3.63157894736842	0.368421052631579
63	5	4.1	0.9
64	4	3.63157894736842	0.368421052631579
65	4	3.63157894736842	0.368421052631579
66	5	4.1	0.9
67	4	3.63157894736842	0.368421052631579
68	3	3.63157894736842	-0.631578947368421
69	4	4.1	-0.0999999999999996
70	4	3.63157894736842	0.368421052631579
71	4	3.63157894736842	0.368421052631579
72	4	3.63157894736842	0.368421052631579
73	4	4.1	-0.0999999999999996
74	4	4.1	-0.0999999999999996
75	4	4.1	-0.0999999999999996
76	3	3.63157894736842	-0.631578947368421
77	4	3.63157894736842	0.368421052631579
78	3	4.1	-1.1
79	5	4.1	0.9
80	4	3.63157894736842	0.368421052631579
81	5	4.1	0.9
82	5	3.63157894736842	1.36842105263158
83	4	3.63157894736842	0.368421052631579
84	4	3.63157894736842	0.368421052631579
85	4	3.63157894736842	0.368421052631579
86	4	3.63157894736842	0.368421052631579
87	4	3.63157894736842	0.368421052631579
88	5	4.1	0.9
89	4	3.63157894736842	0.368421052631579
90	3	3.63157894736842	-0.631578947368421
91	4	3.63157894736842	0.368421052631579
92	3	4.1	-1.1
93	4	3.63157894736842	0.368421052631579
94	4	3.63157894736842	0.368421052631579
95	4	4.1	-0.0999999999999996
96	4	4.1	-0.0999999999999996
97	4	3.63157894736842	0.368421052631579
98	3	3.63157894736842	-0.631578947368421
99	3	3.63157894736842	-0.631578947368421
100	3	3.63157894736842	-0.631578947368421
101	3	3.63157894736842	-0.631578947368421
102	3	3.63157894736842	-0.631578947368421
103	2	3.63157894736842	-1.63157894736842
104	3	3.63157894736842	-0.631578947368421
105	5	4.1	0.9
106	2	2.93333333333333	-0.933333333333333
107	2	3.63157894736842	-1.63157894736842
108	3	2.93333333333333	0.0666666666666669
109	3	3.63157894736842	-0.631578947368421
110	3	4.1	-1.1
111	4	3.63157894736842	0.368421052631579
112	4	3.63157894736842	0.368421052631579
113	3	3.63157894736842	-0.631578947368421
114	2	3.63157894736842	-1.63157894736842
115	3	3.63157894736842	-0.631578947368421
116	4	3.63157894736842	0.368421052631579
117	2	2.93333333333333	-0.933333333333333
118	4	2.93333333333333	1.06666666666667
119	4	3.63157894736842	0.368421052631579
120	2	3.63157894736842	-1.63157894736842
121	3	3.63157894736842	-0.631578947368421
122	3	3.63157894736842	-0.631578947368421
123	3	3.63157894736842	-0.631578947368421
124	4	4.1	-0.0999999999999996
125	4	3.63157894736842	0.368421052631579
126	4	3.63157894736842	0.368421052631579
127	3	3.63157894736842	-0.631578947368421
128	4	3.63157894736842	0.368421052631579
129	4	3.63157894736842	0.368421052631579
130	4	3.63157894736842	0.368421052631579
131	2	3.63157894736842	-1.63157894736842
132	4	3.63157894736842	0.368421052631579
133	5	3.63157894736842	1.36842105263158
134	4	3.63157894736842	0.368421052631579
135	4	3.63157894736842	0.368421052631579
136	4	3.63157894736842	0.368421052631579
137	3	3.63157894736842	-0.631578947368421
138	1	3.63157894736842	-2.63157894736842
139	4	3.63157894736842	0.368421052631579
140	3	2.93333333333333	0.0666666666666669
141	3	2.93333333333333	0.0666666666666669
142	3	3.63157894736842	-0.631578947368421
143	1	2.93333333333333	-1.93333333333333
144	4	3.63157894736842	0.368421052631579
145	5	3.63157894736842	1.36842105263158
146	4	3.63157894736842	0.368421052631579
147	3	3.63157894736842	-0.631578947368421
148	4	2.93333333333333	1.06666666666667
149	3	2.93333333333333	0.0666666666666669
150	4	3.63157894736842	0.368421052631579
151	4	3.63157894736842	0.368421052631579
152	4	3.63157894736842	0.368421052631579
153	5	3.63157894736842	1.36842105263158
154	2	2.93333333333333	-0.933333333333333
155	3	4.1	-1.1
156	3	3.63157894736842	-0.631578947368421
157	4	3.63157894736842	0.368421052631579
158	4	3.63157894736842	0.368421052631579
159	3	3.63157894736842	-0.631578947368421

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292851163kav42jf9jev7i29/27ib11292851292.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292851163kav42jf9jev7i29/27ib11292851292.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292851163kav42jf9jev7i29/37ib11292851292.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292851163kav42jf9jev7i29/37ib11292851292.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292851163kav42jf9jev7i29/4hsb41292851292.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292851163kav42jf9jev7i29/4hsb41292851292.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = no ;

Parameters (R input):

par1 = 7 ; 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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