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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: Wed, 22 Dec 2010 20:35:36 +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/t1293050013a591noaqjz92f2y.htm/, Retrieved Wed, 22 Dec 2010 21:33:34 +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/t1293050013a591noaqjz92f2y.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 «

5 4 3 2 12 4 4 3 2 11 5 5 5 2 15 2 2 2 1 6 4 5 4 2 13 3 3 4 2 10 5 3 4 2 12 5 5 4 2 14 4 3 5 2 12 3 3 NA 2 6 2 4 4 1 10 4 4 4 2 12 4 4 4 1 12 4 3 4 2 11 5 5 5 2 15 5 4 3 1 12 4 4 2 1 10 4 4 4 2 12 4 4 3 1 11 4 4 4 2 12 4 4 3 1 11 4 4 4 2 12 5 4 4 2 13 3 4 4 2 11 5 NA 4 1 9 4 4 5 2 13 4 3 3 1 10 5 5 4 2 14 4 4 4 2 12 3 4 3 1 10 4 4 4 2 12 4 2 2 1 8 4 3 3 2 10 4 4 4 2 12 4 4 4 1 12 2 2 3 1 7 3 NA 3 1 6 4 4 4 1 12 3 4 3 2 10 4 3 3 1 10 2 4 4 1 10 4 4 4 2 12 5 5 5 1 15 4 3 3 1 10 4 4 2 2 10 4 4 4 2 12 5 4 4 2 13 3 4 4 2 11 4 4 3 2 11 4 4 4 1 12 5 5 4 2 14 3 3 4 1 10 4 4 4 1 12 5 4 4 2 13 2 1 2 1 5 2 2 2 2 6 4 4 4 2 12 4 4 4 2 12 4 3 4 1 11 4 3 3 2 10 2 2 3 1 7 4 4 4 1 12 5 5 4 2 14 3 4 4 2 11 4 4 4 2 12 5 4 4 1 13 5 5 4 2 14 4 4 3 1 11 4 4 4 2 12 4 4 4 1 12 2 3 3 1 8 3 4 4 2 11 5 5 4 2 14 4 5 5 1 14 4 4 4 1 12 3 2 4 2 9 4 4 5 2 13 3 4 4 2 11 4 4 4 1 12 4 4 4 1 12 4 4 4 1 12 4 4 4 1 12 4 4 4 2 12 4 4 4 1 12 4 4 3 2 11 3 4 3 2 10 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	0.9134
R-squared	0.8343
RMSE	0.8692

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	12	12.8333333333333	-0.833333333333334
2	11	10.8571428571429	0.142857142857142
3	15	14.1052631578947	0.894736842105264
4	6	7.3125	-1.3125
5	13	12.1041666666667	0.895833333333334
6	10	10.0555555555556	-0.0555555555555554
7	12	10.0555555555556	1.94444444444444
8	14	14.1052631578947	-0.105263157894736
9	12	10.0555555555556	1.94444444444444
10	6	10.0555555555556	-4.05555555555556
11	10	10.6	-0.6
12	12	12.1041666666667	-0.104166666666666
13	12	12.1041666666667	-0.104166666666666
14	11	10.0555555555556	0.944444444444445
15	15	14.1052631578947	0.894736842105264
16	12	12.8333333333333	-0.833333333333334
17	10	10.8571428571429	-0.857142857142858
18	12	12.1041666666667	-0.104166666666666
19	11	10.8571428571429	0.142857142857142
20	12	12.1041666666667	-0.104166666666666
21	11	10.8571428571429	0.142857142857142
22	12	12.1041666666667	-0.104166666666666
23	13	12.8333333333333	0.166666666666666
24	11	10.6	0.4
25	9	14.1052631578947	-5.10526315789474
26	13	12.1041666666667	0.895833333333334
27	10	10.0555555555556	-0.0555555555555554
28	14	14.1052631578947	-0.105263157894736
29	12	12.1041666666667	-0.104166666666666
30	10	8.88888888888889	1.11111111111111
31	12	12.1041666666667	-0.104166666666666
32	8	8.44444444444444	-0.444444444444445
33	10	10.0555555555556	-0.0555555555555554
34	12	12.1041666666667	-0.104166666666666
35	12	12.1041666666667	-0.104166666666666
36	7	7.3125	-0.3125
37	6	8.88888888888889	-2.88888888888889
38	12	12.1041666666667	-0.104166666666666
39	10	8.88888888888889	1.11111111111111
40	10	10.0555555555556	-0.0555555555555554
41	10	10.6	-0.6
42	12	12.1041666666667	-0.104166666666666
43	15	14.1052631578947	0.894736842105264
44	10	10.0555555555556	-0.0555555555555554
45	10	10.8571428571429	-0.857142857142858
46	12	12.1041666666667	-0.104166666666666
47	13	12.8333333333333	0.166666666666666
48	11	10.6	0.4
49	11	10.8571428571429	0.142857142857142
50	12	12.1041666666667	-0.104166666666666
51	14	14.1052631578947	-0.105263157894736
52	10	10.0555555555556	-0.0555555555555554
53	12	12.1041666666667	-0.104166666666666
54	13	12.8333333333333	0.166666666666666
55	5	7.3125	-2.3125
56	6	7.3125	-1.3125
57	12	12.1041666666667	-0.104166666666666
58	12	12.1041666666667	-0.104166666666666
59	11	10.0555555555556	0.944444444444445
60	10	10.0555555555556	-0.0555555555555554
61	7	7.3125	-0.3125
62	12	12.1041666666667	-0.104166666666666
63	14	14.1052631578947	-0.105263157894736
64	11	10.6	0.4
65	12	12.1041666666667	-0.104166666666666
66	13	12.8333333333333	0.166666666666666
67	14	14.1052631578947	-0.105263157894736
68	11	10.8571428571429	0.142857142857142
69	12	12.1041666666667	-0.104166666666666
70	12	12.1041666666667	-0.104166666666666
71	8	7.3125	0.6875
72	11	10.6	0.4
73	14	14.1052631578947	-0.105263157894736
74	14	12.1041666666667	1.89583333333333
75	12	12.1041666666667	-0.104166666666666
76	9	10.0555555555556	-1.05555555555556
77	13	12.1041666666667	0.895833333333334
78	11	10.6	0.4
79	12	12.1041666666667	-0.104166666666666
80	12	12.1041666666667	-0.104166666666666
81	12	12.1041666666667	-0.104166666666666
82	12	12.1041666666667	-0.104166666666666
83	12	12.1041666666667	-0.104166666666666
84	12	12.1041666666667	-0.104166666666666
85	11	10.8571428571429	0.142857142857142
86	10	8.88888888888889	1.11111111111111
87	9	8.44444444444444	0.555555555555555
88	12	12.1041666666667	-0.104166666666666
89	12	12.1041666666667	-0.104166666666666
90	12	12.1041666666667	-0.104166666666666
91	9	10.0555555555556	-1.05555555555556
92	15	14.1052631578947	0.894736842105264
93	12	12.1041666666667	-0.104166666666666
94	12	12.1041666666667	-0.104166666666666
95	12	12.1041666666667	-0.104166666666666
96	10	10.8571428571429	-0.857142857142858
97	13	12.8333333333333	0.166666666666666
98	9	8.44444444444444	0.555555555555555
99	12	12.1041666666667	-0.104166666666666
100	10	10.6	-0.6
101	14	14.1052631578947	-0.105263157894736
102	11	10.8571428571429	0.142857142857142
103	15	14.1052631578947	0.894736842105264
104	11	10.8571428571429	0.142857142857142
105	11	10.8571428571429	0.142857142857142
106	12	12.1041666666667	-0.104166666666666
107	12	12.1041666666667	-0.104166666666666
108	12	12.1041666666667	-0.104166666666666
109	11	10.0555555555556	0.944444444444445
110	7	8.88888888888889	-1.88888888888889
111	12	12.1041666666667	-0.104166666666666
112	14	14.1052631578947	-0.105263157894736
113	11	10.8571428571429	0.142857142857142
114	11	10.0555555555556	0.944444444444445
115	10	10.6	-0.6
116	13	12.8333333333333	0.166666666666666
117	13	14.1052631578947	-1.10526315789474
118	8	7.3125	0.6875
119	11	10.8571428571429	0.142857142857142
120	12	12.1041666666667	-0.104166666666666
121	11	10.8571428571429	0.142857142857142
122	13	12.8333333333333	0.166666666666666
123	12	12.1041666666667	-0.104166666666666
124	14	14.1052631578947	-0.105263157894736
125	13	12.8333333333333	0.166666666666666
126	15	14.1052631578947	0.894736842105264
127	10	10.0555555555556	-0.0555555555555554
128	11	10.8571428571429	0.142857142857142
129	9	8.44444444444444	0.555555555555555
130	11	10.8571428571429	0.142857142857142
131	10	8.88888888888889	1.11111111111111
132	11	10.8571428571429	0.142857142857142
133	8	8.44444444444444	-0.444444444444445
134	11	10.8571428571429	0.142857142857142
135	12	12.1041666666667	-0.104166666666666
136	12	12.1041666666667	-0.104166666666666
137	9	8.44444444444444	0.555555555555555
138	11	10.8571428571429	0.142857142857142
139	10	8.88888888888889	1.11111111111111
140	8	7.3125	0.6875
141	9	7.3125	1.6875
142	8	8.44444444444444	-0.444444444444445
143	9	8.88888888888889	0.111111111111111
144	15	14.1052631578947	0.894736842105264
145	11	10.6	0.4
146	8	8.88888888888889	-0.88888888888889
147	13	12.8333333333333	0.166666666666666
148	12	12.1041666666667	-0.104166666666666
149	12	12.1041666666667	-0.104166666666666
150	9	7.3125	1.6875
151	7	8.44444444444444	-1.44444444444444
152	13	12.8333333333333	0.166666666666666
153	9	10.0555555555556	-1.05555555555556
154	6	7.3125	-1.3125
155	8	7.3125	0.6875
156	8	7.3125	0.6875
157	15	14.1052631578947	0.894736842105264
158	6	7.3125	-1.3125
159	9	8.44444444444444	0.555555555555555
160	11	10.8571428571429	0.142857142857142
161	8	7.3125	0.6875
162	8	7.3125	0.6875

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293050013a591noaqjz92f2y/26ais1293050129.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293050013a591noaqjz92f2y/26ais1293050129.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293050013a591noaqjz92f2y/36ais1293050129.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293050013a591noaqjz92f2y/36ais1293050129.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293050013a591noaqjz92f2y/4h2zv1293050129.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293050013a591noaqjz92f2y/4h2zv1293050129.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 5 ; par2 = none ; 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')

}

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Software written by Ed van Stee & Patrick Wessa

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