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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, 29 Dec 2010 17:53:59 +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/29/t1293645137gjnb40ganp6ze06.htm/, Retrieved Wed, 29 Dec 2010 18:52:17 +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/29/t1293645137gjnb40ganp6ze06.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 «

18 1 27 5 26 49 35 10 1 36 4 25 45 34 23 1 25 4 17 54 13 14 1 27 3 37 36 35 20 2 25 3 35 36 28 15 2 44 3 15 53 32 18 1 50 4 27 46 35 19 1 41 4 36 42 36 19 1 48 5 25 41 27 14 2 43 4 30 45 29 15 2 47 2 27 47 27 14 2 41 3 33 42 28 16 1 44 2 29 45 29 13 2 47 5 30 40 28 13 2 40 3 25 45 30 14 2 46 3 23 40 25 23 1 28 3 26 42 15 17 1 56 3 24 45 33 14 2 49 4 35 47 31 21 2 25 4 39 31 37 15 2 41 4 23 46 37 19 2 26 3 32 34 34 20 1 50 5 29 43 32 18 1 47 4 26 45 21 13 1 52 2 21 42 25 20 2 37 5 35 51 32 12 2 41 3 23 44 28 17 1 45 4 21 47 22 13 2 26 4 28 47 25 17 1 NA 3 30 41 26 16 1 52 4 21 44 34 20 1 46 2 29 51 34 18 1 58 3 28 46 36 9 1 54 5 19 47 36 14 1 29 3 26 46 26 12 2 50 3 33 38 26 21 1 43 2 34 50 34 16 2 30 3 33 48 33 12 2 47 2 40 36 31 20 1 45 3 24 51 33 18 2 48 1 35 35 22 22 2 48 3 35 49 29 17 2 26 4 32 38 24 16 1 46 5 20 47 37 14 2 NA 3 35 36 32 19 2 50 3 35 47 23 21 1 25 4 21 46 29 18 1 47 2 33 43 35 23 2 47 2 40 53 20 20 1 41 3 22 55 28 10 2 45 2 35 39 26 16 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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.2781
R-squared	0.0774
RMSE	3.3877

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	18	18.125	-0.125
2	10	15.9568345323741	-5.9568345323741
3	23	18.125	4.875
4	14	18.125	-4.125
5	20	18.125	1.875
6	15	15.9568345323741	-0.956834532374101
7	18	15.9568345323741	2.0431654676259
8	19	15.9568345323741	3.0431654676259
9	19	15.9568345323741	3.0431654676259
10	14	15.9568345323741	-1.9568345323741
11	15	15.9568345323741	-0.956834532374101
12	14	15.9568345323741	-1.9568345323741
13	16	15.9568345323741	0.043165467625899
14	13	15.9568345323741	-2.9568345323741
15	13	15.9568345323741	-2.9568345323741
16	14	15.9568345323741	-1.9568345323741
17	23	18.125	4.875
18	17	15.9568345323741	1.0431654676259
19	14	15.9568345323741	-1.9568345323741
20	21	18.125	2.875
21	15	15.9568345323741	-0.956834532374101
22	19	18.125	0.875
23	20	15.9568345323741	4.0431654676259
24	18	15.9568345323741	2.0431654676259
25	13	15.9568345323741	-2.9568345323741
26	20	15.9568345323741	4.0431654676259
27	12	15.9568345323741	-3.9568345323741
28	17	15.9568345323741	1.0431654676259
29	13	18.125	-5.125
30	17	15.9568345323741	1.0431654676259
31	16	15.9568345323741	0.043165467625899
32	20	15.9568345323741	4.0431654676259
33	18	15.9568345323741	2.0431654676259
34	9	15.9568345323741	-6.9568345323741
35	14	18.125	-4.125
36	12	15.9568345323741	-3.9568345323741
37	21	15.9568345323741	5.0431654676259
38	16	18.125	-2.125
39	12	15.9568345323741	-3.9568345323741
40	20	15.9568345323741	4.0431654676259
41	18	15.9568345323741	2.0431654676259
42	22	15.9568345323741	6.0431654676259
43	17	18.125	-1.125
44	16	15.9568345323741	0.043165467625899
45	14	15.9568345323741	-1.9568345323741
46	19	15.9568345323741	3.0431654676259
47	21	18.125	2.875
48	18	15.9568345323741	2.0431654676259
49	23	15.9568345323741	7.0431654676259
50	20	15.9568345323741	4.0431654676259
51	10	15.9568345323741	-5.9568345323741
52	16	15.9568345323741	0.043165467625899
53	18	15.9568345323741	2.0431654676259
54	12	15.9568345323741	-3.9568345323741
55	15	18.125	-3.125
56	19	18.125	0.875
57	11	15.9568345323741	-4.9568345323741
58	16	15.9568345323741	0.043165467625899
59	12	15.9568345323741	-3.9568345323741
60	18	15.9568345323741	2.0431654676259
61	14	15.9568345323741	-1.9568345323741
62	20	15.9568345323741	4.0431654676259
63	15	18.125	-3.125
64	17	18.125	-1.125
65	20	18.125	1.875
66	14	15.9568345323741	-1.9568345323741
67	16	15.9568345323741	0.043165467625899
68	15	15.9568345323741	-0.956834532374101
69	17	15.9568345323741	1.0431654676259
70	20	15.9568345323741	4.0431654676259
71	14	15.9568345323741	-1.9568345323741
72	20	15.9568345323741	4.0431654676259
73	20	18.125	1.875
74	15	15.9568345323741	-0.956834532374101
75	21	18.125	2.875
76	22	18.125	3.875
77	11	15.9568345323741	-4.9568345323741
78	20	18.125	1.875
79	17	15.9568345323741	1.0431654676259
80	19	18.125	0.875
81	17	18.125	-1.125
82	15	18.125	-3.125
83	20	18.125	1.875
84	12	15.9568345323741	-3.9568345323741
85	13	15.9568345323741	-2.9568345323741
86	18	15.9568345323741	2.0431654676259
87	19	15.9568345323741	3.0431654676259
88	13	18.125	-5.125
89	12	15.9568345323741	-3.9568345323741
90	16	15.9568345323741	0.043165467625899
91	21	18.125	2.875
92	19	18.125	0.875
93	19	15.9568345323741	3.0431654676259
94	12	15.9568345323741	-3.9568345323741
95	22	15.9568345323741	6.0431654676259
96	9	18.125	-9.125
97	9	15.9568345323741	-6.9568345323741
98	18	18.125	-0.125
99	14	15.9568345323741	-1.9568345323741
100	14	18.125	-4.125
101	23	18.125	4.875
102	19	18.125	0.875
103	24	15.9568345323741	8.0431654676259
104	12	15.9568345323741	-3.9568345323741
105	20	15.9568345323741	4.0431654676259
106	21	18.125	2.875
107	18	15.9568345323741	2.0431654676259
108	20	15.9568345323741	4.0431654676259
109	18	15.9568345323741	2.0431654676259
110	18	18.125	-0.125
111	17	15.9568345323741	1.0431654676259
112	18	15.9568345323741	2.0431654676259
113	14	18.125	-4.125
114	23	15.9568345323741	7.0431654676259
115	19	15.9568345323741	3.0431654676259
116	14	15.9568345323741	-1.9568345323741
117	17	15.9568345323741	1.0431654676259
118	22	18.125	3.875
119	10	15.9568345323741	-5.9568345323741
120	16	15.9568345323741	0.043165467625899
121	14	15.9568345323741	-1.9568345323741
122	19	15.9568345323741	3.0431654676259
123	14	15.9568345323741	-1.9568345323741
124	18	15.9568345323741	2.0431654676259
125	19	15.9568345323741	3.0431654676259
126	21	15.9568345323741	5.0431654676259
127	13	15.9568345323741	-2.9568345323741
128	17	15.9568345323741	1.0431654676259
129	11	18.125	-7.125
130	16	15.9568345323741	0.043165467625899
131	22	18.125	3.875
132	19	15.9568345323741	3.0431654676259
133	17	15.9568345323741	1.0431654676259
134	25	15.9568345323741	9.0431654676259
135	17	15.9568345323741	1.0431654676259
136	23	18.125	4.875
137	21	18.125	2.875
138	12	15.9568345323741	-3.9568345323741
139	18	15.9568345323741	2.0431654676259
140	15	18.125	-3.125
141	17	15.9568345323741	1.0431654676259
142	11	15.9568345323741	-4.9568345323741
143	17	15.9568345323741	1.0431654676259
144	13	15.9568345323741	-2.9568345323741
145	17	18.125	-1.125
146	16	15.9568345323741	0.043165467625899
147	14	15.9568345323741	-1.9568345323741
148	15	15.9568345323741	-0.956834532374101
149	20	15.9568345323741	4.0431654676259
150	14	15.9568345323741	-1.9568345323741
151	16	15.9568345323741	0.043165467625899
152	14	15.9568345323741	-1.9568345323741
153	13	15.9568345323741	-2.9568345323741
154	15	15.9568345323741	-0.956834532374101
155	13	15.9568345323741	-2.9568345323741
156	13	15.9568345323741	-2.9568345323741
157	23	18.125	4.875
158	18	15.9568345323741	2.0431654676259
159	21	18.125	2.875
160	14	15.9568345323741	-1.9568345323741
161	12	15.9568345323741	-3.9568345323741
162	17	15.9568345323741	1.0431654676259
163	11	15.9568345323741	-4.9568345323741
164	15	18.125	-3.125
165	14	15.9568345323741	-1.9568345323741
166	19	18.125	0.875
167	12	15.9568345323741	-3.9568345323741
168	14	18.125	-4.125
169	18	18.125	-0.125
170	25	18.125	6.875
171	22	18.125	3.875
172	15	15.9568345323741	-0.956834532374101
173	18	15.9568345323741	2.0431654676259
174	18	15.9568345323741	2.0431654676259
175	12	15.9568345323741	-3.9568345323741
176	12	15.9568345323741	-3.9568345323741
177	16	15.9568345323741	0.043165467625899
178	22	15.9568345323741	6.0431654676259
179	15	15.9568345323741	-0.956834532374101
180	16	15.9568345323741	0.043165467625899
181	11	15.9568345323741	-4.9568345323741
182	20	15.9568345323741	4.0431654676259
183	14	15.9568345323741	-1.9568345323741
184	20	15.9568345323741	4.0431654676259
185	15	15.9568345323741	-0.956834532374101
186	12	15.9568345323741	-3.9568345323741
187	18	18.125	-0.125
188	18	15.9568345323741	2.0431654676259
189	11	18.125	-7.125
190	13	18.125	-5.125
191	15	15.9568345323741	-0.956834532374101
192	19	18.125	0.875
193	13	15.9568345323741	-2.9568345323741
194	19	15.9568345323741	3.0431654676259
195	18	18.125	-0.125

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293645137gjnb40ganp6ze06/2ypyy1293645230.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293645137gjnb40ganp6ze06/2ypyy1293645230.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293645137gjnb40ganp6ze06/3ypyy1293645230.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293645137gjnb40ganp6ze06/3ypyy1293645230.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293645137gjnb40ganp6ze06/4ryfj1293645230.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293645137gjnb40ganp6ze06/4ryfj1293645230.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;

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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