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RECURSIVE PARTITIONING - NO CAT

*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: Sun, 12 Dec 2010 19:03:25 +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/12/t12921805411h5wtb9vtqf63tz.htm/, Retrieved Sun, 12 Dec 2010 20:02:24 +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/12/t12921805411h5wtb9vtqf63tz.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 «

1 3 4 4 2 1 3 2 2 2 1 3 5 5 4 1 5 4 5 3 2 3 1 1 2 1 2 2 4 1 4 3 5 6 4 1 2 1 5 3 1 2 3 4 1 2 3 5 5 4 1 7 2 7 4 1 4 2 2 4 2 6 2 7 3 1 2 2 5 4 1 4 1 5 1 1 4 4 7 4 1 2 3 3 1 1 6 6 6 4 1 1 1 2 4 2 3 3 6 3 1 2 2 1 2 2 5 5 5 6 1 3 5 4 5 2 5 3 4 4 1 1 3 7 6 1 7 5 7 1 1 2 5 5 2 2 5 4 6 4 1 5 2 5 4 1 1 1 1 1 2 4 4 6 2 1 5 6 4 1 1 2 2 2 2 1 1 3 2 2 1 5 2 6 2 2 7 4 6 6 1 4 2 6 2 1 1 1 1 1 1 5 5 6 4 1 5 5 6 3 1 1 1 1 3 1 6 1 1 1 1 5 2 7 4 1 5 4 2 3 1 3 5 3 4 1 4 3 5 3 1 4 3 3 2 1 4 1 4 1 1 5 2 2 5 1 3 3 3 4 2 2 2 7 1 2 6 5 7 2 1 1 4 5 4 1 4 4 1 3 1 3 2 2 2 2 3 3 5 3 1 6 6 2 3 1 3 2 4 2 2 5 3 7 2 1 2 2 2 4 1 4 5 5 4 1 3 5 6 2 1 2 5 3 2 1 6 6 7 5 2 5 4 4 4 1 5 2 3 5 1 4 5 5 5 2 1 2 3 2 1 3 1 2 3 1 4 6 6 4 1 2 6 6 2 1 5 3 5 2 1 2 4 2 2 3 4 5 3 5 2 2 2 4 2 2 5 4 6 3 1 3 3 5 2 1 1 2 2 2 1 5 2 5 2 1 2 3 2 2 1 2 3 1 2 1 2 7 2 1 1 5 2 4 3 1 5 2 5 3 1 2 2 5 3 1 4 5 3 3 1 2 1 2 1 3 6 5 7 4 1 1 2 1 1 1 1 1 5 1 1 4 2 5 1 1 2 2 2 3 1 4 0 6 2 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	12 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Goodness of Fit
Correlation	0.301
R-squared	0.0906
RMSE	0.5447

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	1	1.10975609756098	-0.109756097560976
2	1	1.10975609756098	-0.109756097560976
3	1	1.46296296296296	-0.462962962962963
4	1	1.10975609756098	-0.109756097560976
5	2	1.10975609756098	0.890243902439024
6	1	1.10975609756098	-0.109756097560976
7	4	1.46296296296296	2.53703703703704
8	1	1.10975609756098	-0.109756097560976
9	1	1.10975609756098	-0.109756097560976
10	2	1.46296296296296	0.537037037037037
11	1	1.46296296296296	-0.462962962962963
12	1	1.46296296296296	-0.462962962962963
13	2	1.42857142857143	0.571428571428571
14	1	1.46296296296296	-0.462962962962963
15	1	1.10975609756098	-0.109756097560976
16	1	1.46296296296296	-0.462962962962963
17	1	1.10975609756098	-0.109756097560976
18	1	1.46296296296296	-0.462962962962963
19	1	1.46296296296296	-0.462962962962963
20	2	1.42857142857143	0.571428571428571
21	1	1.10975609756098	-0.109756097560976
22	2	1.46296296296296	0.537037037037037
23	1	1.46296296296296	-0.462962962962963
24	2	1.46296296296296	0.537037037037037
25	1	1.46296296296296	-0.462962962962963
26	1	1.42857142857143	-0.428571428571429
27	1	1.10975609756098	-0.109756097560976
28	2	1.46296296296296	0.537037037037037
29	1	1.46296296296296	-0.462962962962963
30	1	1.10975609756098	-0.109756097560976
31	2	1.42857142857143	0.571428571428571
32	1	1.10975609756098	-0.109756097560976
33	1	1.10975609756098	-0.109756097560976
34	1	1.10975609756098	-0.109756097560976
35	1	1.42857142857143	-0.428571428571429
36	2	1.46296296296296	0.537037037037037
37	1	1.42857142857143	-0.428571428571429
38	1	1.10975609756098	-0.109756097560976
39	1	1.46296296296296	-0.462962962962963
40	1	1.42857142857143	-0.428571428571429
41	1	1.10975609756098	-0.109756097560976
42	1	1.10975609756098	-0.109756097560976
43	1	1.46296296296296	-0.462962962962963
44	1	1.10975609756098	-0.109756097560976
45	1	1.46296296296296	-0.462962962962963
46	1	1.10975609756098	-0.109756097560976
47	1	1.10975609756098	-0.109756097560976
48	1	1.10975609756098	-0.109756097560976
49	1	1.46296296296296	-0.462962962962963
50	1	1.46296296296296	-0.462962962962963
51	2	1.42857142857143	0.571428571428571
52	2	1.42857142857143	0.571428571428571
53	1	1.46296296296296	-0.462962962962963
54	1	1.10975609756098	-0.109756097560976
55	1	1.10975609756098	-0.109756097560976
56	2	1.10975609756098	0.890243902439024
57	1	1.10975609756098	-0.109756097560976
58	1	1.10975609756098	-0.109756097560976
59	2	1.42857142857143	0.571428571428571
60	1	1.46296296296296	-0.462962962962963
61	1	1.46296296296296	-0.462962962962963
62	1	1.42857142857143	-0.428571428571429
63	1	1.10975609756098	-0.109756097560976
64	1	1.46296296296296	-0.462962962962963
65	2	1.46296296296296	0.537037037037037
66	1	1.46296296296296	-0.462962962962963
67	1	1.46296296296296	-0.462962962962963
68	2	1.10975609756098	0.890243902439024
69	1	1.10975609756098	-0.109756097560976
70	1	1.46296296296296	-0.462962962962963
71	1	1.42857142857143	-0.428571428571429
72	1	1.10975609756098	-0.109756097560976
73	1	1.10975609756098	-0.109756097560976
74	3	1.46296296296296	1.53703703703704
75	2	1.10975609756098	0.890243902439024
76	2	1.42857142857143	0.571428571428571
77	1	1.10975609756098	-0.109756097560976
78	1	1.10975609756098	-0.109756097560976
79	1	1.10975609756098	-0.109756097560976
80	1	1.10975609756098	-0.109756097560976
81	1	1.10975609756098	-0.109756097560976
82	1	1.10975609756098	-0.109756097560976
83	1	1.10975609756098	-0.109756097560976
84	1	1.10975609756098	-0.109756097560976
85	1	1.10975609756098	-0.109756097560976
86	1	1.10975609756098	-0.109756097560976
87	1	1.10975609756098	-0.109756097560976
88	3	1.46296296296296	1.53703703703704
89	1	1.10975609756098	-0.109756097560976
90	1	1.10975609756098	-0.109756097560976
91	1	1.10975609756098	-0.109756097560976
92	1	1.10975609756098	-0.109756097560976
93	1	1.42857142857143	-0.428571428571429
94	1	1.10975609756098	-0.109756097560976
95	1	1.46296296296296	-0.462962962962963
96	1	1.10975609756098	-0.109756097560976
97	1	1.10975609756098	-0.109756097560976
98	1	1.10975609756098	-0.109756097560976
99	1	1.10975609756098	-0.109756097560976
100	2	1.46296296296296	0.537037037037037
101	1	1.46296296296296	-0.462962962962963
102	1	1.10975609756098	-0.109756097560976
103	1	1.10975609756098	-0.109756097560976
104	2	1.42857142857143	0.571428571428571
105	1	1.10975609756098	-0.109756097560976
106	2	1.46296296296296	0.537037037037037
107	2	1.10975609756098	0.890243902439024
108	3	1.46296296296296	1.53703703703704
109	2	1.10975609756098	0.890243902439024
110	2	1.42857142857143	0.571428571428571
111	1	1.46296296296296	-0.462962962962963
112	1	1.46296296296296	-0.462962962962963
113	1	1.10975609756098	-0.109756097560976
114	1	1.10975609756098	-0.109756097560976
115	1	1.46296296296296	-0.462962962962963
116	1	1.10975609756098	-0.109756097560976
117	1	1.10975609756098	-0.109756097560976
118	2	1.10975609756098	0.890243902439024
119	1	1.46296296296296	-0.462962962962963
120	1	1.42857142857143	-0.428571428571429
121	1	1.46296296296296	-0.462962962962963
122	1	1.10975609756098	-0.109756097560976
123	1	1.46296296296296	-0.462962962962963
124	1	1.10975609756098	-0.109756097560976
125	1	1.10975609756098	-0.109756097560976
126	1	1.10975609756098	-0.109756097560976
127	1	1.46296296296296	-0.462962962962963
128	2	1.10975609756098	0.890243902439024
129	2	1.10975609756098	0.890243902439024
130	1	1.10975609756098	-0.109756097560976
131	1	1.46296296296296	-0.462962962962963
132	1	1.46296296296296	-0.462962962962963
133	1	1.46296296296296	-0.462962962962963
134	4	1.46296296296296	2.53703703703704
135	1	1.46296296296296	-0.462962962962963
136	2	1.46296296296296	0.537037037037037
137	1	1.42857142857143	-0.428571428571429
138	1	1.46296296296296	-0.462962962962963
139	1	1.10975609756098	-0.109756097560976
140	1	1.42857142857143	-0.428571428571429
141	1	1.10975609756098	-0.109756097560976
142	1	1.10975609756098	-0.109756097560976
143	1	1.10975609756098	-0.109756097560976
144	1	1.10975609756098	-0.109756097560976
145	1	1.42857142857143	-0.428571428571429
146	1	1.10975609756098	-0.109756097560976
147	1	1.10975609756098	-0.109756097560976
148	2	1.46296296296296	0.537037037037037
149	1	1.10975609756098	-0.109756097560976
150	1	1.46296296296296	-0.462962962962963
151	3	1.46296296296296	1.53703703703704
152	1	1.10975609756098	-0.109756097560976
153	1	1.10975609756098	-0.109756097560976
154	1	1.10975609756098	-0.109756097560976
155	1	1.10975609756098	-0.109756097560976
156	2	1.46296296296296	0.537037037037037
157	1	1.42857142857143	-0.428571428571429

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921805411h5wtb9vtqf63tz/2zv381292180588.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921805411h5wtb9vtqf63tz/2zv381292180588.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921805411h5wtb9vtqf63tz/3r5lb1292180588.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921805411h5wtb9vtqf63tz/3r5lb1292180588.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921805411h5wtb9vtqf63tz/42w2e1292180588.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921805411h5wtb9vtqf63tz/42w2e1292180588.ps (open in new window)

Parameters (Session):

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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