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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: Fri, 10 Dec 2010 15:28:13 +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/10/t1291994784pmmgupaps510py9.htm/, Retrieved Fri, 10 Dec 2010 16:26:24 +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/10/t1291994784pmmgupaps510py9.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 «

24 26 38 23 10 11 25 23 36 15 10 11 30 25 23 25 10 11 19 23 30 18 10 11 22 19 26 21 10 11 22 29 26 19 10 11 25 25 30 15 13 12 23 21 27 22 10 11 17 22 34 19 10 11 21 25 28 20 13 9 19 24 36 26 10 11 19 18 42 26 10 11 15 22 31 21 10 11 23 22 26 19 10 11 27 28 16 19 13 12 14 12 23 19 10 11 23 20 45 28 10 11 19 21 30 27 10 11 18 23 45 18 10 11 20 28 30 19 10 11 23 24 24 24 10 11 25 24 29 21 13 12 19 24 30 22 13 9 24 23 31 25 10 11 25 29 34 15 10 11 26 24 41 34 10 11 29 18 37 23 10 11 32 25 33 19 10 11 29 26 48 15 10 11 28 22 44 15 10 11 17 22 29 17 10 11 28 22 44 30 13 9 26 30 43 28 10 11 25 23 31 23 10 11 14 17 28 23 10 11 25 23 26 21 10 11 26 23 30 18 10 11 20 25 27 19 15 11 18 24 34 24 10 11 32 24 47 15 10 11 25 21 37 24 13 16 21 24 27 20 10 11 20 28 30 20 10 11 30 20 36 44 10 11 24 29 39 20 10 11 26 27 32 20 10 11 24 22 25 20 10 11 22 28 19 11 10 11 14 16 29 21 10 11 24 25 26 21 13 9 24 24 31 19 13 12 24 28 31 21 10 11 24 24 31 17 10 11 22 24 39 19 10 11 27 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	7 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.4677
R-squared	0.2188
RMSE	3.4897

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	26	23.5543478260870	2.44565217391304
2	23	23.5543478260870	-0.554347826086957
3	25	21.0769230769231	3.92307692307692
4	23	23.5543478260870	-0.554347826086957
5	19	23.5543478260870	-4.55434782608696
6	29	23.5543478260870	5.44565217391304
7	25	23.5543478260870	1.44565217391304
8	21	23.5543478260870	-2.55434782608696
9	22	23.5543478260870	-1.55434782608696
10	25	23.5543478260870	1.44565217391304
11	24	21.0769230769231	2.92307692307692
12	18	21.0769230769231	-3.07692307692308
13	22	16.625	5.375
14	22	23.5543478260870	-1.55434782608696
15	28	23.5543478260870	4.44565217391304
16	12	16.625	-4.625
17	20	21.0769230769231	-1.07692307692308
18	21	21.0769230769231	-0.0769230769230766
19	23	23.5543478260870	-0.554347826086957
20	28	23.5543478260870	4.44565217391304
21	24	23.5543478260870	0.445652173913043
22	24	23.5543478260870	0.445652173913043
23	24	23.5543478260870	0.445652173913043
24	23	21.0769230769231	1.92307692307692
25	29	23.5543478260870	5.44565217391304
26	24	21.0769230769231	2.92307692307692
27	18	23.5543478260870	-5.55434782608696
28	25	23.5543478260870	1.44565217391304
29	26	23.5543478260870	2.44565217391304
30	22	23.5543478260870	-1.55434782608696
31	22	23.5543478260870	-1.55434782608696
32	22	21.0769230769231	0.923076923076923
33	30	21.0769230769231	8.92307692307692
34	23	23.5543478260870	-0.554347826086957
35	17	16.625	0.375
36	23	23.5543478260870	-0.554347826086957
37	23	23.5543478260870	-0.554347826086957
38	25	23.5543478260870	1.44565217391304
39	24	23.5543478260870	0.445652173913043
40	24	23.5543478260870	0.445652173913043
41	21	23.5543478260870	-2.55434782608696
42	24	23.5543478260870	0.445652173913043
43	28	23.5543478260870	4.44565217391304
44	20	21.0769230769231	-1.07692307692308
45	29	23.5543478260870	5.44565217391304
46	27	23.5543478260870	3.44565217391304
47	22	23.5543478260870	-1.55434782608696
48	28	23.5543478260870	4.44565217391304
49	16	16.625	-0.625
50	25	23.5543478260870	1.44565217391304
51	24	23.5543478260870	0.445652173913043
52	28	23.5543478260870	4.44565217391304
53	24	23.5543478260870	0.445652173913043
54	24	23.5543478260870	0.445652173913043
55	21	23.5543478260870	-2.55434782608696
56	25	23.5543478260870	1.44565217391304
57	25	23.5543478260870	1.44565217391304
58	22	23.5543478260870	-1.55434782608696
59	23	23.5543478260870	-0.554347826086957
60	26	23.5543478260870	2.44565217391304
61	25	23.5543478260870	1.44565217391304
62	21	23.5543478260870	-2.55434782608696
63	25	23.5543478260870	1.44565217391304
64	24	23.5543478260870	0.445652173913043
65	29	23.5543478260870	5.44565217391304
66	22	23.5543478260870	-1.55434782608696
67	27	23.5543478260870	3.44565217391304
68	26	23.5543478260870	2.44565217391304
69	24	21.0769230769231	2.92307692307692
70	27	23.5543478260870	3.44565217391304
71	24	23.5543478260870	0.445652173913043
72	24	23.5543478260870	0.445652173913043
73	29	23.5543478260870	5.44565217391304
74	22	21.0769230769231	0.923076923076923
75	24	23.5543478260870	0.445652173913043
76	24	23.5543478260870	0.445652173913043
77	23	23.5543478260870	-0.554347826086957
78	20	23.5543478260870	-3.55434782608696
79	27	23.5543478260870	3.44565217391304
80	26	21.0769230769231	4.92307692307692
81	25	23.5543478260870	1.44565217391304
82	21	21.0769230769231	-0.0769230769230766
83	19	23.5543478260870	-4.55434782608696
84	21	21.0769230769231	-0.0769230769230766
85	16	21.0769230769231	-5.07692307692308
86	29	23.5543478260870	5.44565217391304
87	15	23.5543478260870	-8.55434782608696
88	17	23.5543478260870	-6.55434782608696
89	15	23.5543478260870	-8.55434782608696
90	21	16.625	4.375
91	19	16.625	2.375
92	24	21.0769230769231	2.92307692307692
93	17	21.0769230769231	-4.07692307692308
94	23	23.5543478260870	-0.554347826086957
95	14	21.0769230769231	-7.07692307692308
96	19	23.5543478260870	-4.55434782608696
97	24	23.5543478260870	0.445652173913043
98	13	16.625	-3.625
99	22	23.5543478260870	-1.55434782608696
100	16	23.5543478260870	-7.55434782608696
101	19	21.0769230769231	-2.07692307692308
102	25	23.5543478260870	1.44565217391304
103	25	23.5543478260870	1.44565217391304
104	23	21.0769230769231	1.92307692307692
105	24	23.5543478260870	0.445652173913043
106	26	23.5543478260870	2.44565217391304
107	26	23.5543478260870	2.44565217391304
108	25	23.5543478260870	1.44565217391304
109	21	23.5543478260870	-2.55434782608696
110	26	23.5543478260870	2.44565217391304
111	23	23.5543478260870	-0.554347826086957
112	13	23.5543478260870	-10.5543478260870
113	24	23.5543478260870	0.445652173913043
114	14	23.5543478260870	-9.55434782608696
115	10	21.0769230769231	-11.0769230769231
116	24	23.5543478260870	0.445652173913043
117	22	21.0769230769231	0.923076923076923
118	24	23.5543478260870	0.445652173913043
119	20	23.5543478260870	-3.55434782608696
120	13	16.625	-3.625
121	20	23.5543478260870	-3.55434782608696
122	22	23.5543478260870	-1.55434782608696
123	24	23.5543478260870	0.445652173913043
124	20	21.0769230769231	-1.07692307692308
125	22	21.0769230769231	0.923076923076923
126	20	21.0769230769231	-1.07692307692308

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291994784pmmgupaps510py9/2wwt41291994884.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291994784pmmgupaps510py9/2wwt41291994884.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291994784pmmgupaps510py9/3wwt41291994884.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291994784pmmgupaps510py9/3wwt41291994884.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291994784pmmgupaps510py9/40era1291994884.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291994784pmmgupaps510py9/40era1291994884.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 2 ; 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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