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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: Sat, 11 Dec 2010 10:06:28 +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/11/t1292061906u6yb8cosibu4que.htm/, Retrieved Sat, 11 Dec 2010 11:05:06 +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/11/t1292061906u6yb8cosibu4que.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 «

13 15 2 9 42 12 12 18 1 9 51 15 15 11 1 9 42 14 12 16 1 8 46 10 10 12 2 14 41 10 12 17 2 14 49 9 15 15 1 15 47 18 9 19 1 11 33 11 11 18 1 8 47 12 11 10 2 14 42 11 11 14 1 9 32 15 15 18 1 6 53 17 7 18 2 14 41 14 11 14 2 8 41 24 11 14 1 11 33 7 10 12 1 16 37 18 14 16 2 11 43 11 6 13 2 13 33 14 11 16 1 7 49 18 15 14 2 9 42 12 11 9 1 15 43 11 12 9 2 16 37 5 14 17 1 10 43 12 15 13 2 14 42 11 9 15 2 12 43 10 13 17 1 6 46 11 13 16 2 4 33 15 16 12 1 12 42 16 13 11 1 14 40 14 12 16 2 13 44 8 14 17 1 9 42 13 11 17 2 14 52 18 9 16 1 14 44 17 16 13 2 10 45 10 12 12 1 14 46 13 10 12 2 8 36 11 13 16 1 8 45 12 16 14 1 10 49 12 14 12 2 9 43 12 15 12 1 9 43 9 5 14 1 11 37 18 8 8 2 15 32 7 11 15 1 9 45 14 16 14 2 9 45 16 17 11 1 10 45 12 9 13 2 8 45 17 9 14 1 8 31 12 13 15 1 14 33 9 10 16 1 10 44 12 6 10 2 11 49 9 12 11 2 9 44 13 8 12 2 12 41 10 14 14 2 13 44 10 12 15 1 14 38 11 11 16 1 15 33 13 16 9 1 11 47 13 8 11 2 9 37 13 15 15 1 8 48 6 7 15 2 7 40 7 16 13 2 10 50 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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.4455
R-squared	0.1984
RMSE	6.564

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	42	45.7833333333333	-3.78333333333333
2	51	45.7833333333333	5.21666666666667
3	42	44.375	-2.375
4	46	45.7833333333333	0.216666666666669
5	41	39.1538461538462	1.84615384615385
6	49	45.7833333333333	3.21666666666667
7	47	45.7833333333333	1.21666666666667
8	33	45.7833333333333	-12.7833333333333
9	47	45.7833333333333	1.21666666666667
10	42	39.1538461538462	2.84615384615385
11	32	39.1538461538462	-7.15384615384615
12	53	45.7833333333333	7.21666666666667
13	41	38.8095238095238	2.19047619047619
14	41	39.1538461538462	1.84615384615385
15	33	39.1538461538462	-6.15384615384615
16	37	36.1111111111111	0.888888888888886
17	43	45.7833333333333	-2.78333333333333
18	33	38.8095238095238	-5.80952380952381
19	49	45.7833333333333	3.21666666666667
20	42	44.375	-2.375
21	43	39.1538461538462	3.84615384615385
22	37	36.1111111111111	0.888888888888886
23	43	45.7833333333333	-2.78333333333333
24	42	44.375	-2.375
25	43	45.7833333333333	-2.78333333333333
26	46	45.7833333333333	0.216666666666669
27	33	45.7833333333333	-12.7833333333333
28	42	44.375	-2.375
29	40	44.375	-4.375
30	44	45.7833333333333	-1.78333333333333
31	42	45.7833333333333	-3.78333333333333
32	52	45.7833333333333	6.21666666666667
33	44	45.7833333333333	-1.78333333333333
34	45	44.375	0.625
35	46	44.375	1.625
36	36	39.1538461538462	-3.15384615384615
37	45	45.7833333333333	-0.783333333333331
38	49	44.375	4.625
39	43	44.375	-1.375
40	43	44.375	-1.375
41	37	38.8095238095238	-1.80952380952381
42	32	38.8095238095238	-6.80952380952381
43	45	45.7833333333333	-0.783333333333331
44	45	44.375	0.625
45	45	44.375	0.625
46	45	39.1538461538462	5.84615384615385
47	31	39.1538461538462	-8.15384615384615
48	33	45.7833333333333	-12.7833333333333
49	44	45.7833333333333	-1.78333333333333
50	49	38.8095238095238	10.1904761904762
51	44	44.375	-0.375
52	41	38.8095238095238	2.19047619047619
53	44	44.375	-0.375
54	38	45.7833333333333	-7.78333333333333
55	33	45.7833333333333	-12.7833333333333
56	47	44.375	2.625
57	37	38.8095238095238	-1.80952380952381
58	48	45.7833333333333	2.21666666666667
59	40	38.8095238095238	1.19047619047619
60	50	44.375	5.625
61	54	45.7833333333333	8.21666666666667
62	43	45.7833333333333	-2.78333333333333
63	54	45.7833333333333	8.21666666666667
64	44	44.375	-0.375
65	47	45.7833333333333	1.21666666666667
66	33	44.375	-11.375
67	45	45.7833333333333	-0.783333333333331
68	33	38.8095238095238	-5.80952380952381
69	44	45.7833333333333	-1.78333333333333
70	47	44.375	2.625
71	45	36.1111111111111	8.88888888888889
72	43	45.7833333333333	-2.78333333333333
73	43	36.1111111111111	6.88888888888889
74	33	44.375	-11.375
75	46	45.7833333333333	0.216666666666669
76	47	44.375	2.625
77	47	38.8095238095238	8.19047619047619
78	0	38.8095238095238	-38.8095238095238
79	43	44.375	-1.375
80	46	45.7833333333333	0.216666666666669
81	36	36.1111111111111	-0.111111111111114
82	42	39.1538461538462	2.84615384615385
83	44	44.375	-0.375
84	47	45.7833333333333	1.21666666666667
85	41	39.1538461538462	1.84615384615385
86	47	45.7833333333333	1.21666666666667
87	46	45.7833333333333	0.216666666666669
88	47	45.7833333333333	1.21666666666667
89	46	44.375	1.625
90	46	45.7833333333333	0.216666666666669
91	36	45.7833333333333	-9.78333333333333
92	30	38.8095238095238	-8.80952380952381
93	48	38.8095238095238	9.19047619047619
94	45	45.7833333333333	-0.783333333333331
95	49	45.7833333333333	3.21666666666667
96	55	44.375	10.625
97	11	36.1111111111111	-25.1111111111111
98	52	45.7833333333333	6.21666666666667
99	33	38.8095238095238	-5.80952380952381
100	47	45.7833333333333	1.21666666666667
101	33	36.1111111111111	-3.11111111111111
102	44	44.375	-0.375
103	42	38.8095238095238	3.19047619047619
104	55	38.8095238095238	16.1904761904762
105	42	45.7833333333333	-3.78333333333333
106	46	44.375	1.625
107	46	45.7833333333333	0.216666666666669
108	47	45.7833333333333	1.21666666666667
109	33	44.375	-11.375
110	53	45.7833333333333	7.21666666666667
111	42	44.375	-2.375
112	44	45.7833333333333	-1.78333333333333
113	55	45.7833333333333	9.21666666666667
114	40	38.8095238095238	1.19047619047619
115	46	45.7833333333333	0.216666666666669
116	53	45.7833333333333	7.21666666666667
117	44	45.7833333333333	-1.78333333333333
118	35	39.1538461538462	-4.15384615384615
119	40	38.8095238095238	1.19047619047619
120	44	44.375	-0.375
121	46	45.7833333333333	0.216666666666669
122	45	36.1111111111111	8.88888888888889
123	53	44.375	8.625
124	45	44.375	0.625
125	48	44.375	3.625
126	46	45.7833333333333	0.216666666666669
127	55	38.8095238095238	16.1904761904762
128	47	45.7833333333333	1.21666666666667
129	43	44.375	-1.375
130	38	36.1111111111111	1.88888888888889
131	40	38.8095238095238	1.19047619047619
132	47	44.375	2.625
133	47	39.1538461538462	7.84615384615385
134	42	38.8095238095238	3.19047619047619
135	53	45.7833333333333	7.21666666666667
136	43	45.7833333333333	-2.78333333333333
137	44	44.375	-0.375
138	42	44.375	-2.375
139	51	44.375	6.625
140	54	45.7833333333333	8.21666666666667
141	41	44.375	-3.375
142	51	45.7833333333333	5.21666666666667
143	51	44.375	6.625

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292061906u6yb8cosibu4que/2yiv11292061980.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292061906u6yb8cosibu4que/2yiv11292061980.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292061906u6yb8cosibu4que/3yiv11292061980.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292061906u6yb8cosibu4que/3yiv11292061980.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292061906u6yb8cosibu4que/49ac41292061980.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292061906u6yb8cosibu4que/49ac41292061980.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = equal ; par3 = 4 ; par4 = no ;

Parameters (R input):

par1 = 5 ; 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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