Home » date » 2010 » Dec » 15 »

recursive partinioning - belonging

*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, 15 Dec 2010 19:17:31 +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/15/t1292440560dmqav1exv6fq36i.htm/, Retrieved Wed, 15 Dec 2010 20:16:00 +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/15/t1292440560dmqav1exv6fq36i.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 «

12 18 9 51 15 15 11 9 42 14 12 16 8 46 10 15 15 15 47 18 9 19 11 33 11 11 18 8 47 12 11 14 9 32 15 15 18 6 53 17 11 14 11 33 7 10 12 16 37 18 11 16 7 49 18 11 9 15 43 11 14 17 10 43 12 13 17 6 46 11 16 12 12 42 16 13 11 14 40 14 14 17 9 42 13 9 16 14 44 17 12 12 14 46 13 13 16 8 45 12 16 14 10 49 12 15 12 9 43 9 5 14 11 37 18 11 15 9 45 14 17 11 10 45 12 9 14 8 31 12 13 15 14 33 9 10 16 10 44 12 12 15 14 38 11 11 16 15 33 13 16 9 11 47 13 15 15 8 48 6 14 17 10 54 21 16 17 10 43 11 9 15 9 54 9 14 13 13 44 18 15 15 10 45 15 15 15 11 44 11 13 14 10 47 14 12 7 16 43 12 12 13 6 33 8 12 15 11 46 11 14 13 14 47 17 6 16 9 47 16 14 12 11 43 13 12 14 12 44 13 16 15 9 47 13 14 15 14 47 15 10 17 8 46 12 16 16 10 47 12 15 14 8 46 15 10 16 11 36 21 8 10 14 30 24 13 15 10 49 15 16 13 9 55 17 11 16 8 52 16 14 18 8 47 15 9 14 16 33 11 14 14 13 44 15 8 14 13 42 12 8 14 8 55 14 11 15 9 42 12 12 14 11 46 20 14 15 9 46 17 16 12 14 33 11 16 19 7 53 11 12 15 11 44 12 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	0.4455
R-squared	0.1984
RMSE	6.564

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

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292440560dmqav1exv6fq36i/2x2jl1292440643.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292440560dmqav1exv6fq36i/2x2jl1292440643.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292440560dmqav1exv6fq36i/3x2jl1292440643.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292440560dmqav1exv6fq36i/3x2jl1292440643.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292440560dmqav1exv6fq36i/48bio1292440643.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/15/t1292440560dmqav1exv6fq36i/48bio1292440643.ps (open in new window)

Parameters (Session):

par1 = 4 ; par2 = none ; par4 = no ;

Parameters (R input):

par1 = 4 ; 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')

}

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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