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Recursive partitioning 1

*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: Thu, 09 Dec 2010 20:38:11 +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/09/t1291927039megsepsem9amfvq.htm/, Retrieved Thu, 09 Dec 2010 21:37:19 +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/09/t1291927039megsepsem9amfvq.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 15 10 77 5 4 15 11 12 13 6 0 12 20 63 6 4 9 12 7 11 4 0 15 16 73 4 10 12 12 13 14 6 0 12 10 76 6 6 15 11 11 12 5 0 14 8 90 3 5 17 11 16 12 5 0 8 14 67 10 8 14 10 10 6 4 1 11 19 69 8 9 9 11 15 10 5 1 15 15 70 3 6 12 9 5 11 3 0 4 23 54 4 8 11 10 4 10 2 0 13 9 54 3 11 13 12 7 12 5 1 19 12 76 5 6 16 12 15 15 6 1 10 14 75 5 8 16 12 5 13 6 1 15 13 76 6 11 15 13 16 18 8 0 6 11 80 5 5 10 9 15 11 6 1 7 11 89 3 10 16 12 13 12 3 0 14 10 73 4 7 12 12 13 13 6 0 16 12 74 8 7 15 12 15 14 6 1 16 18 78 8 13 13 12 15 16 7 1 14 12 76 8 10 18 13 10 16 8 0 15 10 69 5 8 13 11 17 16 6 1 14 15 74 8 6 17 12 14 15 7 1 12 15 82 2 8 14 12 9 13 4 0 9 12 77 0 7 13 15 6 8 4 1 12 9 84 5 5 13 11 11 14 2 1 14 11 75 2 9 15 12 13 15 6 1 12 15 54 7 9 13 10 12 13 6 1 14 16 79 5 11 15 11 10 16 6 1 10 17 79 2 11 13 13 4 13 6 1 14 12 69 12 11 14 6 13 12 6 1 16 11 88 7 9 13 12 15 15 7 1 10 13 57 0 7 16 12 8 11 4 1 8 9 69 2 6 14 10 10 14 3 1 12 11 86 3 6 18 12 8 13 5 1 11 9 65 0 6 15 12 7 13 6 0 8 20 66 9 5 9 11 9 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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.6745
R-squared	0.4549
RMSE	2.1611

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	15	12.7931034482759	2.20689655172414
2	12	8.93548387096774	3.06451612903226
3	15	14.32	0.68
4	12	11.1481481481481	0.851851851851851
5	14	12.7931034482759	1.20689655172414
6	8	8.93548387096774	-0.935483870967742
7	11	12.7931034482759	-1.79310344827586
8	15	8.93548387096774	6.06451612903226
9	4	8.93548387096774	-4.93548387096774
10	13	11.1481481481481	1.85185185185185
11	19	14.32	4.68
12	10	11.1481481481481	-1.14814814814815
13	15	14.32	0.68
14	6	12.7931034482759	-6.79310344827586
15	7	8.93548387096774	-1.93548387096774
16	14	12.7931034482759	1.20689655172414
17	16	14.32	1.68
18	16	14.32	1.68
19	14	11.1481481481481	2.85185185185185
20	15	14.32	0.68
21	14	14.32	-0.32
22	12	11.2631578947368	0.736842105263158
23	9	8.93548387096774	0.064516129032258
24	12	11.2631578947368	0.736842105263158
25	14	14.32	-0.32
26	12	12.7931034482759	-0.793103448275861
27	14	11.1481481481481	2.85185185185185
28	10	11.1481481481481	-1.14814814814815
29	14	12.7931034482759	1.20689655172414
30	16	14.32	1.68
31	10	8.93548387096774	1.06451612903226
32	8	11.2631578947368	-3.26315789473684
33	12	11.1481481481481	0.851851851851851
34	11	11.1481481481481	-0.148148148148149
35	8	8.93548387096774	-0.935483870967742
36	13	14.32	-1.32
37	11	11.2631578947368	-0.263157894736842
38	12	8.93548387096774	3.06451612903226
39	16	14.32	1.68
40	16	14.32	1.68
41	13	14.32	-1.32
42	14	14.32	-0.32
43	5	8.93548387096774	-3.93548387096774
44	14	14.32	-0.32
45	13	8.93548387096774	4.06451612903226
46	16	14.32	1.68
47	14	14.32	-0.32
48	15	14.32	0.68
49	15	14.32	0.68
50	11	14.32	-3.32
51	15	12.7931034482759	2.20689655172414
52	16	14.32	1.68
53	13	14.32	-1.32
54	11	12.7931034482759	-1.79310344827586
55	12	12.7931034482759	-0.793103448275861
56	12	11.1481481481481	0.851851851851851
57	10	14.32	-4.32
58	8	11.1481481481481	-3.14814814814815
59	9	8.93548387096774	0.064516129032258
60	12	12.7931034482759	-0.793103448275861
61	14	14.32	-0.32
62	12	12.7931034482759	-0.793103448275861
63	11	11.2631578947368	-0.263157894736842
64	14	14.32	-0.32
65	7	8.93548387096774	-1.93548387096774
66	16	14.32	1.68
67	16	14.32	1.68
68	11	11.1481481481481	-0.148148148148149
69	16	14.32	1.68
70	13	11.1481481481481	1.85185185185185
71	11	11.1481481481481	-0.148148148148149
72	13	12.7931034482759	0.206896551724139
73	14	12.7931034482759	1.20689655172414
74	15	11.2631578947368	3.73684210526316
75	10	11.1481481481481	-1.14814814814815
76	15	14.32	0.68
77	11	11.1481481481481	-0.148148148148149
78	11	11.1481481481481	-0.148148148148149
79	6	8.93548387096774	-2.93548387096774
80	11	11.2631578947368	-0.263157894736842
81	12	11.2631578947368	0.736842105263158
82	13	14.32	-1.32
83	12	11.1481481481481	0.851851851851851
84	8	11.1481481481481	-3.14814814814815
85	9	8.93548387096774	0.064516129032258
86	10	11.2631578947368	-1.26315789473684
87	16	14.32	1.68
88	15	12.7931034482759	2.20689655172414
89	14	12.7931034482759	1.20689655172414
90	12	12.7931034482759	-0.793103448275861
91	12	11.1481481481481	0.851851851851851
92	10	8.93548387096774	1.06451612903226
93	12	11.2631578947368	0.736842105263158
94	8	8.93548387096774	-0.935483870967742
95	16	14.32	1.68
96	11	8.93548387096774	2.06451612903226
97	12	11.2631578947368	0.736842105263158
98	9	12.7931034482759	-3.79310344827586
99	14	12.7931034482759	1.20689655172414
100	15	14.32	0.68
101	8	11.2631578947368	-3.26315789473684
102	12	14.32	-2.32
103	10	11.1481481481481	-1.14814814814815
104	16	14.32	1.68
105	17	14.32	2.68
106	8	11.2631578947368	-3.26315789473684
107	9	11.1481481481481	-2.14814814814815
108	8	11.2631578947368	-3.26315789473684
109	11	11.1481481481481	-0.148148148148149
110	16	14.32	1.68
111	13	14.32	-1.32
112	5	8.93548387096774	-3.93548387096774
113	15	12.7931034482759	2.20689655172414
114	15	12.7931034482759	2.20689655172414
115	12	12.7931034482759	-0.793103448275861
116	12	12.7931034482759	-0.793103448275861
117	16	14.32	1.68
118	12	12.7931034482759	-0.793103448275861
119	10	11.1481481481481	-1.14814814814815
120	12	11.1481481481481	0.851851851851851
121	4	8.93548387096774	-4.93548387096774
122	11	14.32	-3.32
123	16	14.32	1.68
124	7	8.93548387096774	-1.93548387096774
125	9	8.93548387096774	0.064516129032258
126	14	8.93548387096774	5.06451612903226
127	11	8.93548387096774	2.06451612903226
128	10	11.1481481481481	-1.14814814814815
129	6	8.93548387096774	-2.93548387096774
130	14	12.7931034482759	1.20689655172414
131	11	11.1481481481481	-0.148148148148149
132	11	8.93548387096774	2.06451612903226
133	9	14.32	-5.32
134	16	11.2631578947368	4.73684210526316
135	7	8.93548387096774	-1.93548387096774
136	8	8.93548387096774	-0.935483870967742
137	10	8.93548387096774	1.06451612903226
138	14	12.7931034482759	1.20689655172414
139	9	8.93548387096774	0.064516129032258
140	13	12.7931034482759	0.206896551724139
141	13	8.93548387096774	4.06451612903226
142	12	14.32	-2.32
143	11	14.32	-3.32
144	10	14.32	-4.32
145	12	12.7931034482759	-0.793103448275861
146	14	14.32	-0.32
147	11	14.32	-3.32
148	13	11.2631578947368	1.73684210526316
149	14	14.32	-0.32
150	13	12.7931034482759	0.206896551724139
151	16	14.32	1.68
152	13	11.1481481481481	1.85185185185185
153	12	11.2631578947368	0.736842105263158
154	9	11.2631578947368	-2.26315789473684
155	14	11.2631578947368	2.73684210526316
156	15	14.32	0.68

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/09/t1291927039megsepsem9amfvq/2to651291927082.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t1291927039megsepsem9amfvq/2to651291927082.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t1291927039megsepsem9amfvq/3to651291927082.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t1291927039megsepsem9amfvq/3to651291927082.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t1291927039megsepsem9amfvq/4lxoq1291927082.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/09/t1291927039megsepsem9amfvq/4lxoq1291927082.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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We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

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