Home » date » 2010 » Dec » 16 »

Recursive Partitioning Learning (no categorization)

*Unverified author*

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, 16 Dec 2010 19:47:46 +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/16/t1292528800rho5pw52o56wjgt.htm/, Retrieved Thu, 16 Dec 2010 20:46:40 +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/16/t1292528800rho5pw52o56wjgt.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 «

0 13 26 9 15 25 25 0 16 20 9 15 25 24 0 19 21 9 14 19 21 1 15 31 14 10 18 23 0 14 21 8 10 18 17 0 13 18 8 12 22 19 0 19 26 11 18 29 18 0 15 22 10 12 26 27 0 14 22 9 14 25 23 0 15 29 15 18 23 23 1 16 15 14 9 23 29 0 16 16 11 11 23 21 1 16 24 14 11 24 26 0 17 17 6 17 30 25 1 15 19 20 8 19 25 1 15 22 9 16 24 23 0 20 31 10 21 32 26 1 18 28 8 24 30 20 0 16 38 11 21 29 29 1 16 26 14 14 17 24 0 19 25 11 7 25 23 0 16 25 16 18 26 24 1 17 29 14 18 26 30 0 17 28 11 13 25 22 1 16 15 11 11 23 22 0 15 18 12 13 21 13 1 14 21 9 13 19 24 0 15 25 7 18 35 17 1 12 23 13 14 19 24 0 14 23 10 12 20 21 0 16 19 9 9 21 23 1 14 18 9 12 21 24 1 10 26 16 5 23 24 1 14 18 12 10 19 23 0 16 18 6 11 17 26 1 16 28 14 11 24 24 1 16 17 14 12 15 21 0 14 29 10 12 25 23 1 20 12 4 15 27 28 1 14 25 12 12 29 23 0 14 28 12 16 27 22 0 11 20 14 14 18 24 0 15 17 9 17 25 21 0 16 17 9 13 22 23 1 14 20 10 10 26 23 0 16 31 14 17 23 20 1 14 21 10 12 16 23 1 12 19 9 13 27 21 0 16 23 14 13 25 27 1 9 15 8 11 14 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	0.3982
R-squared	0.1585
RMSE	1.9319

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	13	16.3611111111111	-3.36111111111111
2	16	16.3611111111111	-0.361111111111111
3	19	15.0862068965517	3.91379310344828
4	15	14.1666666666667	0.833333333333334
5	14	15.0862068965517	-1.08620689655172
6	13	15.0862068965517	-2.08620689655172
7	19	15.0862068965517	3.91379310344828
8	15	16.3611111111111	-1.36111111111111
9	14	15.0862068965517	-1.08620689655172
10	15	14.1666666666667	0.833333333333334
11	16	14.1666666666667	1.83333333333333
12	16	15.0862068965517	0.913793103448276
13	16	14.1666666666667	1.83333333333333
14	17	16.3611111111111	0.638888888888889
15	15	14.1666666666667	0.833333333333334
16	15	15.0862068965517	-0.0862068965517242
17	20	16.3611111111111	3.63888888888889
18	18	15.0862068965517	2.91379310344828
19	16	16.3611111111111	-0.361111111111111
20	16	14.1666666666667	1.83333333333333
21	19	15.0862068965517	3.91379310344828
22	16	14.1666666666667	1.83333333333333
23	17	14.1666666666667	2.83333333333333
24	17	15.0862068965517	1.91379310344828
25	16	15.0862068965517	0.913793103448276
26	15	14.1666666666667	0.833333333333334
27	14	16.3611111111111	-2.36111111111111
28	15	15.0862068965517	-0.0862068965517242
29	12	14.1666666666667	-2.16666666666667
30	14	15.0862068965517	-1.08620689655172
31	16	15.0862068965517	0.913793103448276
32	14	16.3611111111111	-2.36111111111111
33	10	14.1666666666667	-4.16666666666667
34	14	14.1666666666667	-0.166666666666666
35	16	16.3611111111111	-0.361111111111111
36	16	14.1666666666667	1.83333333333333
37	16	14.1666666666667	1.83333333333333
38	14	15.0862068965517	-1.08620689655172
39	20	16.3611111111111	3.63888888888889
40	14	14.1666666666667	-0.166666666666666
41	14	14.1666666666667	-0.166666666666666
42	11	14.1666666666667	-3.16666666666667
43	15	15.0862068965517	-0.0862068965517242
44	16	15.0862068965517	0.913793103448276
45	14	15.0862068965517	-1.08620689655172
46	16	14.1666666666667	1.83333333333333
47	14	15.0862068965517	-1.08620689655172
48	12	15.0862068965517	-3.08620689655172
49	16	14.1666666666667	1.83333333333333
50	9	15.0862068965517	-6.08620689655172
51	14	15.0862068965517	-1.08620689655172
52	16	15.0862068965517	0.913793103448276
53	16	15.0862068965517	0.913793103448276
54	15	15.0862068965517	-0.0862068965517242
55	16	15.0862068965517	0.913793103448276
56	12	14.1666666666667	-2.16666666666667
57	16	16.3611111111111	-0.361111111111111
58	16	16.3611111111111	-0.361111111111111
59	14	16.3611111111111	-2.36111111111111
60	16	14.1666666666667	1.83333333333333
61	17	16.3611111111111	0.638888888888889
62	18	15.0862068965517	2.91379310344828
63	18	14.1666666666667	3.83333333333333
64	12	14.1666666666667	-2.16666666666667
65	16	16.3611111111111	-0.361111111111111
66	10	14.1666666666667	-4.16666666666667
67	14	14.1666666666667	-0.166666666666666
68	18	16.3611111111111	1.63888888888889
69	18	16.3611111111111	1.63888888888889
70	16	16.3611111111111	-0.361111111111111
71	16	16.3611111111111	-0.361111111111111
72	16	14.1666666666667	1.83333333333333
73	13	15.0862068965517	-2.08620689655172
74	16	15.0862068965517	0.913793103448276
75	16	14.1666666666667	1.83333333333333
76	20	16.3611111111111	3.63888888888889
77	16	15.0862068965517	0.913793103448276
78	15	15.0862068965517	-0.0862068965517242
79	15	16.3611111111111	-1.36111111111111
80	16	16.3611111111111	-0.361111111111111
81	14	14.1666666666667	-0.166666666666666
82	15	14.1666666666667	0.833333333333334
83	12	15.0862068965517	-3.08620689655172
84	17	15.0862068965517	1.91379310344828
85	16	16.3611111111111	-0.361111111111111
86	15	14.1666666666667	0.833333333333334
87	13	14.1666666666667	-1.16666666666667
88	16	15.0862068965517	0.913793103448276
89	16	16.3611111111111	-0.361111111111111
90	16	16.3611111111111	-0.361111111111111
91	16	16.3611111111111	-0.361111111111111
92	14	15.0862068965517	-1.08620689655172
93	16	14.1666666666667	1.83333333333333
94	16	14.1666666666667	1.83333333333333
95	20	16.3611111111111	3.63888888888889
96	15	16.3611111111111	-1.36111111111111
97	16	14.1666666666667	1.83333333333333
98	13	15.0862068965517	-2.08620689655172
99	17	16.3611111111111	0.638888888888889
100	16	14.1666666666667	1.83333333333333
101	12	14.1666666666667	-2.16666666666667
102	16	15.0862068965517	0.913793103448276
103	16	14.1666666666667	1.83333333333333
104	17	15.0862068965517	1.91379310344828
105	13	14.1666666666667	-1.16666666666667
106	12	15.0862068965517	-3.08620689655172
107	18	16.3611111111111	1.63888888888889
108	14	14.1666666666667	-0.166666666666666
109	14	14.1666666666667	-0.166666666666666
110	13	14.1666666666667	-1.16666666666667
111	16	15.0862068965517	0.913793103448276
112	13	14.1666666666667	-1.16666666666667
113	16	15.0862068965517	0.913793103448276
114	13	15.0862068965517	-2.08620689655172
115	16	16.3611111111111	-0.361111111111111
116	15	14.1666666666667	0.833333333333334
117	16	14.1666666666667	1.83333333333333
118	15	15.0862068965517	-0.0862068965517242
119	17	16.3611111111111	0.638888888888889
120	15	15.0862068965517	-0.0862068965517242
121	12	15.0862068965517	-3.08620689655172
122	16	15.0862068965517	0.913793103448276
123	10	14.1666666666667	-4.16666666666667
124	16	15.0862068965517	0.913793103448276
125	14	15.0862068965517	-1.08620689655172
126	15	16.3611111111111	-1.36111111111111
127	13	14.1666666666667	-1.16666666666667
128	15	15.0862068965517	-0.0862068965517242
129	11	14.1666666666667	-3.16666666666667
130	12	14.1666666666667	-2.16666666666667
131	16	16.3611111111111	-0.361111111111111
132	15	14.1666666666667	0.833333333333334
133	17	15.0862068965517	1.91379310344828
134	16	16.3611111111111	-0.361111111111111
135	10	14.1666666666667	-4.16666666666667
136	18	15.0862068965517	2.91379310344828
137	13	14.1666666666667	-1.16666666666667
138	15	15.0862068965517	-0.0862068965517242
139	16	15.0862068965517	0.913793103448276
140	16	14.1666666666667	1.83333333333333
141	14	14.1666666666667	-0.166666666666666
142	10	14.1666666666667	-4.16666666666667
143	17	15.0862068965517	1.91379310344828
144	13	15.0862068965517	-2.08620689655172
145	15	15.0862068965517	-0.0862068965517242
146	16	16.3611111111111	-0.361111111111111
147	12	15.0862068965517	-3.08620689655172
148	13	15.0862068965517	-2.08620689655172

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292528800rho5pw52o56wjgt/2wtqx1292528857.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292528800rho5pw52o56wjgt/2wtqx1292528857.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292528800rho5pw52o56wjgt/3wtqx1292528857.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292528800rho5pw52o56wjgt/3wtqx1292528857.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292528800rho5pw52o56wjgt/4o2801292528857.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292528800rho5pw52o56wjgt/4o2801292528857.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 2 ; par2 = none ; par3 = 2 ; 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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