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Recursive Partitioning 10

*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: Tue, 14 Dec 2010 20:32:49 +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/14/t1292358710uwaub0og89hi8sd.htm/, Retrieved Tue, 14 Dec 2010 21:31:53 +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/14/t1292358710uwaub0og89hi8sd.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 «

2 1 22 15 16 17 10 1 2 22 23 24 42 9 1 2 22 26 22 39 30 1 2 23 19 21 22 18 1 2 21 19 23 20 16 1 2 21 16 23 31 20 1 1 24 23 21 42 20 2 1 22 22 20 30 18 1 2 21 19 22 33 21 1 2 23 24 20 29 20 1 1 20 19 12 31 20 2 1 23 25 23 39 20 1 1 20 23 23 44 29 1 2 21 31 30 40 14 2 1 22 29 22 42 25 2 2 22 18 21 28 19 2 2 21 17 21 29 19 1 1 20 22 15 35 25 1 1 21 21 22 26 25 1 2 21 24 24 42 19 1 1 20 22 23 26 19 1 1 21 16 15 30 18 1 2 23 22 24 28 24 1 1 23 21 24 24 18 2 1 21 25 21 26 26 1 1 22 22 21 39 26 2 1 20 24 18 33 24 2 1 23 21 20 50 29 2 1 21 25 19 40 26 1 1 21 29 29 49 28 2 1 23 19 20 31 18 2 1 23 29 23 37 19 2 2 22 25 24 29 21 1 1 21 19 27 37 13 1 1 NA 27 28 16 19 1 2 21 25 24 28 26 1 2 21 23 29 29 17 1 1 22 24 24 31 19 2 2 22 23 22 34 28 1 2 22 25 25 30 15 1 1 22 26 24 31 16 1 1 23 23 14 44 18 2 1 NA 22 22 35 25 1 2 22 32 24 47 15 1 2 21 22 24 39 24 1 1 23 18 24 34 24 2 1 21 19 24 15 14 2 2 32 23 22 26 19 2 2 32 24 22 25 20 2 1 21 19 21 30 27 1 1 20 16 21 25 20 1 1 2 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	'George Udny Yule' @ 72.249.76.132

Goodness of Fit
Correlation	0.4502
R-squared	0.2027
RMSE	3.6947

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	15	17.2857142857143	-2.28571428571428
2	23	23.7777777777778	-0.777777777777779
3	26	23.7777777777778	2.22222222222222
4	19	21.7727272727273	-2.77272727272727
5	19	21.7727272727273	-2.77272727272727
6	16	21.7727272727273	-5.77272727272727
7	23	23.7777777777778	-0.777777777777779
8	22	21.7727272727273	0.227272727272727
9	19	23.7777777777778	-4.77777777777778
10	24	21.7727272727273	2.22727272727273
11	19	17.2857142857143	1.71428571428572
12	25	23.7777777777778	1.22222222222222
13	23	23.7777777777778	-0.777777777777779
14	31	23.7777777777778	7.22222222222222
15	29	23.7777777777778	5.22222222222222
16	18	21.7727272727273	-3.77272727272727
17	17	21.7727272727273	-4.77272727272727
18	22	23.7777777777778	-1.77777777777778
19	21	21.7727272727273	-0.772727272727273
20	24	23.7777777777778	0.222222222222221
21	22	21.7727272727273	0.227272727272727
22	16	17.2857142857143	-1.28571428571428
23	22	21.7727272727273	0.227272727272727
24	21	21.7727272727273	-0.772727272727273
25	25	21.7727272727273	3.22727272727273
26	22	23.7777777777778	-1.77777777777778
27	24	23.7777777777778	0.222222222222221
28	21	23.7777777777778	-2.77777777777778
29	25	23.7777777777778	1.22222222222222
30	29	23.7777777777778	5.22222222222222
31	19	21.7727272727273	-2.77272727272727
32	29	23.7777777777778	5.22222222222222
33	25	21.7727272727273	3.22727272727273
34	19	23.7777777777778	-4.77777777777778
35	27	21.7727272727273	5.22727272727273
36	25	21.7727272727273	3.22727272727273
37	23	21.7727272727273	1.22727272727273
38	24	21.7727272727273	2.22727272727273
39	23	23.7777777777778	-0.777777777777779
40	25	21.7727272727273	3.22727272727273
41	26	21.7727272727273	4.22727272727273
42	23	23.7777777777778	-0.777777777777779
43	22	23.7777777777778	-1.77777777777778
44	32	23.7777777777778	8.22222222222222
45	22	23.7777777777778	-1.77777777777778
46	18	23.7777777777778	-5.77777777777778
47	19	21.7727272727273	-2.77272727272727
48	23	21.7727272727273	1.22727272727273
49	24	21.7727272727273	2.22727272727273
50	19	21.7727272727273	-2.77272727272727
51	16	21.7727272727273	-5.77272727272727
52	23	23.7777777777778	-0.777777777777779
53	17	23.7777777777778	-6.77777777777778
54	17	21.7727272727273	-4.77272727272727
55	28	23.7777777777778	4.22222222222222
56	24	21.7727272727273	2.22727272727273
57	21	17.2857142857143	3.71428571428572
58	14	17.2857142857143	-3.28571428571428
59	21	23.7777777777778	-2.77777777777778
60	20	21.7727272727273	-1.77272727272727
61	25	21.7727272727273	3.22727272727273
62	20	23.7777777777778	-3.77777777777778
63	17	23.7777777777778	-6.77777777777778
64	26	23.7777777777778	2.22222222222222
65	17	21.7727272727273	-4.77272727272727
66	17	23.7777777777778	-6.77777777777778
67	24	21.7727272727273	2.22727272727273
68	30	23.7777777777778	6.22222222222222
69	25	23.7777777777778	1.22222222222222
70	15	21.7727272727273	-6.77272727272727
71	25	23.7777777777778	1.22222222222222
72	18	23.7777777777778	-5.77777777777778
73	20	23.7777777777778	-3.77777777777778
74	32	23.7777777777778	8.22222222222222
75	14	17.2857142857143	-3.28571428571428
76	20	21.7727272727273	-1.77272727272727
77	25	23.7777777777778	1.22222222222222
78	25	21.7727272727273	3.22727272727273
79	25	21.7727272727273	3.22727272727273
80	35	23.7777777777778	11.2222222222222
81	29	23.7777777777778	5.22222222222222
82	25	23.7777777777778	1.22222222222222
83	21	21.7727272727273	-0.772727272727273
84	21	21.7727272727273	-0.772727272727273
85	24	23.7777777777778	0.222222222222221
86	26	21.7727272727273	4.22727272727273
87	24	21.7727272727273	2.22727272727273
88	20	23.7777777777778	-3.77777777777778
89	24	21.7727272727273	2.22727272727273
90	18	17.2857142857143	0.714285714285715
91	17	17.2857142857143	-0.285714285714285
92	22	23.7777777777778	-1.77777777777778
93	22	23.7777777777778	-1.77777777777778
94	22	17.2857142857143	4.71428571428572
95	24	23.7777777777778	0.222222222222221
96	32	23.7777777777778	8.22222222222222
97	19	21.7727272727273	-2.77272727272727
98	21	21.7727272727273	-0.772727272727273
99	23	21.7727272727273	1.22727272727273
100	26	23.7777777777778	2.22222222222222
101	18	23.7777777777778	-5.77777777777778
102	19	23.7777777777778	-4.77777777777778
103	22	21.7727272727273	0.227272727272727
104	27	21.7727272727273	5.22727272727273
105	21	21.7727272727273	-0.772727272727273
106	20	21.7727272727273	-1.77272727272727
107	21	23.7777777777778	-2.77777777777778
108	20	21.7727272727273	-1.77272727272727
109	29	23.7777777777778	5.22222222222222
110	30	21.7727272727273	8.22727272727273
111	23	21.7727272727273	1.22727272727273
112	29	21.7727272727273	7.22727272727273
113	19	23.7777777777778	-4.77777777777778
114	26	23.7777777777778	2.22222222222222
115	22	21.7727272727273	0.227272727272727
116	26	23.7777777777778	2.22222222222222
117	27	23.7777777777778	3.22222222222222
118	19	23.7777777777778	-4.77777777777778
119	24	23.7777777777778	0.222222222222221
120	26	21.7727272727273	4.22727272727273
121	22	21.7727272727273	0.227272727272727
122	23	23.7777777777778	-0.777777777777779
123	25	23.7777777777778	1.22222222222222
124	19	23.7777777777778	-4.77777777777778
125	20	23.7777777777778	-3.77777777777778
126	25	23.7777777777778	1.22222222222222
127	14	17.2857142857143	-3.28571428571428
128	20	17.2857142857143	2.71428571428572
129	27	23.7777777777778	3.22222222222222
130	21	21.7727272727273	-0.772727272727273
131	21	23.7777777777778	-2.77777777777778
132	14	17.2857142857143	-3.28571428571428
133	21	23.7777777777778	-2.77777777777778
134	23	21.7727272727273	1.22727272727273
135	18	23.7777777777778	-5.77777777777778
136	20	21.7727272727273	-1.77272727272727
137	19	21.7727272727273	-2.77272727272727
138	15	21.7727272727273	-6.77272727272727
139	23	21.7727272727273	1.22727272727273
140	26	23.7777777777778	2.22222222222222
141	21	21.7727272727273	-0.772727272727273
142	13	17.2857142857143	-4.28571428571428
143	24	21.7727272727273	2.22727272727273
144	17	21.7727272727273	-4.77272727272727
145	21	21.7727272727273	-0.772727272727273
146	28	23.7777777777778	4.22222222222222
147	22	23.7777777777778	-1.77777777777778
148	25	17.2857142857143	7.71428571428572
149	18	21.7727272727273	-3.77272727272727
150	27	23.7777777777778	3.22222222222222
151	25	23.7777777777778	1.22222222222222
152	21	21.7727272727273	-0.772727272727273

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292358710uwaub0og89hi8sd/2kfkt1292358761.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292358710uwaub0og89hi8sd/2kfkt1292358761.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292358710uwaub0og89hi8sd/3kfkt1292358761.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292358710uwaub0og89hi8sd/3kfkt1292358761.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292358710uwaub0og89hi8sd/4doke1292358761.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292358710uwaub0og89hi8sd/4doke1292358761.ps (open in new window)

Parameters (Session):

par1 = 4 ; par2 = none ; par3 = 3 ; par4 = yes ;

Parameters (R input):

par1 = 4 ; par2 = none ; par3 = 3 ; par4 = yes ;

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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