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Recursive Partitioning

*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: Mon, 20 Dec 2010 13:28:39 +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/20/t1292851583x3d9j0kbnut8hqx.htm/, Retrieved Mon, 20 Dec 2010 14:26:27 +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/20/t1292851583x3d9j0kbnut8hqx.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 «

24 14 11 12 24 26 25 11 7 8 25 23 17 6 17 8 30 25 18 12 10 8 19 23 18 8 12 9 22 19 16 10 12 7 22 29 20 10 11 4 25 25 16 11 11 11 23 21 18 16 12 7 17 22 17 11 13 7 21 25 23 13 14 12 19 24 30 12 16 10 19 18 23 8 11 10 15 22 18 12 10 8 16 15 15 11 11 8 23 22 12 4 15 4 27 28 21 9 9 9 22 20 15 8 11 8 14 12 20 8 17 7 22 24 31 14 17 11 23 20 27 15 11 9 23 21 34 16 18 11 21 20 21 9 14 13 19 21 31 14 10 8 18 23 19 11 11 8 20 28 16 8 15 9 23 24 20 9 15 6 25 24 21 9 13 9 19 24 22 9 16 9 24 23 17 9 13 6 22 23 24 10 9 6 25 29 25 16 18 16 26 24 26 11 18 5 29 18 25 8 12 7 32 25 17 9 17 9 25 21 32 16 9 6 29 26 33 11 9 6 28 22 13 16 12 5 17 22 32 12 18 12 28 22 25 12 12 7 29 23 29 14 18 10 26 30 22 9 14 9 25 23 18 10 15 8 14 17 17 9 16 5 25 23 20 10 10 8 26 23 15 12 11 8 20 25 20 14 14 10 18 24 33 14 9 6 32 24 29 10 12 8 25 23 23 14 17 7 25 21 26 16 5 4 23 24 18 9 12 8 21 24 20 10 12 8 20 28 11 6 6 4 15 16 28 8 24 20 30 20 26 13 12 8 24 29 22 10 12 8 26 27 17 8 14 6 24 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
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Goodness of Fit
Correlation	0.6087
R-squared	0.3705
RMSE	2.7251

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	11	18.5	-7.5
2	7	12.1743119266055	-5.17431192660550
3	17	12.1743119266055	4.82568807339450
4	10	12.1743119266055	-2.17431192660550
5	12	12.1743119266055	-0.174311926605505
6	12	12.1743119266055	-0.174311926605505
7	11	8.66666666666667	2.33333333333333
8	11	13.6190476190476	-2.61904761904762
9	12	12.1743119266055	-0.174311926605505
10	13	12.1743119266055	0.825688073394495
11	14	13.6190476190476	0.380952380952381
12	16	16.8	-0.8
13	11	13.6190476190476	-2.61904761904762
14	10	12.1743119266055	-2.17431192660550
15	11	12.1743119266055	-1.17431192660550
16	15	8.66666666666667	6.33333333333333
17	9	12.1743119266055	-3.17431192660550
18	11	12.1743119266055	-1.17431192660550
19	17	12.1743119266055	4.82568807339450
20	17	16.8	0.199999999999999
21	11	12.1743119266055	-1.17431192660550
22	18	16.8	1.2
23	14	13.6190476190476	0.380952380952381
24	10	12.1743119266055	-2.17431192660550
25	11	12.1743119266055	-1.17431192660550
26	15	12.1743119266055	2.82568807339450
27	15	12.1743119266055	2.82568807339450
28	13	12.1743119266055	0.825688073394495
29	16	12.1743119266055	3.82568807339450
30	13	12.1743119266055	0.825688073394495
31	9	12.1743119266055	-3.17431192660550
32	18	18.5	-0.5
33	18	12.1743119266055	5.82568807339450
34	12	12.1743119266055	-0.174311926605505
35	17	12.1743119266055	4.82568807339450
36	9	12.1743119266055	-3.17431192660550
37	9	12.1743119266055	-3.17431192660550
38	12	12.1743119266055	-0.174311926605505
39	18	18.5	-0.5
40	12	12.1743119266055	-0.174311926605505
41	18	16.8	1.2
42	14	12.1743119266055	1.82568807339450
43	15	12.1743119266055	2.82568807339450
44	16	12.1743119266055	3.82568807339450
45	10	12.1743119266055	-2.17431192660550
46	11	12.1743119266055	-1.17431192660550
47	14	13.6190476190476	0.380952380952381
48	9	12.1743119266055	-3.17431192660550
49	12	12.1743119266055	-0.174311926605505
50	17	12.1743119266055	4.82568807339450
51	5	8.66666666666667	-3.66666666666667
52	12	12.1743119266055	-0.174311926605505
53	12	12.1743119266055	-0.174311926605505
54	6	8.66666666666667	-2.66666666666667
55	24	18.5	5.5
56	12	12.1743119266055	-0.174311926605505
57	12	12.1743119266055	-0.174311926605505
58	14	12.1743119266055	1.82568807339450
59	7	8.66666666666667	-1.66666666666667
60	13	12.1743119266055	0.825688073394495
61	12	12.1743119266055	-0.174311926605505
62	13	12.1743119266055	0.825688073394495
63	14	12.1743119266055	1.82568807339450
64	8	12.1743119266055	-4.17431192660550
65	11	12.1743119266055	-1.17431192660550
66	9	12.1743119266055	-3.17431192660550
67	11	12.1743119266055	-1.17431192660550
68	13	12.1743119266055	0.825688073394495
69	10	12.1743119266055	-2.17431192660550
70	11	12.1743119266055	-1.17431192660550
71	12	12.1743119266055	-0.174311926605505
72	9	12.1743119266055	-3.17431192660550
73	15	12.1743119266055	2.82568807339450
74	18	16.8	1.2
75	15	12.1743119266055	2.82568807339450
76	12	12.1743119266055	-0.174311926605505
77	13	12.1743119266055	0.825688073394495
78	14	12.1743119266055	1.82568807339450
79	10	12.1743119266055	-2.17431192660550
80	13	12.1743119266055	0.825688073394495
81	13	13.6190476190476	-0.619047619047619
82	11	12.1743119266055	-1.17431192660550
83	13	12.1743119266055	0.825688073394495
84	16	13.6190476190476	2.38095238095238
85	8	12.1743119266055	-4.17431192660550
86	16	12.1743119266055	3.82568807339450
87	11	12.1743119266055	-1.17431192660550
88	9	12.1743119266055	-3.17431192660550
89	16	18.5	-2.5
90	12	12.1743119266055	-0.174311926605505
91	14	12.1743119266055	1.82568807339450
92	8	12.1743119266055	-4.17431192660550
93	9	12.1743119266055	-3.17431192660550
94	15	12.1743119266055	2.82568807339450
95	11	13.6190476190476	-2.61904761904762
96	21	18.5	2.5
97	14	12.1743119266055	1.82568807339450
98	18	18.5	-0.5
99	12	12.1743119266055	-0.174311926605505
100	13	12.1743119266055	0.825688073394495
101	15	13.6190476190476	1.38095238095238
102	12	12.1743119266055	-0.174311926605505
103	19	13.6190476190476	5.38095238095238
104	15	13.6190476190476	1.38095238095238
105	11	13.6190476190476	-2.61904761904762
106	11	12.1743119266055	-1.17431192660550
107	10	12.1743119266055	-2.17431192660550
108	13	16.8	-3.8
109	15	16.8	-1.8
110	12	12.1743119266055	-0.174311926605505
111	12	12.1743119266055	-0.174311926605505
112	16	18.5	-2.5
113	9	13.6190476190476	-4.61904761904762
114	18	16.8	1.2
115	8	13.6190476190476	-5.61904761904762
116	13	12.1743119266055	0.825688073394495
117	17	12.1743119266055	4.82568807339450
118	9	12.1743119266055	-3.17431192660550
119	15	12.1743119266055	2.82568807339450
120	8	8.66666666666667	-0.666666666666666
121	7	8.66666666666667	-1.66666666666667
122	12	12.1743119266055	-0.174311926605505
123	14	13.6190476190476	0.380952380952381
124	6	12.1743119266055	-6.17431192660550
125	8	12.1743119266055	-4.17431192660550
126	17	12.1743119266055	4.82568807339450
127	10	8.66666666666667	1.33333333333333
128	11	12.1743119266055	-1.17431192660550
129	14	12.1743119266055	1.82568807339450
130	11	12.1743119266055	-1.17431192660550
131	13	13.6190476190476	-0.619047619047619
132	12	12.1743119266055	-0.174311926605505
133	11	12.1743119266055	-1.17431192660550
134	9	8.66666666666667	0.333333333333334
135	12	12.1743119266055	-0.174311926605505
136	20	16.8	3.2
137	12	12.1743119266055	-0.174311926605505
138	13	12.1743119266055	0.825688073394495
139	12	12.1743119266055	-0.174311926605505
140	12	13.6190476190476	-1.61904761904762
141	9	12.1743119266055	-3.17431192660550
142	15	16.8	-1.8
143	24	18.5	5.5
144	7	12.1743119266055	-5.17431192660550
145	17	13.6190476190476	3.38095238095238
146	11	12.1743119266055	-1.17431192660550
147	17	12.1743119266055	4.82568807339450
148	11	12.1743119266055	-1.17431192660550
149	12	12.1743119266055	-0.174311926605505
150	14	13.6190476190476	0.380952380952381
151	11	12.1743119266055	-1.17431192660550
152	16	12.1743119266055	3.82568807339450
153	21	12.1743119266055	8.8256880733945
154	14	12.1743119266055	1.82568807339450
155	20	13.6190476190476	6.38095238095238
156	13	12.1743119266055	0.825688073394495
157	11	12.1743119266055	-1.17431192660550
158	15	13.6190476190476	1.38095238095238
159	19	18.5	0.5

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292851583x3d9j0kbnut8hqx/26bqg1292851711.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292851583x3d9j0kbnut8hqx/26bqg1292851711.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292851583x3d9j0kbnut8hqx/36bqg1292851711.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292851583x3d9j0kbnut8hqx/36bqg1292851711.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292851583x3d9j0kbnut8hqx/4hkp01292851711.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292851583x3d9j0kbnut8hqx/4hkp01292851711.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 3 ; 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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