Home » date » 2010 » Dec » 19 »

Paper Recursive Partitioning (no categorization)

*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: Sun, 19 Dec 2010 14:40:42 +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/19/t12927695243bxs659brqz4ozu.htm/, Retrieved Sun, 19 Dec 2010 15:38:45 +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/19/t12927695243bxs659brqz4ozu.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 41 25 15 9 3 1 38 25 15 9 4 1 37 19 14 9 4 1 42 18 10 8 4 1 40 23 18 15 3 1 43 25 14 9 4 1 40 23 11 11 4 1 45 30 17 6 5 1 45 32 21 10 4 1 44 25 7 11 4 1 42 26 18 16 4 1 41 35 18 7 4 1 38 20 12 10 4 1 38 21 9 9 4 1 46 17 11 6 5 1 42 27 16 12 4 1 46 25 12 10 4 1 43 18 14 14 5 1 38 22 13 9 4 1 39 23 17 14 4 1 40 25 13 14 3 1 37 19 13 9 2 1 41 20 12 8 4 1 46 26 12 10 4 1 37 22 9 9 3 1 39 25 17 9 4 1 44 29 18 11 5 1 38 22 12 10 2 1 38 32 12 8 0 1 38 23 9 14 4 1 33 18 13 10 3 1 43 26 11 14 4 1 41 14 13 15 2 1 45 25 11 10 5 1 38 23 15 10 4 1 39 24 11 11 4 1 40 21 14 10 4 1 36 17 12 16 2 1 49 29 8 6 5 1 41 25 11 11 4 1 42 25 17 14 3 1 41 25 16 9 5 1 43 21 13 11 4 1 46 23 15 8 3 1 41 25 16 8 5 1 39 25 7 11 4 1 42 24 16 16 4 1 35 21 13 12 5 1 36 22 15 14 3 1 41 20 12 10 4 1 41 22 15 10 3 1 36 28 18 12 4 1 46 25 17 9 4 1 44 21 15 8 4 1 43 27 11 16 2 1 40 19 12 13 5 1 40 20 14 8 3 1 39 22 10 8 4 1 44 26 11 7 4 1 38 17 12 11 2 1 39 15 6 6 4 1 41 27 15 9 5 1 3 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	4 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Goodness of Fit
Correlation	0.5509
R-squared	0.3035
RMSE	3.0828

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	41	41.6222222222222	-0.62222222222222
2	38	41.6222222222222	-3.62222222222222
3	37	39.2	-2.2
4	42	39.2	2.8
5	40	41.6222222222222	-1.62222222222222
6	43	41.6222222222222	1.37777777777778
7	40	41.6222222222222	-1.62222222222222
8	45	44.0588235294118	0.941176470588232
9	45	41.6222222222222	3.37777777777778
10	44	41.6222222222222	2.37777777777778
11	42	41.6222222222222	0.37777777777778
12	41	41.6222222222222	-0.62222222222222
13	38	39.2	-1.2
14	38	39.2	-1.2
15	46	44.0588235294118	1.94117647058823
16	42	41.6222222222222	0.37777777777778
17	46	41.6222222222222	4.37777777777778
18	43	44.0588235294118	-1.05882352941177
19	38	39.2	-1.2
20	39	41.6222222222222	-2.62222222222222
21	40	41.6222222222222	-1.62222222222222
22	37	37.4736842105263	-0.473684210526315
23	41	39.2	1.8
24	46	41.6222222222222	4.37777777777778
25	37	39.2	-2.2
26	39	41.6222222222222	-2.62222222222222
27	44	44.0588235294118	-0.058823529411768
28	38	37.4736842105263	0.526315789473685
29	38	37.4736842105263	0.526315789473685
30	38	41.6222222222222	-3.62222222222222
31	33	39.2	-6.2
32	43	41.6222222222222	1.37777777777778
33	41	37.4736842105263	3.52631578947368
34	45	44.0588235294118	0.941176470588232
35	38	41.6222222222222	-3.62222222222222
36	39	41.6222222222222	-2.62222222222222
37	40	39.2	0.799999999999997
38	36	37.4736842105263	-1.47368421052632
39	49	44.0588235294118	4.94117647058823
40	41	41.6222222222222	-0.62222222222222
41	42	41.6222222222222	0.37777777777778
42	41	44.0588235294118	-3.05882352941177
43	43	39.2	3.8
44	46	41.6222222222222	4.37777777777778
45	41	44.0588235294118	-3.05882352941177
46	39	41.6222222222222	-2.62222222222222
47	42	41.6222222222222	0.37777777777778
48	35	44.0588235294118	-9.05882352941177
49	36	39.2	-3.2
50	41	39.2	1.8
51	41	39.2	1.8
52	36	41.6222222222222	-5.62222222222222
53	46	41.6222222222222	4.37777777777778
54	44	39.2	4.8
55	43	37.4736842105263	5.52631578947368
56	40	44.0588235294118	-4.05882352941177
57	40	39.2	0.799999999999997
58	39	39.2	-0.200000000000003
59	44	41.6222222222222	2.37777777777778
60	38	37.4736842105263	0.526315789473685
61	39	39.2	-0.200000000000003
62	41	44.0588235294118	-3.05882352941177
63	39	41.6222222222222	-2.62222222222222
64	40	39.2	0.799999999999997
65	44	39.2	4.8
66	42	39.2	2.8
67	46	44.0588235294118	1.94117647058823
68	44	41.6222222222222	2.37777777777778
69	37	41.6222222222222	-4.62222222222222
70	39	37.4736842105263	1.52631578947368
71	40	39.2	0.799999999999997
72	42	41.6222222222222	0.37777777777778
73	37	39.2	-2.2
74	33	37.4736842105263	-4.47368421052632
75	35	39.2	-4.2
76	42	37.4736842105263	4.52631578947368
77	36	37.4736842105263	-1.47368421052632
78	44	41.6222222222222	2.37777777777778
79	45	41.6222222222222	3.37777777777778
80	47	41.6222222222222	5.37777777777778
81	40	41.6222222222222	-1.62222222222222
82	48	44.0588235294118	3.94117647058823
83	45	44.0588235294118	0.941176470588232
84	41	39.2	1.8
85	34	37.4736842105263	-3.47368421052632
86	38	37.4736842105263	0.526315789473685
87	37	39.2	-2.2
88	48	44.0588235294118	3.94117647058823
89	39	41.6222222222222	-2.62222222222222
90	34	41.6222222222222	-7.62222222222222
91	35	37.4736842105263	-2.47368421052632
92	41	41.6222222222222	-0.62222222222222
93	43	39.2	3.8
94	41	39.2	1.8
95	39	37.4736842105263	1.52631578947368
96	36	39.2	-3.2
97	46	41.6222222222222	4.37777777777778
98	42	41.6222222222222	0.37777777777778
99	42	37.4736842105263	4.52631578947368
100	45	39.2	5.8
101	39	41.6222222222222	-2.62222222222222
102	45	41.6222222222222	3.37777777777778
103	48	44.0588235294118	3.94117647058823
104	35	37.4736842105263	-2.47368421052632
105	38	39.2	-1.2
106	42	39.2	2.8
107	36	39.2	-3.2
108	37	39.2	-2.2
109	38	39.2	-1.2
110	43	41.6222222222222	1.37777777777778
111	35	37.4736842105263	-2.47368421052632
112	36	39.2	-3.2
113	33	37.4736842105263	-4.47368421052632
114	39	39.2	-0.200000000000003
115	45	39.2	5.8
116	35	39.2	-4.2
117	38	39.2	-1.2
118	36	39.2	-3.2
119	42	39.2	2.8
120	41	39.2	1.8
121	35	39.2	-4.2
122	43	39.2	3.8
123	40	41.6222222222222	-1.62222222222222
124	46	41.6222222222222	4.37777777777778
125	44	44.0588235294118	-0.058823529411768
126	35	39.2	-4.2

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927695243bxs659brqz4ozu/2mrg01292769635.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927695243bxs659brqz4ozu/2mrg01292769635.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927695243bxs659brqz4ozu/3w0f31292769635.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927695243bxs659brqz4ozu/3w0f31292769635.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927695243bxs659brqz4ozu/47sfo1292769635.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t12927695243bxs659brqz4ozu/47sfo1292769635.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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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