Home » date » 2010 » Dec » 16 »

Recursive Partitioning Doubts (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: Thu, 16 Dec 2010 22:25:11 +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/16/t129253821513pbvfxps4r59hv.htm/, Retrieved Thu, 16 Dec 2010 23:23:35 +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/t129253821513pbvfxps4r59hv.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	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.451
R-squared	0.2034
RMSE	2.4605

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	9	12.1818181818182	-3.18181818181818
2	9	10.4102564102564	-1.41025641025641
3	9	8.06666666666667	0.933333333333334
4	14	12.1818181818182	1.81818181818182
5	8	10.4102564102564	-2.41025641025641
6	8	10.4102564102564	-2.41025641025641
7	11	12.1818181818182	-1.18181818181818
8	10	10.4102564102564	-0.410256410256411
9	9	10.4102564102564	-1.41025641025641
10	15	12.1818181818182	2.81818181818182
11	14	10.4102564102564	3.58974358974359
12	11	10.4102564102564	0.58974358974359
13	14	12.1818181818182	1.81818181818182
14	6	8.06666666666667	-2.06666666666667
15	20	10.4102564102564	9.58974358974359
16	9	10.4102564102564	-1.41025641025641
17	10	12.1818181818182	-2.18181818181818
18	8	12.1818181818182	-4.18181818181818
19	11	12.1818181818182	-1.18181818181818
20	14	12.1818181818182	1.81818181818182
21	11	12.1818181818182	-1.18181818181818
22	16	12.1818181818182	3.81818181818182
23	14	12.1818181818182	1.81818181818182
24	11	12.1818181818182	-1.18181818181818
25	11	10.4102564102564	0.58974358974359
26	12	10.4102564102564	1.58974358974359
27	9	10.4102564102564	-1.41025641025641
28	7	12.1818181818182	-5.18181818181818
29	13	12.1818181818182	0.818181818181818
30	10	12.1818181818182	-2.18181818181818
31	9	10.4102564102564	-1.41025641025641
32	9	10.4102564102564	-1.41025641025641
33	16	12.1818181818182	3.81818181818182
34	12	10.4102564102564	1.58974358974359
35	6	10.4102564102564	-4.41025641025641
36	14	12.1818181818182	1.81818181818182
37	14	10.4102564102564	3.58974358974359
38	10	12.1818181818182	-2.18181818181818
39	4	8.06666666666667	-4.06666666666667
40	12	12.1818181818182	-0.181818181818182
41	12	12.1818181818182	-0.181818181818182
42	14	10.4102564102564	3.58974358974359
43	9	10.4102564102564	-1.41025641025641
44	9	10.4102564102564	-1.41025641025641
45	10	10.4102564102564	-0.410256410256411
46	14	12.1818181818182	1.81818181818182
47	10	10.4102564102564	-0.410256410256411
48	9	10.4102564102564	-1.41025641025641
49	14	12.1818181818182	1.81818181818182
50	8	10.4102564102564	-2.41025641025641
51	9	12.1818181818182	-3.18181818181818
52	8	12.1818181818182	-4.18181818181818
53	9	10.4102564102564	-1.41025641025641
54	9	10.4102564102564	-1.41025641025641
55	9	10.4102564102564	-1.41025641025641
56	15	10.4102564102564	4.58974358974359
57	8	10.4102564102564	-2.41025641025641
58	10	10.4102564102564	-0.410256410256411
59	8	12.1818181818182	-4.18181818181818
60	14	12.1818181818182	1.81818181818182
61	11	12.1818181818182	-1.18181818181818
62	10	8.06666666666667	1.93333333333333
63	12	8.06666666666667	3.93333333333333
64	14	12.1818181818182	1.81818181818182
65	9	10.4102564102564	-1.41025641025641
66	13	10.4102564102564	2.58974358974359
67	15	10.4102564102564	4.58974358974359
68	8	8.06666666666667	-0.0666666666666664
69	7	8.06666666666667	-1.06666666666667
70	10	10.4102564102564	-0.410256410256411
71	10	10.4102564102564	-0.410256410256411
72	13	12.1818181818182	0.818181818181818
73	11	12.1818181818182	-1.18181818181818
74	8	10.4102564102564	-2.41025641025641
75	12	10.4102564102564	1.58974358974359
76	9	8.06666666666667	0.933333333333334
77	10	12.1818181818182	-2.18181818181818
78	11	10.4102564102564	0.58974358974359
79	11	10.4102564102564	0.58974358974359
80	10	10.4102564102564	-0.410256410256411
81	16	12.1818181818182	3.81818181818182
82	16	10.4102564102564	5.58974358974359
83	8	10.4102564102564	-2.41025641025641
84	6	8.06666666666667	-2.06666666666667
85	11	10.4102564102564	0.58974358974359
86	12	10.4102564102564	1.58974358974359
87	14	12.1818181818182	1.81818181818182
88	9	10.4102564102564	-1.41025641025641
89	11	10.4102564102564	0.58974358974359
90	8	10.4102564102564	-2.41025641025641
91	8	12.1818181818182	-4.18181818181818
92	7	10.4102564102564	-3.41025641025641
93	16	12.1818181818182	3.81818181818182
94	13	12.1818181818182	0.818181818181818
95	8	8.06666666666667	-0.0666666666666664
96	11	10.4102564102564	0.58974358974359
97	14	12.1818181818182	1.81818181818182
98	10	10.4102564102564	-0.410256410256411
99	10	8.06666666666667	1.93333333333333
100	14	12.1818181818182	1.81818181818182
101	14	12.1818181818182	1.81818181818182
102	10	10.4102564102564	-0.410256410256411
103	12	12.1818181818182	-0.181818181818182
104	9	12.1818181818182	-3.18181818181818
105	16	10.4102564102564	5.58974358974359
106	8	10.4102564102564	-2.41025641025641
107	9	8.06666666666667	0.933333333333334
108	16	12.1818181818182	3.81818181818182
109	13	10.4102564102564	2.58974358974359
110	13	10.4102564102564	2.58974358974359
111	8	10.4102564102564	-2.41025641025641
112	14	12.1818181818182	1.81818181818182
113	11	10.4102564102564	0.58974358974359
114	9	10.4102564102564	-1.41025641025641
115	8	10.4102564102564	-2.41025641025641
116	13	10.4102564102564	2.58974358974359
117	13	12.1818181818182	0.818181818181818
118	10	10.4102564102564	-0.410256410256411
119	8	8.06666666666667	-0.0666666666666664
120	7	10.4102564102564	-3.41025641025641
121	11	10.4102564102564	0.58974358974359
122	11	10.4102564102564	0.58974358974359
123	14	12.1818181818182	1.81818181818182
124	6	10.4102564102564	-4.41025641025641
125	10	10.4102564102564	-0.410256410256411
126	9	12.1818181818182	-3.18181818181818
127	12	10.4102564102564	1.58974358974359
128	11	10.4102564102564	0.58974358974359
129	14	10.4102564102564	3.58974358974359
130	12	12.1818181818182	-0.181818181818182
131	8	12.1818181818182	-4.18181818181818
132	14	12.1818181818182	1.81818181818182
133	8	12.1818181818182	-4.18181818181818
134	11	10.4102564102564	0.58974358974359
135	12	12.1818181818182	-0.181818181818182
136	9	8.06666666666667	0.933333333333334
137	16	12.1818181818182	3.81818181818182
138	11	10.4102564102564	0.58974358974359
139	11	10.4102564102564	0.58974358974359
140	12	10.4102564102564	1.58974358974359
141	15	12.1818181818182	2.81818181818182
142	13	10.4102564102564	2.58974358974359
143	6	8.06666666666667	-2.06666666666667
144	11	12.1818181818182	-1.18181818181818
145	7	10.4102564102564	-3.41025641025641
146	8	10.4102564102564	-2.41025641025641
147	8	10.4102564102564	-2.41025641025641
148	9	10.4102564102564	-1.41025641025641

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/16/t129253821513pbvfxps4r59hv/252lb1292538303.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t129253821513pbvfxps4r59hv/252lb1292538303.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t129253821513pbvfxps4r59hv/352lb1292538303.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t129253821513pbvfxps4r59hv/352lb1292538303.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t129253821513pbvfxps4r59hv/4gt3w1292538303.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t129253821513pbvfxps4r59hv/4gt3w1292538303.ps (open in new window)

Parameters (Session):

par1 = 4 ; par2 = none ; par3 = 2 ; par4 = no ;

Parameters (R input):

par1 = 4 ; 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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