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WS10 RP

*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, 12 Dec 2010 20:20:40 +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/12/t1292185141bsqpvo3s9djjy16.htm/, Retrieved Sun, 12 Dec 2010 21:19:01 +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/12/t1292185141bsqpvo3s9djjy16.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 «

5 2 1 3 73 62 66 12 1 1 1 58 54 54 11 1 1 3 68 41 82 6 1 1 3 62 49 61 12 1 2 3 65 49 65 11 1 1 3 81 72 77 12 1 1 1 73 78 66 7 2 4 3 64 58 66 8 1 1 3 68 58 66 13 1 1 1 51 23 48 12 1 1 1 68 39 57 13 1 1 3 61 63 80 12 1 1 1 69 46 60 12 1 3 3 73 58 70 11 2 1 3 61 39 85 12 2 1 1 62 44 59 12 1 1 1 63 49 72 12 1 6 1 69 57 70 11 2 1 3 47 76 74 13 2 1 1 66 63 70 9 1 1 3 58 18 51 11 2 1 3 63 40 70 11 1 1 1 69 59 71 11 2 1 3 59 62 72 9 1 1 1 59 70 50 11 2 1 4 63 65 69 12 2 1 3 65 56 73 12 1 1 3 65 45 66 10 2 1 3 71 57 73 12 1 4 3 60 50 58 12 2 1 1 81 40 78 12 1 1 3 67 58 83 9 2 1 3 66 49 76 9 1 1 3 62 49 77 12 1 1 3 63 27 79 14 2 1 1 73 51 71 12 2 1 3 55 75 79 11 1 1 1 59 65 60 9 1 1 2 64 47 73 11 2 1 3 63 49 70 7 1 1 1 64 65 42 15 1 1 1 73 61 74 11 1 1 3 54 46 68 12 1 1 3 76 69 83 12 2 2 1 74 55 62 9 2 1 3 63 78 79 12 2 1 3 73 58 61 11 2 1 3 67 34 86 11 2 2 3 68 67 64 8 1 4 3 66 45 75 7 2 1 1 62 68 59 12 2 4 3 71 49 82 8 1 1 2 63 19 61 10 1 1 1 75 72 69 12 1 2 2 77 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	NA
R-squared	NA
RMSE	1.7904

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	5	11	-6
2	12	11	1
3	11	11	0
4	6	11	-5
5	12	11	1
6	11	11	0
7	12	11	1
8	7	11	-4
9	8	11	-3
10	13	11	2
11	12	11	1
12	13	11	2
13	12	11	1
14	12	11	1
15	11	11	0
16	12	11	1
17	12	11	1
18	12	11	1
19	11	11	0
20	13	11	2
21	9	11	-2
22	11	11	0
23	11	11	0
24	11	11	0
25	9	11	-2
26	11	11	0
27	12	11	1
28	12	11	1
29	10	11	-1
30	12	11	1
31	12	11	1
32	12	11	1
33	9	11	-2
34	9	11	-2
35	12	11	1
36	14	11	3
37	12	11	1
38	11	11	0
39	9	11	-2
40	11	11	0
41	7	11	-4
42	15	11	4
43	11	11	0
44	12	11	1
45	12	11	1
46	9	11	-2
47	12	11	1
48	11	11	0
49	11	11	0
50	8	11	-3
51	7	11	-4
52	12	11	1
53	8	11	-3
54	10	11	-1
55	12	11	1
56	15	11	4
57	12	11	1
58	12	11	1
59	12	11	1
60	12	11	1
61	8	11	-3
62	10	11	-1
63	14	11	3
64	10	11	-1
65	12	11	1
66	14	11	3
67	6	11	-5
68	11	11	0
69	10	11	-1
70	14	11	3
71	12	11	1
72	13	11	2
73	11	11	0
74	11	11	0
75	12	11	1
76	13	11	2
77	12	11	1
78	8	11	-3
79	12	11	1
80	11	11	0
81	10	11	-1
82	12	11	1
83	11	11	0
84	12	11	1
85	12	11	1
86	10	11	-1
87	12	11	1
88	12	11	1
89	11	11	0
90	10	11	-1
91	12	11	1
92	11	11	0
93	12	11	1
94	12	11	1
95	10	11	-1
96	11	11	0
97	10	11	-1
98	11	11	0
99	11	11	0
100	12	11	1
101	11	11	0
102	11	11	0
103	7	11	-4
104	12	11	1
105	8	11	-3
106	10	11	-1
107	12	11	1
108	11	11	0
109	13	11	2
110	9	11	-2
111	11	11	0
112	13	11	2
113	8	11	-3
114	12	11	1
115	11	11	0
116	11	11	0
117	12	11	1
118	13	11	2
119	11	11	0
120	10	11	-1
121	10	11	-1
122	10	11	-1
123	12	11	1
124	12	11	1
125	13	11	2
126	11	11	0
127	11	11	0
128	12	11	1
129	9	11	-2
130	11	11	0
131	12	11	1
132	12	11	1
133	13	11	2
134	6	11	-5
135	11	11	0
136	10	11	-1
137	12	11	1
138	11	11	0
139	12	11	1
140	12	11	1
141	7	11	-4
142	12	11	1
143	12	11	1
144	9	11	-2
145	12	11	1
146	12	11	1

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292185141bsqpvo3s9djjy16/2wr1j1292185232.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292185141bsqpvo3s9djjy16/2wr1j1292185232.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292185141bsqpvo3s9djjy16/3wr1j1292185232.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292185141bsqpvo3s9djjy16/3wr1j1292185232.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292185141bsqpvo3s9djjy16/4p0i41292185232.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292185141bsqpvo3s9djjy16/4p0i41292185232.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = none ; 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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