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*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: Fri, 24 Dec 2010 16:49:02 +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/24/t1293209585lm77qat6i6ujz1n.htm/, Retrieved Fri, 24 Dec 2010 17:53:09 +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/24/t1293209585lm77qat6i6ujz1n.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	8 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Goodness of Fit
Correlation	0.4918
R-squared	0.2418
RMSE	3.3895

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	26	21.65	4.35
2	23	24.2698412698413	-1.26984126984127
3	25	24.2698412698413	0.730158730158731
4	23	21.4583333333333	1.54166666666667
5	19	21.65	-2.65
6	29	24.2698412698413	4.73015873015873
7	25	24.2698412698413	0.730158730158731
8	21	21.65	-0.649999999999999
9	22	21.4583333333333	0.541666666666668
10	25	24.2698412698413	0.730158730158731
11	24	21.65	2.35
12	18	21.65	-3.65
13	22	17.3333333333333	4.66666666666667
14	15	17.3333333333333	-2.33333333333333
15	22	24.2698412698413	-2.26984126984127
16	28	24.2698412698413	3.73015873015873
17	20	21.65	-1.65
18	12	17.3333333333333	-5.33333333333333
19	24	24.2698412698413	-0.269841269841269
20	20	21.65	-1.65
21	21	21.65	-0.649999999999999
22	20	21.65	-1.65
23	21	21.65	-0.649999999999999
24	23	21.4583333333333	1.54166666666667
25	28	24.2698412698413	3.73015873015873
26	24	21.65	2.35
27	24	24.2698412698413	-0.269841269841269
28	24	21.65	2.35
29	23	21.65	1.35000000000000
30	23	24.2698412698413	-1.26984126984127
31	29	24.2698412698413	4.73015873015873
32	24	21.65	2.35
33	18	24.2698412698413	-6.26984126984127
34	25	24.2698412698413	0.730158730158731
35	21	21.65	-0.649999999999999
36	26	24.2698412698413	1.73015873015873
37	22	24.2698412698413	-2.26984126984127
38	22	21.4583333333333	0.541666666666668
39	22	21.65	0.350000000000001
40	23	24.2698412698413	-1.26984126984127
41	30	21.65	8.35
42	23	21.65	1.35000000000000
43	17	17.3333333333333	-0.333333333333332
44	23	24.2698412698413	-1.26984126984127
45	23	24.2698412698413	-1.26984126984127
46	25	24.2698412698413	0.730158730158731
47	24	21.65	2.35
48	24	24.2698412698413	-0.269841269841269
49	23	24.2698412698413	-1.26984126984127
50	21	24.2698412698413	-3.26984126984127
51	24	24.2698412698413	-0.269841269841269
52	24	24.2698412698413	-0.269841269841269
53	28	24.2698412698413	3.73015873015873
54	16	17.3333333333333	-1.33333333333333
55	20	21.65	-1.65
56	29	24.2698412698413	4.73015873015873
57	27	24.2698412698413	2.73015873015873
58	22	24.2698412698413	-2.26984126984127
59	28	24.2698412698413	3.73015873015873
60	16	17.3333333333333	-1.33333333333333
61	25	21.65	3.35
62	24	24.2698412698413	-0.269841269841269
63	28	24.2698412698413	3.73015873015873
64	24	21.65	2.35
65	23	21.4583333333333	1.54166666666667
66	30	24.2698412698413	5.73015873015873
67	24	24.2698412698413	-0.269841269841269
68	21	24.2698412698413	-3.26984126984127
69	25	21.4583333333333	3.54166666666667
70	25	24.2698412698413	0.730158730158731
71	22	24.2698412698413	-2.26984126984127
72	23	24.2698412698413	-1.26984126984127
73	26	21.65	4.35
74	23	21.65	1.35000000000000
75	25	24.2698412698413	0.730158730158731
76	21	24.2698412698413	-3.26984126984127
77	25	24.2698412698413	0.730158730158731
78	24	21.65	2.35
79	29	24.2698412698413	4.73015873015873
80	22	24.2698412698413	-2.26984126984127
81	27	21.65	5.35
82	26	21.4583333333333	4.54166666666667
83	22	21.4583333333333	0.541666666666668
84	24	21.65	2.35
85	27	21.4583333333333	5.54166666666667
86	24	24.2698412698413	-0.269841269841269
87	24	24.2698412698413	-0.269841269841269
88	29	24.2698412698413	4.73015873015873
89	22	21.65	0.350000000000001
90	21	21.4583333333333	-0.458333333333332
91	24	21.4583333333333	2.54166666666667
92	24	21.4583333333333	2.54166666666667
93	23	21.4583333333333	1.54166666666667
94	20	24.2698412698413	-4.26984126984127
95	27	21.65	5.35
96	26	21.65	4.35
97	25	21.65	3.35
98	21	21.65	-0.649999999999999
99	21	21.4583333333333	-0.458333333333332
100	19	21.4583333333333	-2.45833333333333
101	21	21.65	-0.649999999999999
102	21	21.4583333333333	-0.458333333333332
103	16	21.65	-5.65
104	22	21.65	0.350000000000001
105	29	21.65	7.35
106	15	21.4583333333333	-6.45833333333333
107	17	21.4583333333333	-4.45833333333333
108	15	21.65	-6.65
109	21	21.65	-0.649999999999999
110	21	17.3333333333333	3.66666666666667
111	19	17.3333333333333	1.66666666666667
112	24	21.65	2.35
113	20	21.65	-1.65
114	17	21.65	-4.65
115	23	21.65	1.35000000000000
116	24	24.2698412698413	-0.269841269841269
117	14	24.2698412698413	-10.2698412698413
118	19	24.2698412698413	-5.26984126984127
119	24	21.65	2.35
120	13	17.3333333333333	-4.33333333333333
121	22	24.2698412698413	-2.26984126984127
122	16	21.4583333333333	-5.45833333333333
123	19	21.65	-2.65
124	25	24.2698412698413	0.730158730158731
125	25	21.4583333333333	3.54166666666667
126	23	24.2698412698413	-1.26984126984127
127	24	24.2698412698413	-0.269841269841269
128	26	24.2698412698413	1.73015873015873
129	26	21.65	4.35
130	25	24.2698412698413	0.730158730158731
131	18	21.65	-3.65
132	21	17.3333333333333	3.66666666666667
133	26	24.2698412698413	1.73015873015873
134	23	21.4583333333333	1.54166666666667
135	23	17.3333333333333	5.66666666666667
136	22	21.65	0.350000000000001
137	20	21.4583333333333	-1.45833333333333
138	13	21.65	-8.65
139	24	21.65	2.35
140	15	21.65	-6.65
141	14	21.65	-7.65
142	22	21.65	0.350000000000001
143	10	21.65	-11.65
144	24	24.2698412698413	-0.269841269841269
145	22	21.65	0.350000000000001
146	24	24.2698412698413	-0.269841269841269
147	19	21.65	-2.65
148	20	24.2698412698413	-4.26984126984127
149	13	17.3333333333333	-4.33333333333333
150	20	21.65	-1.65
151	22	21.65	0.350000000000001
152	24	24.2698412698413	-0.269841269841269
153	29	24.2698412698413	4.73015873015873
154	12	21.4583333333333	-9.45833333333333
155	20	21.65	-1.65
156	21	21.4583333333333	-0.458333333333332
157	24	24.2698412698413	-0.269841269841269
158	22	21.65	0.350000000000001
159	20	21.65	-1.65

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293209585lm77qat6i6ujz1n/23nsh1293209333.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293209585lm77qat6i6ujz1n/23nsh1293209333.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293209585lm77qat6i6ujz1n/33nsh1293209333.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293209585lm77qat6i6ujz1n/33nsh1293209333.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293209585lm77qat6i6ujz1n/4ew9k1293209333.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293209585lm77qat6i6ujz1n/4ew9k1293209333.ps (open in new window)

Parameters (Session):

par1 = 6 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 6 ; 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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We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

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