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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: Sun, 12 Dec 2010 13:46:39 +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/12/t1292161476edh73f3fvoibc2g.htm/, Retrieved Sun, 12 Dec 2010 14:44:36 +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/t1292161476edh73f3fvoibc2g.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 24 5 3 25 11 7 25 4 2 17 6 17 30 4 4 18 12 10 19 4 2 18 8 12 22 2 4 16 10 12 22 5 2 20 10 11 25 5 3 16 11 11 23 4 4 18 16 12 17 3 1 17 11 13 21 4 2 23 13 14 19 4 2 30 12 16 19 NA 2 23 8 11 15 3 2 18 12 10 16 3 1 15 11 11 23 4 2 12 4 15 27 5 4 21 9 9 22 4 2 15 8 11 14 2 2 20 8 17 22 4 3 31 14 17 23 4 2 27 15 11 23 4 3 34 16 18 21 4 2 21 9 14 19 4 3 31 14 10 18 4 2 19 11 11 20 5 3 16 8 15 23 4 3 20 9 15 25 4 4 21 9 13 19 4 2 22 9 16 24 4 4 17 9 13 22 4 3 24 10 9 25 4 4 25 16 18 26 4 3 26 11 18 29 2 4 25 8 12 32 4 4 17 9 17 25 4 3 32 16 9 29 5 5 33 11 9 28 5 4 13 16 12 17 4 2 32 12 18 28 3 4 25 12 12 29 4 4 29 14 18 26 5 5 22 9 14 25 4 4 18 10 15 14 4 2 17 9 16 25 5 4 20 10 10 26 4 4 15 12 11 20 5 1 20 14 14 18 4 2 33 14 9 32 4 5 29 10 12 25 4 4 23 14 17 25 3 2 26 16 5 23 4 3 18 9 12 21 4 4 20 10 12 20 4 2 11 6 6 15 2 1 28 8 24 30 3 4 26 13 12 24 5 2 22 10 12 26 4 4 17 8 14 24 4 2 12 7 7 22 5 3 14 15 13 14 3 2 17 9 12 24 5 3 21 10 13 24 4 2 1 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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.7708
R-squared	0.5941
RMSE	2.6782

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	24	22.3783783783784	1.62162162162162
2	25	20.1296296296296	4.87037037037037
3	30	25.5365853658537	4.46341463414634
4	19	20.1296296296296	-1.12962962962963
5	22	25.5365853658537	-3.53658536585366
6	22	20.1296296296296	1.87037037037037
7	25	22.3783783783784	2.62162162162162
8	23	25.5365853658537	-2.53658536585366
9	17	17.25	-0.25
10	21	20.1296296296296	0.87037037037037
11	19	20.1296296296296	-1.12962962962963
12	19	20.1296296296296	-1.12962962962963
13	15	17.25	-2.25
14	16	17.25	-1.25
15	23	20.1296296296296	2.87037037037037
16	27	25.5365853658537	1.46341463414634
17	22	20.1296296296296	1.87037037037037
18	14	17.25	-3.25
19	22	22.3783783783784	-0.378378378378379
20	23	20.1296296296296	2.87037037037037
21	23	22.3783783783784	0.621621621621621
22	21	20.1296296296296	0.87037037037037
23	19	22.3783783783784	-3.37837837837838
24	18	20.1296296296296	-2.12962962962963
25	20	22.3783783783784	-2.37837837837838
26	23	22.3783783783784	0.621621621621621
27	25	25.5365853658537	-0.536585365853657
28	19	20.1296296296296	-1.12962962962963
29	24	25.5365853658537	-1.53658536585366
30	22	22.3783783783784	-0.378378378378379
31	25	25.5365853658537	-0.536585365853657
32	26	22.3783783783784	3.62162162162162
33	29	25.5365853658537	3.46341463414634
34	32	25.5365853658537	6.46341463414634
35	25	22.3783783783784	2.62162162162162
36	29	30.5714285714286	-1.57142857142857
37	28	25.5365853658537	2.46341463414634
38	17	20.1296296296296	-3.12962962962963
39	28	25.5365853658537	2.46341463414634
40	29	25.5365853658537	3.46341463414634
41	26	30.5714285714286	-4.57142857142857
42	25	25.5365853658537	-0.536585365853657
43	14	20.1296296296296	-6.12962962962963
44	25	25.5365853658537	-0.536585365853657
45	26	25.5365853658537	0.463414634146343
46	20	20.1296296296296	-0.129629629629630
47	18	20.1296296296296	-2.12962962962963
48	32	30.5714285714286	1.42857142857143
49	25	25.5365853658537	-0.536585365853657
50	25	17.25	7.75
51	23	22.3783783783784	0.621621621621621
52	21	25.5365853658537	-4.53658536585366
53	20	20.1296296296296	-0.129629629629630
54	15	17.25	-2.25
55	30	25.5365853658537	4.46341463414634
56	24	20.1296296296296	3.87037037037037
57	26	25.5365853658537	0.463414634146343
58	24	20.1296296296296	3.87037037037037
59	22	22.3783783783784	-0.378378378378379
60	14	17.25	-3.25
61	24	22.3783783783784	1.62162162162162
62	24	20.1296296296296	3.87037037037037
63	24	20.1296296296296	3.87037037037037
64	24	22.3783783783784	1.62162162162162
65	19	20.1296296296296	-1.12962962962963
66	31	30.5714285714286	0.428571428571427
67	22	25.5365853658537	-3.53658536585366
68	27	25.5365853658537	1.46341463414634
69	19	20.1296296296296	-1.12962962962963
70	25	20.1296296296296	4.87037037037037
71	20	17.25	2.75
72	21	20.1296296296296	0.87037037037037
73	27	25.5365853658537	1.46341463414634
74	23	20.1296296296296	2.87037037037037
75	25	22.3783783783784	2.62162162162162
76	20	20.1296296296296	-0.129629629629630
77	21	20.1296296296296	0.87037037037037
78	22	20.1296296296296	1.87037037037037
79	23	25.5365853658537	-2.53658536585366
80	25	22.3783783783784	2.62162162162162
81	25	22.3783783783784	2.62162162162162
82	17	17.25	-0.25
83	19	20.1296296296296	-1.12962962962963
84	25	22.3783783783784	2.62162162162162
85	19	20.1296296296296	-1.12962962962963
86	20	20.1296296296296	-0.129629629629630
87	26	22.3783783783784	3.62162162162162
88	23	25.5365853658537	-2.53658536585366
89	27	25.5365853658537	1.46341463414634
90	17	20.1296296296296	-3.12962962962963
91	17	20.1296296296296	-3.12962962962963
92	19	20.1296296296296	-1.12962962962963
93	17	17.25	-0.25
94	22	22.3783783783784	-0.378378378378379
95	21	20.1296296296296	0.87037037037037
96	32	30.5714285714286	1.42857142857143
97	21	20.1296296296296	0.87037037037037
98	21	25.5365853658537	-4.53658536585366
99	18	22.3783783783784	-4.37837837837838
100	18	22.3783783783784	-4.37837837837838
101	23	22.3783783783784	0.621621621621621
102	19	20.1296296296296	-1.12962962962963
103	20	22.3783783783784	-2.37837837837838
104	21	20.1296296296296	0.87037037037037
105	20	20.1296296296296	-0.129629629629630
106	17	17.25	-0.25
107	18	20.1296296296296	-2.12962962962963
108	19	17.25	1.75
109	22	22.3783783783784	-0.378378378378379
110	15	20.1296296296296	-5.12962962962963
111	14	17.25	-3.25
112	18	20.1296296296296	-2.12962962962963
113	24	17.25	6.75
114	35	30.5714285714286	4.42857142857143
115	29	25.5365853658537	3.46341463414634
116	21	22.3783783783784	-1.37837837837838
117	25	25.5365853658537	-0.536585365853657
118	20	17.25	2.75
119	22	20.1296296296296	1.87037037037037
120	13	17.25	-4.25
121	26	25.5365853658537	0.463414634146343
122	17	17.25	-0.25
123	25	25.5365853658537	-0.536585365853657
124	20	20.1296296296296	-0.129629629629630
125	19	20.1296296296296	-1.12962962962963
126	21	22.3783783783784	-1.37837837837838
127	22	20.1296296296296	1.87037037037037
128	24	22.3783783783784	1.62162162162162
129	21	20.1296296296296	0.87037037037037
130	26	25.5365853658537	0.463414634146343
131	24	22.3783783783784	1.62162162162162
132	16	20.1296296296296	-4.12962962962963
133	23	25.5365853658537	-2.53658536585366
134	18	22.3783783783784	-4.37837837837838
135	16	20.1296296296296	-4.12962962962963
136	26	25.5365853658537	0.463414634146343
137	19	17.25	1.75
138	21	20.1296296296296	0.87037037037037
139	21	20.1296296296296	0.87037037037037
140	22	17.25	4.75
141	23	22.3783783783784	0.621621621621621
142	29	25.5365853658537	3.46341463414634
143	21	25.5365853658537	-4.53658536585366
144	21	25.5365853658537	-4.53658536585366
145	23	22.3783783783784	0.621621621621621
146	27	25.5365853658537	1.46341463414634
147	25	25.5365853658537	-0.536585365853657
148	21	22.3783783783784	-1.37837837837838
149	10	17.25	-7.25
150	20	20.1296296296296	-0.129629629629630
151	26	25.5365853658537	0.463414634146343
152	24	22.3783783783784	1.62162162162162
153	29	30.5714285714286	-1.57142857142857
154	19	22.3783783783784	-3.37837837837838
155	24	25.5365853658537	-1.53658536585366
156	19	20.1296296296296	-1.12962962962963
157	24	25.5365853658537	-1.53658536585366
158	22	22.3783783783784	-0.378378378378379
159	17	22.3783783783784	-5.37837837837838

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292161476edh73f3fvoibc2g/2bwo81292161591.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292161476edh73f3fvoibc2g/2bwo81292161591.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292161476edh73f3fvoibc2g/3bwo81292161591.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292161476edh73f3fvoibc2g/3bwo81292161591.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292161476edh73f3fvoibc2g/44onb1292161591.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292161476edh73f3fvoibc2g/44onb1292161591.ps (open in new window)

Parameters (Session):

par1 = 4 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 4 ; 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

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

We may request personal information to be submitted to our servers in order to be able to:

personalize online software applications according to your needs
enforce strict security rules with respect to the data that you upload (e.g. statistical data)
manage user sessions of online applications
alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

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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