Blog & Share Statistical Computations at FreeStatistics.org

Home » date » 2010 » Dec » 14 »

*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: Tue, 14 Dec 2010 16:05:48 +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/14/t12923434321upyu68eih7xda5.htm/, Retrieved Tue, 14 Dec 2010 17:17:12 +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/14/t12923434321upyu68eih7xda5.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	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.6552
R-squared	0.4292
RMSE	4.31

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	24	25.0714285714286	-1.07142857142857
2	25	20.4230769230769	4.57692307692308
3	17	21.4615384615385	-4.46153846153846
4	18	20.4230769230769	-2.42307692307692
5	18	20.4230769230769	-2.42307692307692
6	16	17.2105263157895	-1.21052631578947
7	20	21.5	-1.5
8	16	20.4230769230769	-4.42307692307692
9	18	17.2105263157895	0.789473684210527
10	17	17.2105263157895	-0.210526315789473
11	23	25.0714285714286	-2.07142857142857
12	30	20.4230769230769	9.57692307692308
13	23	20.4230769230769	2.57692307692308
14	18	20.4230769230769	-2.42307692307692
15	15	20.4230769230769	-5.42307692307692
16	12	21.4615384615385	-9.46153846153846
17	21	20.4230769230769	0.576923076923077
18	15	20.4230769230769	-5.42307692307692
19	20	17.2105263157895	2.78947368421053
20	31	25.0714285714286	5.92857142857143
21	27	25.0714285714286	1.92857142857143
22	34	25.0714285714286	8.92857142857143
23	21	20.4230769230769	0.576923076923077
24	31	25.0714285714286	5.92857142857143
25	19	20.4230769230769	-1.42307692307692
26	16	20.4230769230769	-4.42307692307692
27	20	21.5	-1.5
28	21	20.4230769230769	0.576923076923077
29	22	20.4230769230769	1.57692307692308
30	17	17.2105263157895	-0.210526315789473
31	24	21.5	2.5
32	25	29.8125	-4.8125
33	26	29.8125	-3.8125
34	25	21.4615384615385	3.53846153846154
35	17	20.4230769230769	-3.42307692307692
36	32	29.8125	2.1875
37	33	29.8125	3.1875
38	13	17.2105263157895	-4.21052631578947
39	32	29.8125	2.1875
40	25	29.8125	-4.8125
41	29	29.8125	-0.8125
42	22	20.4230769230769	1.57692307692308
43	18	20.4230769230769	-2.42307692307692
44	17	21.5	-4.5
45	20	21.4615384615385	-1.46153846153846
46	15	20.4230769230769	-5.42307692307692
47	20	25.0714285714286	-5.07142857142857
48	33	29.8125	3.1875
49	29	20.4230769230769	8.57692307692308
50	23	21.5	1.5
51	26	21.5	4.5
52	18	20.4230769230769	-2.42307692307692
53	20	20.4230769230769	-0.423076923076923
54	11	17.2105263157895	-6.21052631578947
55	28	21.4615384615385	6.53846153846154
56	26	25.0714285714286	0.928571428571427
57	22	21.4615384615385	0.53846153846154
58	17	21.5	-4.5
59	12	17.2105263157895	-5.21052631578947
60	14	25.0714285714286	-11.0714285714286
61	17	20.4230769230769	-3.42307692307692
62	21	21.5	-0.5
63	19	21.5	-2.5
64	18	25.0714285714286	-7.07142857142857
65	10	17.2105263157895	-7.21052631578947
66	29	29.8125	-0.8125
67	31	20.4230769230769	10.5769230769231
68	19	21.4615384615385	-2.46153846153846
69	9	17.2105263157895	-8.21052631578947
70	20	20.4230769230769	-0.423076923076923
71	28	17.2105263157895	10.7894736842105
72	19	17.2105263157895	1.78947368421053
73	30	21.4615384615385	8.53846153846154
74	29	25.0714285714286	3.92857142857143
75	26	21.5	4.5
76	23	20.4230769230769	2.57692307692308
77	13	17.2105263157895	-4.21052631578947
78	21	20.4230769230769	0.576923076923077
79	19	20.4230769230769	-1.42307692307692
80	28	21.5	6.5
81	23	25.0714285714286	-2.07142857142857
82	18	20.4230769230769	-2.42307692307692
83	21	20.4230769230769	0.576923076923077
84	20	20.4230769230769	-0.423076923076923
85	23	17.2105263157895	5.78947368421053
86	21	17.2105263157895	3.78947368421053
87	21	21.4615384615385	-0.46153846153846
88	15	25.0714285714286	-10.0714285714286
89	28	29.8125	-1.8125
90	19	17.2105263157895	1.78947368421053
91	26	25.0714285714286	0.928571428571427
92	10	17.2105263157895	-7.21052631578947
93	16	17.2105263157895	-1.21052631578947
94	22	17.2105263157895	4.78947368421053
95	19	20.4230769230769	-1.42307692307692
96	31	21.4615384615385	9.53846153846154
97	31	25.0714285714286	5.92857142857143
98	29	25.0714285714286	3.92857142857143
99	19	17.2105263157895	1.78947368421053
100	22	20.4230769230769	1.57692307692308
101	23	20.4230769230769	2.57692307692308
102	15	17.2105263157895	-2.21052631578947
103	20	20.4230769230769	-0.423076923076923
104	18	20.4230769230769	-2.42307692307692
105	23	25.0714285714286	-2.07142857142857
106	25	17.2105263157895	7.78947368421053
107	21	17.2105263157895	3.78947368421053
108	24	20.4230769230769	3.57692307692308
109	25	25.0714285714286	-0.071428571428573
110	17	17.2105263157895	-0.210526315789473
111	13	17.2105263157895	-4.21052631578947
112	28	20.4230769230769	7.57692307692308
113	21	20.4230769230769	0.576923076923077
114	25	21.4615384615385	3.53846153846154
115	9	21.4615384615385	-12.4615384615385
116	16	17.2105263157895	-1.21052631578947
117	19	20.4230769230769	-1.42307692307692
118	17	17.2105263157895	-0.210526315789473
119	25	25.0714285714286	-0.071428571428573
120	20	17.2105263157895	2.78947368421053
121	29	29.8125	-0.8125
122	14	17.2105263157895	-3.21052631578947
123	22	25.0714285714286	-3.07142857142857
124	15	17.2105263157895	-2.21052631578947
125	19	17.2105263157895	1.78947368421053
126	20	20.4230769230769	-0.423076923076923
127	15	17.2105263157895	-2.21052631578947
128	20	20.4230769230769	-0.423076923076923
129	18	20.4230769230769	-2.42307692307692
130	33	29.8125	3.1875
131	22	20.4230769230769	1.57692307692308
132	16	20.4230769230769	-4.42307692307692
133	17	21.5	-4.5
134	16	17.2105263157895	-1.21052631578947
135	21	20.4230769230769	0.576923076923077
136	26	29.8125	-3.8125
137	18	17.2105263157895	0.789473684210527
138	18	20.4230769230769	-2.42307692307692
139	17	20.4230769230769	-3.42307692307692
140	22	25.0714285714286	-3.07142857142857
141	30	25.0714285714286	4.92857142857143
142	30	29.8125	0.1875
143	24	25.0714285714286	-1.07142857142857
144	21	17.2105263157895	3.78947368421053
145	21	25.0714285714286	-4.07142857142857
146	29	29.8125	-0.8125
147	31	20.4230769230769	10.5769230769231
148	20	17.2105263157895	2.78947368421053
149	16	20.4230769230769	-4.42307692307692
150	22	20.4230769230769	1.57692307692308
151	20	21.4615384615385	-1.46153846153846
152	28	25.0714285714286	2.92857142857143
153	38	29.8125	8.1875
154	22	17.2105263157895	4.78947368421053
155	20	20.4230769230769	-0.423076923076923
156	17	17.2105263157895	-0.210526315789473
157	28	25.0714285714286	2.92857142857143
158	22	25.0714285714286	-3.07142857142857
159	31	25.0714285714286	5.92857142857143

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t12923434321upyu68eih7xda5/23ybn1292342740.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t12923434321upyu68eih7xda5/23ybn1292342740.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t12923434321upyu68eih7xda5/33ybn1292342740.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t12923434321upyu68eih7xda5/33ybn1292342740.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t12923434321upyu68eih7xda5/4d8tq1292342740.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t12923434321upyu68eih7xda5/4d8tq1292342740.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 0 ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = 0 ; 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.

FreeStatistics.org is powered by