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workshop 10d

*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 13:18:21 +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/t1292159843ye797bfspmoru4w.htm/, Retrieved Sun, 12 Dec 2010 14:17:26 +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/t1292159843ye797bfspmoru4w.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 «

11 6 6 4 15 16 2 40 37 15 10 77 26 16 5 4 23 24 1 29 31 9 20 63 26 13 20 10 26 22 1 37 35 12 16 73 15 7 12 6 19 21 1 32 36 15 10 76 10 10 11 5 19 23 1 39 32 17 8 90 21 10 12 8 16 23 1 32 30 14 14 67 27 15 11 9 23 21 2 35 34 9 19 69 21 9 9 9 22 20 2 35 34 12 15 70 21 12 13 8 19 22 1 28 22 11 23 54 21 8 9 11 24 20 1 37 27 13 9 54 22 9 14 6 19 12 2 32 27 16 12 76 29 10 12 8 25 23 2 34 33 16 14 75 29 15 18 11 23 23 2 37 38 15 13 76 29 11 9 5 31 30 1 35 37 10 11 80 30 12 15 10 29 22 2 40 31 16 11 89 19 9 12 7 18 21 1 37 36 12 10 73 19 10 12 7 17 21 1 37 38 15 12 74 22 13 12 13 22 15 2 33 31 13 18 78 18 8 15 10 21 22 2 37 34 18 12 76 28 14 11 8 24 24 1 35 33 13 10 69 17 9 13 6 22 23 2 36 38 17 15 74 18 12 10 8 16 15 2 32 28 14 15 82 20 8 17 7 22 24 1 38 34 13 12 77 16 8 13 5 21 24 2 34 32 13 9 84 17 9 17 9 25 21 2 33 34 15 11 75 25 14 15 11 22 21 2 33 39 15 16 79 22 11 13 11 24 18 2 42 37 13 17 79 34 16 18 11 21 20 2 33 34 14 12 69 31 9 17 9 25 19 2 32 41 13 11 88 38 11 21 7 29 29 2 32 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	7 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Goodness of Fit
Correlation	0.5398
R-squared	0.2913
RMSE	3.5869

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	15	17.2	-2.2
2	23	25.0869565217391	-2.08695652173913
3	26	25.0869565217391	0.913043478260871
4	19	21.5730337078652	-2.57303370786517
5	19	21.5730337078652	-2.57303370786517
6	16	21.5730337078652	-5.57303370786517
7	23	25.0869565217391	-2.08695652173913
8	22	21.5730337078652	0.426966292134832
9	19	21.5730337078652	-2.57303370786517
10	24	21.5730337078652	2.42696629213483
11	19	17.2	1.8
12	25	25.0869565217391	-0.086956521739129
13	23	25.0869565217391	-2.08695652173913
14	31	25.0869565217391	5.91304347826087
15	29	25.0869565217391	3.91304347826087
16	18	21.5730337078652	-3.57303370786517
17	17	21.5730337078652	-4.57303370786517
18	22	17.2	4.8
19	21	21.5730337078652	-0.573033707865168
20	24	25.0869565217391	-1.08695652173913
21	22	21.5730337078652	0.426966292134832
22	16	17.2	-1.2
23	22	21.5730337078652	0.426966292134832
24	21	21.5730337078652	-0.573033707865168
25	25	21.5730337078652	3.42696629213483
26	22	25.0869565217391	-3.08695652173913
27	24	21.5730337078652	2.42696629213483
28	21	25.0869565217391	-4.08695652173913
29	25	25.0869565217391	-0.086956521739129
30	29	25.0869565217391	3.91304347826087
31	19	21.5730337078652	-2.57303370786517
32	29	25.0869565217391	3.91304347826087
33	25	21.5730337078652	3.42696629213483
34	19	21.5730337078652	-2.57303370786517
35	27	21.5730337078652	5.42696629213483
36	25	21.5730337078652	3.42696629213483
37	23	21.5730337078652	1.42696629213483
38	24	21.5730337078652	2.42696629213483
39	23	21.5730337078652	1.42696629213483
40	25	21.5730337078652	3.42696629213483
41	23	25.0869565217391	-2.08695652173913
42	22	21.5730337078652	0.426966292134832
43	32	25.0869565217391	6.91304347826087
44	22	25.0869565217391	-3.08695652173913
45	18	21.5730337078652	-3.57303370786517
46	19	21.5730337078652	-2.57303370786517
47	23	21.5730337078652	1.42696629213483
48	19	21.5730337078652	-2.57303370786517
49	16	21.5730337078652	-5.57303370786517
50	23	21.5730337078652	1.42696629213483
51	17	25.0869565217391	-8.08695652173913
52	17	21.5730337078652	-4.57303370786517
53	28	25.0869565217391	2.91304347826087
54	24	21.5730337078652	2.42696629213483
55	21	17.2	3.8
56	14	21.5730337078652	-7.57303370786517
57	21	17.2	3.8
58	20	21.5730337078652	-1.57303370786517
59	25	21.5730337078652	3.42696629213483
60	20	21.5730337078652	-1.57303370786517
61	17	21.5730337078652	-4.57303370786517
62	26	25.0869565217391	0.913043478260871
63	17	21.5730337078652	-4.57303370786517
64	17	25.0869565217391	-8.08695652173913
65	24	21.5730337078652	2.42696629213483
66	30	25.0869565217391	4.91304347826087
67	25	25.0869565217391	-0.086956521739129
68	15	21.5730337078652	-6.57303370786517
69	25	21.5730337078652	3.42696629213483
70	18	25.0869565217391	-7.08695652173913
71	20	25.0869565217391	-5.08695652173913
72	32	25.0869565217391	6.91304347826087
73	14	17.2	-3.2
74	20	21.5730337078652	-1.57303370786517
75	25	21.5730337078652	3.42696629213483
76	25	21.5730337078652	3.42696629213483
77	25	21.5730337078652	3.42696629213483
78	35	25.0869565217391	9.91304347826087
79	29	25.0869565217391	3.91304347826087
80	25	21.5730337078652	3.42696629213483
81	21	21.5730337078652	-0.573033707865168
82	21	21.5730337078652	-0.573033707865168
83	24	25.0869565217391	-1.08695652173913
84	26	21.5730337078652	4.42696629213483
85	24	21.5730337078652	2.42696629213483
86	20	21.5730337078652	-1.57303370786517
87	24	21.5730337078652	2.42696629213483
88	18	17.2	0.8
89	17	17.2	-0.199999999999999
90	22	25.0869565217391	-3.08695652173913
91	22	21.5730337078652	0.426966292134832
92	22	21.5730337078652	0.426966292134832
93	24	25.0869565217391	-1.08695652173913
94	32	25.0869565217391	6.91304347826087
95	19	21.5730337078652	-2.57303370786517
96	21	21.5730337078652	-0.573033707865168
97	23	21.5730337078652	1.42696629213483
98	26	25.0869565217391	0.913043478260871
99	18	25.0869565217391	-7.08695652173913
100	19	17.2	1.8
101	22	21.5730337078652	0.426966292134832
102	27	21.5730337078652	5.42696629213483
103	21	21.5730337078652	-0.573033707865168
104	20	21.5730337078652	-1.57303370786517
105	21	25.0869565217391	-4.08695652173913
106	20	21.5730337078652	-1.57303370786517
107	29	25.0869565217391	3.91304347826087
108	30	21.5730337078652	8.42696629213483
109	10	17.2	-7.2
110	23	21.5730337078652	1.42696629213483
111	29	21.5730337078652	7.42696629213483
112	19	21.5730337078652	-2.57303370786517
113	26	21.5730337078652	4.42696629213483
114	22	21.5730337078652	0.426966292134832
115	26	25.0869565217391	0.913043478260871
116	27	25.0869565217391	1.91304347826087
117	19	25.0869565217391	-6.08695652173913
118	24	21.5730337078652	2.42696629213483
119	26	21.5730337078652	4.42696629213483
120	22	21.5730337078652	0.426966292134832
121	23	25.0869565217391	-2.08695652173913
122	25	25.0869565217391	-0.086956521739129
123	19	21.5730337078652	-2.57303370786517
124	20	21.5730337078652	-1.57303370786517
125	25	25.0869565217391	-0.086956521739129
126	14	17.2	-3.2
127	19	21.5730337078652	-2.57303370786517
128	27	25.0869565217391	1.91304347826087
129	21	21.5730337078652	-0.573033707865168
130	21	21.5730337078652	-0.573033707865168
131	14	17.2	-3.2
132	21	25.0869565217391	-4.08695652173913
133	23	21.5730337078652	1.42696629213483
134	18	21.5730337078652	-3.57303370786517
135	20	21.5730337078652	-1.57303370786517
136	19	21.5730337078652	-2.57303370786517
137	15	21.5730337078652	-6.57303370786517
138	23	21.5730337078652	1.42696629213483
139	26	25.0869565217391	0.913043478260871
140	21	21.5730337078652	-0.573033707865168
141	13	17.2	-4.2
142	24	21.5730337078652	2.42696629213483
143	17	21.5730337078652	-4.57303370786517
144	21	21.5730337078652	-0.573033707865168
145	28	25.0869565217391	2.91304347826087
146	22	21.5730337078652	0.426966292134832
147	25	17.2	7.8
148	27	25.0869565217391	1.91304347826087
149	25	21.5730337078652	3.42696629213483
150	21	21.5730337078652	-0.573033707865168

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292159843ye797bfspmoru4w/2qmt51292159893.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292159843ye797bfspmoru4w/2qmt51292159893.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292159843ye797bfspmoru4w/31vs81292159893.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292159843ye797bfspmoru4w/31vs81292159893.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292159843ye797bfspmoru4w/4um9t1292159893.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292159843ye797bfspmoru4w/4um9t1292159893.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 5 ; 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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