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Workshop 10 (3)

*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 12:43:36 +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/t12921577364bchd4mekhen891.htm/, Retrieved Sun, 12 Dec 2010 13:42:16 +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/t12921577364bchd4mekhen891.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 «

13 15 42 14 13 12 18 51 8 13 15 11 42 12 16 12 16 46 7 12 10 12 41 10 11 12 17 49 7 12 15 15 47 16 18 9 19 33 11 11 12 16 33 14 14 11 18 47 6 9 11 10 42 16 14 11 14 32 11 12 15 18 53 16 11 7 18 41 12 12 11 14 41 7 13 11 14 33 13 11 10 12 37 11 12 14 16 43 15 16 10 15 45 7 9 6 13 33 9 11 11 16 49 7 13 15 14 42 14 15 11 9 43 15 10 12 9 37 7 11 14 17 43 15 13 15 13 42 17 16 9 15 43 15 15 13 17 46 14 14 13 16 33 14 14 16 12 42 8 14 13 11 40 8 8 12 16 44 14 13 14 17 42 14 15 11 17 52 8 13 9 16 44 11 11 16 13 45 16 15 12 12 46 10 15 10 12 36 8 9 13 16 45 14 13 16 14 49 16 16 14 12 43 13 13 15 12 43 5 11 5 14 37 8 12 8 8 32 10 12 11 15 45 8 12 16 14 45 13 14 17 11 45 15 14 9 13 45 6 8 9 14 31 12 13 13 15 33 16 16 10 16 44 5 13 6 10 49 15 11 12 11 44 12 14 8 12 41 8 13 14 14 44 13 13 12 15 38 14 13 11 16 33 12 12 16 9 47 16 16 8 11 37 10 15 15 15 48 15 15 7 15 40 8 12 16 13 50 16 14 14 17 54 19 12 16 17 43 14 15 9 15 54 6 12 14 13 44 13 13 11 15 47 15 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	4 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Goodness of Fit
Correlation	0.6467
R-squared	0.4182
RMSE	2.233

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	13	12.3783783783784	0.621621621621621
2	12	11.0666666666667	0.933333333333334
3	15	14.2549019607843	0.745098039215685
4	12	9.30555555555556	2.69444444444444
5	10	9.30555555555556	0.694444444444445
6	12	9.30555555555556	2.69444444444444
7	15	14.2549019607843	0.745098039215685
8	9	9.30555555555556	-0.305555555555555
9	12	14.2549019607843	-2.25490196078431
10	11	9.30555555555556	1.69444444444444
11	11	14.2549019607843	-3.25490196078431
12	11	9.30555555555556	1.69444444444444
13	15	12.3783783783784	2.62162162162162
14	7	12.3783783783784	-5.37837837837838
15	11	11.0666666666667	-0.0666666666666664
16	11	12.3783783783784	-1.37837837837838
17	10	9.30555555555556	0.694444444444445
18	14	14.2549019607843	-0.254901960784315
19	10	9.30555555555556	0.694444444444445
20	6	9.30555555555556	-3.30555555555556
21	11	11.0666666666667	-0.0666666666666664
22	15	14.2549019607843	0.745098039215685
23	11	12.3783783783784	-1.37837837837838
24	12	9.30555555555556	2.69444444444444
25	14	12.3783783783784	1.62162162162162
26	15	14.2549019607843	0.745098039215685
27	9	14.2549019607843	-5.25490196078431
28	13	14.2549019607843	-1.25490196078431
29	13	14.2549019607843	-1.25490196078431
30	16	11.0666666666667	4.93333333333333
31	13	9.30555555555556	3.69444444444444
32	12	12.3783783783784	-0.378378378378379
33	14	14.2549019607843	-0.254901960784315
34	11	11.0666666666667	-0.0666666666666664
35	9	9.30555555555556	-0.305555555555555
36	16	14.2549019607843	1.74509803921569
37	12	11.0666666666667	0.933333333333334
38	10	9.30555555555556	0.694444444444445
39	13	12.3783783783784	0.621621621621621
40	16	14.2549019607843	1.74509803921569
41	14	12.3783783783784	1.62162162162162
42	15	9.30555555555556	5.69444444444444
43	5	9.30555555555556	-4.30555555555556
44	8	9.30555555555556	-1.30555555555556
45	11	9.30555555555556	1.69444444444444
46	16	14.2549019607843	1.74509803921569
47	17	14.2549019607843	2.74509803921569
48	9	9.30555555555556	-0.305555555555555
49	9	12.3783783783784	-3.37837837837838
50	13	14.2549019607843	-1.25490196078431
51	10	11.0666666666667	-1.06666666666667
52	6	12.3783783783784	-6.37837837837838
53	12	14.2549019607843	-2.25490196078431
54	8	11.0666666666667	-3.06666666666667
55	14	12.3783783783784	1.62162162162162
56	12	12.3783783783784	-0.378378378378379
57	11	12.3783783783784	-1.37837837837838
58	16	14.2549019607843	1.74509803921569
59	8	11.0666666666667	-3.06666666666667
60	15	14.2549019607843	0.745098039215685
61	7	9.30555555555556	-2.30555555555556
62	16	14.2549019607843	1.74509803921569
63	14	12.3783783783784	1.62162162162162
64	16	14.2549019607843	1.74509803921569
65	9	9.30555555555556	-0.305555555555555
66	14	12.3783783783784	1.62162162162162
67	11	12.3783783783784	-1.37837837837838
68	13	9.30555555555556	3.69444444444444
69	15	12.3783783783784	2.62162162162162
70	5	9.30555555555556	-4.30555555555556
71	15	12.3783783783784	2.62162162162162
72	13	12.3783783783784	0.621621621621621
73	11	11.0666666666667	-0.0666666666666664
74	11	14.2549019607843	-3.25490196078431
75	12	12.3783783783784	-0.378378378378379
76	12	12.3783783783784	-0.378378378378379
77	12	12.3783783783784	-0.378378378378379
78	12	12.3783783783784	-0.378378378378379
79	14	11.0666666666667	2.93333333333333
80	6	9.30555555555556	-3.30555555555556
81	7	9.30555555555556	-2.30555555555556
82	14	12.3783783783784	1.62162162162162
83	14	14.2549019607843	-0.254901960784315
84	10	9.30555555555556	0.694444444444445
85	13	9.30555555555556	3.69444444444444
86	12	12.3783783783784	-0.378378378378379
87	9	9.30555555555556	-0.305555555555555
88	12	11.0666666666667	0.933333333333334
89	16	14.2549019607843	1.74509803921569
90	10	9.30555555555556	0.694444444444445
91	14	11.0666666666667	2.93333333333333
92	10	14.2549019607843	-4.25490196078431
93	16	14.2549019607843	1.74509803921569
94	15	12.3783783783784	2.62162162162162
95	12	9.30555555555556	2.69444444444444
96	10	9.30555555555556	0.694444444444445
97	8	11.0666666666667	-3.06666666666667
98	8	9.30555555555556	-1.30555555555556
99	11	14.2549019607843	-3.25490196078431
100	13	11.0666666666667	1.93333333333333
101	16	14.2549019607843	1.74509803921569
102	14	14.2549019607843	-0.254901960784315
103	11	11.0666666666667	-0.0666666666666664
104	4	9.30555555555556	-5.30555555555556
105	14	11.0666666666667	2.93333333333333
106	9	11.0666666666667	-2.06666666666667
107	14	14.2549019607843	-0.254901960784315
108	8	11.0666666666667	-3.06666666666667
109	8	11.0666666666667	-3.06666666666667
110	11	14.2549019607843	-3.25490196078431
111	12	12.3783783783784	-0.378378378378379
112	11	11.0666666666667	-0.0666666666666664
113	14	14.2549019607843	-0.254901960784315
114	15	14.2549019607843	0.745098039215685
115	16	14.2549019607843	1.74509803921569
116	16	14.2549019607843	1.74509803921569
117	14	14.2549019607843	-0.254901960784315
118	14	12.3783783783784	1.62162162162162
119	12	12.3783783783784	-0.378378378378379
120	14	12.3783783783784	1.62162162162162
121	8	9.30555555555556	-1.30555555555556
122	13	14.2549019607843	-1.25490196078431
123	16	14.2549019607843	1.74509803921569
124	12	11.0666666666667	0.933333333333334
125	16	14.2549019607843	1.74509803921569
126	12	12.3783783783784	-0.378378378378379
127	11	11.0666666666667	-0.0666666666666664
128	4	9.30555555555556	-5.30555555555556
129	16	14.2549019607843	1.74509803921569
130	15	12.3783783783784	2.62162162162162
131	10	11.0666666666667	-1.06666666666667
132	13	11.0666666666667	1.93333333333333
133	15	14.2549019607843	0.745098039215685
134	12	11.0666666666667	0.933333333333334
135	14	14.2549019607843	-0.254901960784315
136	7	12.3783783783784	-5.37837837837838
137	19	14.2549019607843	4.74509803921569
138	12	14.2549019607843	-2.25490196078431
139	12	12.3783783783784	-0.378378378378379
140	13	14.2549019607843	-1.25490196078431
141	15	14.2549019607843	0.745098039215685
142	8	9.30555555555556	-1.30555555555556
143	12	11.0666666666667	0.933333333333334
144	10	11.0666666666667	-1.06666666666667
145	8	11.0666666666667	-3.06666666666667
146	10	14.2549019607843	-4.25490196078431
147	15	14.2549019607843	0.745098039215685
148	16	14.2549019607843	1.74509803921569
149	13	12.3783783783784	0.621621621621621
150	16	14.2549019607843	1.74509803921569
151	9	9.30555555555556	-0.305555555555555
152	14	14.2549019607843	-0.254901960784315
153	14	12.3783783783784	1.62162162162162
154	12	11.0666666666667	0.933333333333334

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921577364bchd4mekhen891/2cex11292157810.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921577364bchd4mekhen891/2cex11292157810.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921577364bchd4mekhen891/3cex11292157810.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921577364bchd4mekhen891/3cex11292157810.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921577364bchd4mekhen891/4m5e41292157810.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t12921577364bchd4mekhen891/4m5e41292157810.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 1 ; 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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