Home » date » 2010 » Dec » 13 »

Are the treatments truly randomized?

*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: Mon, 13 Dec 2010 15:36:02 +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/13/t1292254856n1ooasou57d2cul.htm/, Retrieved Mon, 13 Dec 2010 16:40:57 +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/13/t1292254856n1ooasou57d2cul.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 «

12 4 10 6 12 7 13 3 1 15 8 12 8 13 12 12 0 1 12 3 12 6 15 8 12 3 0 9 5 11 9 9 6 6 2 0 12 6 7 5 9 10 9 3 1 6 7 11 7 10 7 6 2 1 11 2 12 9 11 10 6 1 0 11 5 6 5 12 10 10 1 1 11 6 10 6 8 8 6 3 0 12 7 11 7 12 12 8 3 1 12 6 10 7 11 12 12 3 1 13 8 13 9 14 12 15 3 1 11 4 10 7 10 12 10 1 1 12 7 11 6 7 10 9 1 1 13 8 14 9 16 14 15 2 1 12 7 12 6 8 10 12 1 1 11 8 12 8 8 7 10 3 0 11 5 11 8 11 11 6 1 0 6 5 9 8 8 7 6 3 0 6 4 10 4 8 8 6 3 0 9 4 9 7 8 6 6 2 0 9 4 9 8 9 8 9 2 0 9 4 12 8 7 6 12 2 1 11 4 11 7 10 11 11 1 1 11 8 11 8 11 6 6 1 0 9 3 12 8 7 6 10 3 0 9 3 8 5 8 6 6 1 0 12 5 9 8 15 9 9 2 1 11 7 10 7 13 7 10 3 1 10 4 11 8 11 10 6 3 0 9 4 11 7 10 8 9 2 0 12 4 10 7 16 6 5 1 1 12 7 9 8 14 11 12 1 1 12 6 12 8 13 9 13 3 0 14 6 12 10 10 10 15 0 0 12 4 8 7 8 10 9 2 0 10 7 10 5 7 6 9 2 0 6 4 12 8 16 6 12 2 1 12 8 8 8 13 10 12 2 1 6 7 10 3 13 6 6 2 0 12 4 15 10 6 9 9 1 0 14 8 8 8 10 11 11 1 1 12 5 13 7 16 12 9 1 0 12 4 12 8 12 13 12 2 0 10 2 8 6 5 7 9 3 1 10 8 9 8 13 8 7 2 1 9 3 11 7 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	4 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Goodness of Fit
Correlation	NA
R-squared	NA
RMSE	1

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	3	1.63846153846154	1.36153846153846
2	0	1.63846153846154	-1.63846153846154
3	3	1.63846153846154	1.36153846153846
4	2	1.63846153846154	0.361538461538462
5	3	1.63846153846154	1.36153846153846
6	2	1.63846153846154	0.361538461538462
7	1	1.63846153846154	-0.638461538461538
8	1	1.63846153846154	-0.638461538461538
9	3	1.63846153846154	1.36153846153846
10	3	1.63846153846154	1.36153846153846
11	3	1.63846153846154	1.36153846153846
12	3	1.63846153846154	1.36153846153846
13	1	1.63846153846154	-0.638461538461538
14	1	1.63846153846154	-0.638461538461538
15	2	1.63846153846154	0.361538461538462
16	1	1.63846153846154	-0.638461538461538
17	3	1.63846153846154	1.36153846153846
18	1	1.63846153846154	-0.638461538461538
19	3	1.63846153846154	1.36153846153846
20	3	1.63846153846154	1.36153846153846
21	2	1.63846153846154	0.361538461538462
22	2	1.63846153846154	0.361538461538462
23	2	1.63846153846154	0.361538461538462
24	1	1.63846153846154	-0.638461538461538
25	1	1.63846153846154	-0.638461538461538
26	3	1.63846153846154	1.36153846153846
27	1	1.63846153846154	-0.638461538461538
28	2	1.63846153846154	0.361538461538462
29	3	1.63846153846154	1.36153846153846
30	3	1.63846153846154	1.36153846153846
31	2	1.63846153846154	0.361538461538462
32	1	1.63846153846154	-0.638461538461538
33	1	1.63846153846154	-0.638461538461538
34	3	1.63846153846154	1.36153846153846
35	0	1.63846153846154	-1.63846153846154
36	2	1.63846153846154	0.361538461538462
37	2	1.63846153846154	0.361538461538462
38	2	1.63846153846154	0.361538461538462
39	2	1.63846153846154	0.361538461538462
40	2	1.63846153846154	0.361538461538462
41	1	1.63846153846154	-0.638461538461538
42	1	1.63846153846154	-0.638461538461538
43	1	1.63846153846154	-0.638461538461538
44	2	1.63846153846154	0.361538461538462
45	3	1.63846153846154	1.36153846153846
46	2	1.63846153846154	0.361538461538462
47	0	1.63846153846154	-1.63846153846154
48	1	1.63846153846154	-0.638461538461538
49	1	1.63846153846154	-0.638461538461538
50	1	1.63846153846154	-0.638461538461538
51	0	1.63846153846154	-1.63846153846154
52	1	1.63846153846154	-0.638461538461538
53	3	1.63846153846154	1.36153846153846
54	2	1.63846153846154	0.361538461538462
55	0	1.63846153846154	-1.63846153846154
56	1	1.63846153846154	-0.638461538461538
57	1	1.63846153846154	-0.638461538461538
58	2	1.63846153846154	0.361538461538462
59	1	1.63846153846154	-0.638461538461538
60	2	1.63846153846154	0.361538461538462
61	0	1.63846153846154	-1.63846153846154
62	1	1.63846153846154	-0.638461538461538
63	0	1.63846153846154	-1.63846153846154
64	2	1.63846153846154	0.361538461538462
65	2	1.63846153846154	0.361538461538462
66	2	1.63846153846154	0.361538461538462
67	3	1.63846153846154	1.36153846153846
68	1	1.63846153846154	-0.638461538461538
69	1	1.63846153846154	-0.638461538461538
70	3	1.63846153846154	1.36153846153846
71	3	1.63846153846154	1.36153846153846
72	3	1.63846153846154	1.36153846153846
73	0	1.63846153846154	-1.63846153846154
74	2	1.63846153846154	0.361538461538462
75	2	1.63846153846154	0.361538461538462
76	1	1.63846153846154	-0.638461538461538
77	2	1.63846153846154	0.361538461538462
78	0	1.63846153846154	-1.63846153846154
79	3	1.63846153846154	1.36153846153846
80	3	1.63846153846154	1.36153846153846
81	2	1.63846153846154	0.361538461538462
82	1	1.63846153846154	-0.638461538461538
83	1	1.63846153846154	-0.638461538461538
84	3	1.63846153846154	1.36153846153846
85	3	1.63846153846154	1.36153846153846
86	1	1.63846153846154	-0.638461538461538
87	1	1.63846153846154	-0.638461538461538
88	2	1.63846153846154	0.361538461538462
89	0	1.63846153846154	-1.63846153846154
90	0	1.63846153846154	-1.63846153846154
91	0	1.63846153846154	-1.63846153846154
92	0	1.63846153846154	-1.63846153846154
93	0	1.63846153846154	-1.63846153846154
94	1	1.63846153846154	-0.638461538461538
95	1	1.63846153846154	-0.638461538461538
96	1	1.63846153846154	-0.638461538461538
97	2	1.63846153846154	0.361538461538462
98	2	1.63846153846154	0.361538461538462
99	3	1.63846153846154	1.36153846153846
100	2	1.63846153846154	0.361538461538462
101	3	1.63846153846154	1.36153846153846
102	0	1.63846153846154	-1.63846153846154
103	0	1.63846153846154	-1.63846153846154
104	2	1.63846153846154	0.361538461538462
105	2	1.63846153846154	0.361538461538462
106	2	1.63846153846154	0.361538461538462
107	3	1.63846153846154	1.36153846153846
108	2	1.63846153846154	0.361538461538462
109	2	1.63846153846154	0.361538461538462
110	0	1.63846153846154	-1.63846153846154
111	1	1.63846153846154	-0.638461538461538
112	3	1.63846153846154	1.36153846153846
113	3	1.63846153846154	1.36153846153846
114	2	1.63846153846154	0.361538461538462
115	2	1.63846153846154	0.361538461538462
116	2	1.63846153846154	0.361538461538462
117	0	1.63846153846154	-1.63846153846154
118	1	1.63846153846154	-0.638461538461538
119	3	1.63846153846154	1.36153846153846
120	1	1.63846153846154	-0.638461538461538
121	3	1.63846153846154	1.36153846153846
122	1	1.63846153846154	-0.638461538461538
123	1	1.63846153846154	-0.638461538461538
124	2	1.63846153846154	0.361538461538462
125	1	1.63846153846154	-0.638461538461538
126	0	1.63846153846154	-1.63846153846154
127	2	1.63846153846154	0.361538461538462
128	1	1.63846153846154	-0.638461538461538
129	2	1.63846153846154	0.361538461538462
130	1	1.63846153846154	-0.638461538461538

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292254856n1ooasou57d2cul/2g78a1292254556.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292254856n1ooasou57d2cul/2g78a1292254556.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292254856n1ooasou57d2cul/3g78a1292254556.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292254856n1ooasou57d2cul/3g78a1292254556.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292254856n1ooasou57d2cul/4rgqv1292254556.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292254856n1ooasou57d2cul/4rgqv1292254556.ps (open in new window)

Parameters (Session):

par1 = 8 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 8 ; 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 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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