Home » date » 2010 » Dec » 12 »

Depression Tree no categorisation

*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 18:04:18 +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/t1292176999xm90gvpoqgyvggh.htm/, Retrieved Sun, 12 Dec 2010 19:03:19 +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/t1292176999xm90gvpoqgyvggh.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 «

15 10 77 46 15 12 13 6 11 6 4 15 16 0 9 20 63 37 12 7 11 4 26 5 4 23 24 1 12 16 73 45 15 13 14 6 26 20 10 26 22 1 15 10 76 46 12 11 12 5 15 12 6 19 21 1 17 8 90 55 14 16 12 5 10 11 5 19 23 1 14 14 67 40 8 10 6 4 21 12 8 16 23 1 9 19 69 43 11 15 10 5 27 11 9 23 21 0 11 23 54 33 4 4 10 2 21 13 8 19 22 1 13 9 54 33 13 7 12 5 21 9 11 24 20 1 16 12 76 47 19 15 15 6 22 14 6 19 12 0 16 14 75 44 10 5 13 6 29 12 8 25 23 0 15 13 76 47 15 16 18 8 29 18 11 23 23 0 10 11 80 49 6 15 11 6 29 9 5 31 30 1 16 11 89 55 7 13 12 3 30 15 10 29 22 0 12 10 73 43 14 13 13 6 19 12 7 18 21 1 15 12 74 46 16 15 14 6 19 12 7 17 21 1 13 18 78 51 16 15 16 7 22 12 13 22 15 0 18 12 76 47 14 10 16 8 18 15 10 21 22 0 13 10 69 42 15 17 16 6 28 11 8 24 24 1 17 15 74 42 14 14 15 7 17 13 6 22 23 0 14 15 82 48 12 9 13 4 18 10 8 16 15 0 13 12 77 45 9 6 8 4 20 17 7 22 24 1 13 9 84 51 12 11 14 2 16 13 5 21 24 0 15 11 75 46 14 13 15 6 17 17 9 25 21 0 15 16 79 47 14 10 16 6 25 15 11 22 21 0 13 17 79 47 10 4 13 6 22 13 11 24 18 0 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	0.5376
R-squared	0.289
RMSE	2.7063

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	10	11.4769230769231	-1.47692307692308
2	20	16.5238095238095	3.47619047619047
3	16	13.0392156862745	2.96078431372549
4	10	11.4769230769231	-1.47692307692308
5	8	11.4769230769231	-3.47692307692308
6	14	13.0392156862745	0.96078431372549
7	19	16.5238095238095	2.47619047619047
8	23	16.5238095238095	6.47619047619047
9	9	13.0392156862745	-4.03921568627451
10	12	11.4769230769231	0.523076923076923
11	14	11.4769230769231	2.52307692307692
12	13	11.4769230769231	1.52307692307692
13	11	16.5238095238095	-5.52380952380953
14	11	11.4769230769231	-0.476923076923077
15	10	13.0392156862745	-3.03921568627451
16	12	11.4769230769231	0.523076923076923
17	18	13.0392156862745	4.96078431372549
18	12	11.4769230769231	0.523076923076923
19	10	13.0392156862745	-3.03921568627451
20	15	11.4769230769231	3.52307692307692
21	15	13.0392156862745	1.96078431372549
22	12	13.0392156862745	-1.03921568627451
23	9	13.0392156862745	-4.03921568627451
24	11	11.4769230769231	-0.476923076923077
25	16	11.4769230769231	4.52307692307692
26	17	13.0392156862745	3.96078431372549
27	11	13.0392156862745	-2.03921568627451
28	13	11.4769230769231	1.52307692307692
29	9	13.0392156862745	-4.03921568627451
30	11	11.4769230769231	-0.476923076923077
31	20	16.5238095238095	3.47619047619047
32	8	11.4769230769231	-3.47692307692308
33	12	11.4769230769231	0.523076923076923
34	10	11.4769230769231	-1.47692307692308
35	11	13.0392156862745	-2.03921568627451
36	13	11.4769230769231	1.52307692307692
37	13	11.4769230769231	1.52307692307692
38	13	13.0392156862745	-0.0392156862745097
39	15	16.5238095238095	-1.52380952380953
40	12	13.0392156862745	-1.03921568627451
41	13	16.5238095238095	-3.52380952380953
42	14	16.5238095238095	-2.52380952380953
43	9	11.4769230769231	-2.47692307692308
44	9	11.4769230769231	-2.47692307692308
45	15	13.0392156862745	1.96078431372549
46	10	11.4769230769231	-1.47692307692308
47	13	11.4769230769231	1.52307692307692
48	8	11.4769230769231	-3.47692307692308
49	15	13.0392156862745	1.96078431372549
50	13	11.4769230769231	1.52307692307692
51	24	16.5238095238095	7.47619047619047
52	11	16.5238095238095	-5.52380952380953
53	13	11.4769230769231	1.52307692307692
54	12	11.4769230769231	0.523076923076923
55	22	16.5238095238095	5.47619047619047
56	11	11.4769230769231	-0.476923076923077
57	15	13.0392156862745	1.96078431372549
58	7	11.4769230769231	-4.47692307692308
59	14	13.0392156862745	0.96078431372549
60	10	13.0392156862745	-3.03921568627451
61	9	16.5238095238095	-7.52380952380953
62	12	13.0392156862745	-1.03921568627451
63	16	16.5238095238095	-0.523809523809526
64	13	11.4769230769231	1.52307692307692
65	11	11.4769230769231	-0.476923076923077
66	11	13.0392156862745	-2.03921568627451
67	13	11.4769230769231	1.52307692307692
68	10	11.4769230769231	-1.47692307692308
69	11	11.4769230769231	-0.476923076923077
70	9	11.4769230769231	-2.47692307692308
71	13	16.5238095238095	-3.52380952380953
72	14	13.0392156862745	0.96078431372549
73	14	13.0392156862745	0.96078431372549
74	11	11.4769230769231	-0.476923076923077
75	10	11.4769230769231	-1.47692307692308
76	11	11.4769230769231	-0.476923076923077
77	12	11.4769230769231	0.523076923076923
78	14	11.4769230769231	2.52307692307692
79	14	13.0392156862745	0.96078431372549
80	21	11.4769230769231	9.52307692307692
81	13	11.4769230769231	1.52307692307692
82	11	11.4769230769231	-0.476923076923077
83	12	13.0392156862745	-1.03921568627451
84	12	11.4769230769231	0.523076923076923
85	11	13.0392156862745	-2.03921568627451
86	14	13.0392156862745	0.96078431372549
87	13	13.0392156862745	-0.0392156862745097
88	13	13.0392156862745	-0.0392156862745097
89	12	13.0392156862745	-1.03921568627451
90	14	13.0392156862745	0.96078431372549
91	12	13.0392156862745	-1.03921568627451
92	12	11.4769230769231	0.523076923076923
93	18	16.5238095238095	1.47619047619047
94	11	11.4769230769231	-0.476923076923077
95	15	13.0392156862745	1.96078431372549
96	13	11.4769230769231	1.52307692307692
97	11	11.4769230769231	-0.476923076923077
98	22	13.0392156862745	8.96078431372549
99	10	11.4769230769231	-1.47692307692308
100	11	13.0392156862745	-2.03921568627451
101	15	13.0392156862745	1.96078431372549
102	14	13.0392156862745	0.96078431372549
103	11	13.0392156862745	-2.03921568627451
104	10	11.4769230769231	-1.47692307692308
105	14	13.0392156862745	0.96078431372549
106	14	11.4769230769231	2.52307692307692
107	15	16.5238095238095	-1.52380952380953
108	11	13.0392156862745	-2.03921568627451
109	10	11.4769230769231	-1.47692307692308
110	10	11.4769230769231	-1.47692307692308
111	12	13.0392156862745	-1.03921568627451
112	15	13.0392156862745	1.96078431372549
113	10	11.4769230769231	-1.47692307692308
114	12	13.0392156862745	-1.03921568627451
115	15	11.4769230769231	3.52307692307692
116	11	13.0392156862745	-2.03921568627451
117	10	11.4769230769231	-1.47692307692308
118	20	16.5238095238095	3.47619047619047
119	19	16.5238095238095	2.47619047619047
120	17	13.0392156862745	3.96078431372549
121	8	11.4769230769231	-3.47692307692308
122	17	16.5238095238095	0.476190476190474
123	11	11.4769230769231	-0.476923076923077
124	13	16.5238095238095	-3.52380952380953
125	9	11.4769230769231	-2.47692307692308
126	10	11.4769230769231	-1.47692307692308
127	13	11.4769230769231	1.52307692307692
128	16	13.0392156862745	2.96078431372549
129	12	11.4769230769231	0.523076923076923
130	14	11.4769230769231	2.52307692307692
131	11	13.0392156862745	-2.03921568627451
132	13	13.0392156862745	-0.0392156862745097
133	15	16.5238095238095	-1.52380952380953
134	14	11.4769230769231	2.52307692307692
135	14	13.0392156862745	0.96078431372549
136	10	13.0392156862745	-3.03921568627451
137	8	11.4769230769231	-3.47692307692308

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292176999xm90gvpoqgyvggh/2o8vu1292177049.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292176999xm90gvpoqgyvggh/2o8vu1292177049.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292176999xm90gvpoqgyvggh/3o8vu1292177049.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292176999xm90gvpoqgyvggh/3o8vu1292177049.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292176999xm90gvpoqgyvggh/4sqti1292177049.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/12/t1292176999xm90gvpoqgyvggh/4sqti1292177049.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = none ; par3 = 6 ; par4 = no ;

Parameters (R input):

par1 = 2 ; par2 = none ; par3 = 6 ; 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