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RP - Depressie - No Cat

*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, 19 Dec 2010 16:08:15 +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/19/t1292774819cakg31s15qkg6cp.htm/, Retrieved Sun, 19 Dec 2010 17:07:03 +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/19/t1292774819cakg31s15qkg6cp.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:

elly.decuyper@student.lessius.eu

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

0 1 4 4 2 0 1 2 2 2 0 1 5 5 4 1 1 4 5 3 0 2 1 1 2 0 1 2 4 1 0 4 5 6 4 0 1 1 5 3 0 1 3 4 1 0 2 5 5 4 1 1 2 7 4 0 1 2 2 4 1 2 2 7 3 0 1 2 5 4 0 1 1 5 1 1 1 4 7 4 1 1 3 3 1 0 1 6 6 4 1 1 1 2 4 0 2 3 6 3 1 1 2 1 2 0 2 5 5 6 0 1 5 4 5 0 2 3 4 4 1 1 3 7 6 0 1 5 7 1 1 1 5 5 2 0 2 4 6 4 1 1 2 5 4 0 1 1 1 1 1 2 4 6 2 0 1 6 4 1 0 1 2 2 2 1 1 3 2 2 1 1 2 6 2 1 2 4 6 6 1 1 2 6 2 0 1 1 1 1 1 1 5 6 4 1 1 5 6 3 0 1 1 1 3 1 1 1 1 1 1 1 2 7 4 0 1 4 2 3 0 1 5 3 4 0 1 3 5 3 0 1 3 3 2 1 1 1 4 1 0 1 2 2 5 1 1 3 3 4 1 2 2 7 1 0 2 5 7 2 1 1 4 5 4 0 1 4 1 3 0 1 2 2 2 0 2 3 5 3 1 1 6 2 3 0 1 2 4 2 1 2 3 7 2 1 1 2 2 4 0 1 5 5 4 0 1 5 6 2 0 1 5 3 2 1 1 6 7 5 0 2 4 4 4 1 1 2 3 5 0 1 5 5 5 1 2 2 3 2 1 1 1 2 3 0 1 6 6 4 0 1 6 6 2 1 1 3 5 2 1 1 4 2 2 0 3 5 3 5 0 2 2 4 2 0 2 4 6 3 1 1 3 5 2 1 1 2 2 2 1 1 2 5 2 1 1 3 2 2 0 1 3 1 2 1 1 7 2 1 0 1 2 4 3 0 1 2 5 3 1 1 2 5 3 0 1 5 3 3 0 1 1 2 1 0 3 5 7 4 0 1 2 1 1 0 1 1 5 1 0 1 2 5 1 0 1 2 2 3 0 1 0 6 2 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	'Gwilym Jenkins' @ 72.249.127.135

Goodness of Fit
Correlation	0.3412
R-squared	0.1164
RMSE	0.5412

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	1	1.11235955056180	-0.112359550561798
2	1	1.11235955056180	-0.112359550561798
3	1	1.46428571428571	-0.464285714285714
4	1	1.11235955056180	-0.112359550561798
5	2	1.11235955056180	0.887640449438202
6	1	1.11235955056180	-0.112359550561798
7	4	1.54285714285714	2.45714285714286
8	1	1.11235955056180	-0.112359550561798
9	1	1.11235955056180	-0.112359550561798
10	2	1.46428571428571	0.535714285714286
11	1	1.54285714285714	-0.542857142857143
12	1	1.11235955056180	-0.112359550561798
13	2	1.54285714285714	0.457142857142857
14	1	1.11235955056180	-0.112359550561798
15	1	1.11235955056180	-0.112359550561798
16	1	1.54285714285714	-0.542857142857143
17	1	1.11235955056180	-0.112359550561798
18	1	1.54285714285714	-0.542857142857143
19	1	1.11235955056180	-0.112359550561798
20	2	1.54285714285714	0.457142857142857
21	1	1.11235955056180	-0.112359550561798
22	2	1.46428571428571	0.535714285714286
23	1	1.46428571428571	-0.464285714285714
24	2	1.11235955056180	0.887640449438202
25	1	1.54285714285714	-0.542857142857143
26	1	1.54285714285714	-0.542857142857143
27	1	1.46428571428571	-0.464285714285714
28	2	1.54285714285714	0.457142857142857
29	1	1.11235955056180	-0.112359550561798
30	1	1.11235955056180	-0.112359550561798
31	2	1.54285714285714	0.457142857142857
32	1	1.46428571428571	-0.464285714285714
33	1	1.11235955056180	-0.112359550561798
34	1	1.11235955056180	-0.112359550561798
35	1	1.54285714285714	-0.542857142857143
36	2	1.54285714285714	0.457142857142857
37	1	1.54285714285714	-0.542857142857143
38	1	1.11235955056180	-0.112359550561798
39	1	1.54285714285714	-0.542857142857143
40	1	1.54285714285714	-0.542857142857143
41	1	1.11235955056180	-0.112359550561798
42	1	1.11235955056180	-0.112359550561798
43	1	1.54285714285714	-0.542857142857143
44	1	1.11235955056180	-0.112359550561798
45	1	1.46428571428571	-0.464285714285714
46	1	1.11235955056180	-0.112359550561798
47	1	1.11235955056180	-0.112359550561798
48	1	1.11235955056180	-0.112359550561798
49	1	1.11235955056180	-0.112359550561798
50	1	1.11235955056180	-0.112359550561798
51	2	1.54285714285714	0.457142857142857
52	2	1.54285714285714	0.457142857142857
53	1	1.11235955056180	-0.112359550561798
54	1	1.11235955056180	-0.112359550561798
55	1	1.11235955056180	-0.112359550561798
56	2	1.11235955056180	0.887640449438202
57	1	1.46428571428571	-0.464285714285714
58	1	1.11235955056180	-0.112359550561798
59	2	1.54285714285714	0.457142857142857
60	1	1.11235955056180	-0.112359550561798
61	1	1.46428571428571	-0.464285714285714
62	1	1.54285714285714	-0.542857142857143
63	1	1.46428571428571	-0.464285714285714
64	1	1.54285714285714	-0.542857142857143
65	2	1.11235955056180	0.887640449438202
66	1	1.11235955056180	-0.112359550561798
67	1	1.46428571428571	-0.464285714285714
68	2	1.11235955056180	0.887640449438202
69	1	1.11235955056180	-0.112359550561798
70	1	1.54285714285714	-0.542857142857143
71	1	1.54285714285714	-0.542857142857143
72	1	1.11235955056180	-0.112359550561798
73	1	1.11235955056180	-0.112359550561798
74	3	1.46428571428571	1.53571428571429
75	2	1.11235955056180	0.887640449438202
76	2	1.54285714285714	0.457142857142857
77	1	1.11235955056180	-0.112359550561798
78	1	1.11235955056180	-0.112359550561798
79	1	1.11235955056180	-0.112359550561798
80	1	1.11235955056180	-0.112359550561798
81	1	1.11235955056180	-0.112359550561798
82	1	1.46428571428571	-0.464285714285714
83	1	1.11235955056180	-0.112359550561798
84	1	1.11235955056180	-0.112359550561798
85	1	1.11235955056180	-0.112359550561798
86	1	1.46428571428571	-0.464285714285714
87	1	1.11235955056180	-0.112359550561798
88	3	1.54285714285714	1.45714285714286
89	1	1.11235955056180	-0.112359550561798
90	1	1.11235955056180	-0.112359550561798
91	1	1.11235955056180	-0.112359550561798
92	1	1.11235955056180	-0.112359550561798
93	1	1.54285714285714	-0.542857142857143
94	1	1.46428571428571	-0.464285714285714
95	1	1.11235955056180	-0.112359550561798
96	1	1.11235955056180	-0.112359550561798
97	1	1.11235955056180	-0.112359550561798
98	1	1.11235955056180	-0.112359550561798
99	1	1.11235955056180	-0.112359550561798
100	2	1.11235955056180	0.887640449438202
101	1	1.54285714285714	-0.542857142857143
102	1	1.46428571428571	-0.464285714285714
103	1	1.11235955056180	-0.112359550561798
104	2	1.54285714285714	0.457142857142857
105	1	1.11235955056180	-0.112359550561798
106	2	1.46428571428571	0.535714285714286
107	2	1.11235955056180	0.887640449438202
108	3	1.46428571428571	1.53571428571429
109	2	1.46428571428571	0.535714285714286
110	2	1.54285714285714	0.457142857142857
111	1	1.54285714285714	-0.542857142857143
112	1	1.11235955056180	-0.112359550561798
113	1	1.11235955056180	-0.112359550561798
114	1	1.11235955056180	-0.112359550561798
115	1	1.46428571428571	-0.464285714285714
116	1	1.11235955056180	-0.112359550561798
117	1	1.11235955056180	-0.112359550561798
118	2	1.46428571428571	0.535714285714286
119	1	1.11235955056180	-0.112359550561798
120	1	1.54285714285714	-0.542857142857143
121	1	1.11235955056180	-0.112359550561798
122	1	1.11235955056180	-0.112359550561798
123	1	1.46428571428571	-0.464285714285714
124	1	1.11235955056180	-0.112359550561798
125	1	1.11235955056180	-0.112359550561798
126	1	1.11235955056180	-0.112359550561798
127	1	1.11235955056180	-0.112359550561798
128	2	1.46428571428571	0.535714285714286
129	2	1.46428571428571	0.535714285714286
130	1	1.11235955056180	-0.112359550561798
131	1	1.11235955056180	-0.112359550561798
132	1	1.11235955056180	-0.112359550561798
133	1	1.11235955056180	-0.112359550561798
134	4	1.54285714285714	2.45714285714286
135	1	1.46428571428571	-0.464285714285714
136	2	1.11235955056180	0.887640449438202
137	1	1.54285714285714	-0.542857142857143
138	1	1.46428571428571	-0.464285714285714
139	1	1.11235955056180	-0.112359550561798
140	1	1.54285714285714	-0.542857142857143
141	1	1.11235955056180	-0.112359550561798
142	1	1.11235955056180	-0.112359550561798
143	1	1.11235955056180	-0.112359550561798
144	1	1.11235955056180	-0.112359550561798
145	1	1.54285714285714	-0.542857142857143
146	1	1.11235955056180	-0.112359550561798
147	1	1.11235955056180	-0.112359550561798
148	2	1.11235955056180	0.887640449438202
149	1	1.46428571428571	-0.464285714285714
150	1	1.11235955056180	-0.112359550561798
151	3	1.46428571428571	1.53571428571429
152	1	1.11235955056180	-0.112359550561798

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292774819cakg31s15qkg6cp/2yrcu1292774888.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292774819cakg31s15qkg6cp/2yrcu1292774888.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292774819cakg31s15qkg6cp/391uf1292774888.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292774819cakg31s15qkg6cp/391uf1292774888.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292774819cakg31s15qkg6cp/41abz1292774888.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292774819cakg31s15qkg6cp/41abz1292774888.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = quantiles ; par3 = 2 ; par4 = no ;

Parameters (R input):

par1 = 2 ; 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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