Home » date » 2010 » Dec » 21 »

paper - RP - geen motivatie

*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: Tue, 21 Dec 2010 08:28:20 +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/21/t1292919981kqewm56zt6788qi.htm/, Retrieved Tue, 21 Dec 2010 09:26:22 +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/21/t1292919981kqewm56zt6788qi.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 «

46 11 52 26 23 44 8 39 25 15 42 10 42 28 25 41 12 35 30 18 48 12 32 28 21 49 10 49 40 19 51 8 33 28 15 47 10 47 27 22 49 11 46 25 19 46 7 40 27 20 51 10 33 32 26 54 9 39 28 26 52 9 37 21 21 52 11 56 40 18 45 12 36 29 19 52 5 24 27 19 56 10 56 31 18 54 11 32 33 19 50 12 41 28 24 35 9 24 26 28 48 3 42 25 20 37 10 47 37 27 47 7 25 13 18 31 9 33 32 19 45 9 43 32 24 47 10 45 38 21 44 9 44 30 22 30 19 46 33 25 40 14 31 22 19 44 5 31 29 15 43 13 42 33 34 51 7 28 31 23 48 8 38 23 19 55 11 59 42 26 48 11 43 35 15 53 12 29 31 15 49 9 38 31 17 44 13 39 38 30 45 12 50 34 19 40 11 44 33 28 44 18 29 23 23 41 8 29 18 23 46 14 36 33 21 47 10 43 26 18 48 13 28 29 19 43 13 39 23 24 46 8 35 18 15 53 10 43 36 20 33 8 28 21 24 47 9 49 31 9 43 10 33 31 20 45 9 39 29 20 49 9 36 24 10 45 9 24 35 44 37 10 47 37 20 42 8 34 29 20 43 11 33 31 11 44 11 43 34 21 39 10 41 38 21 37 23 40 27 19 53 9 39 33 17 48 12 54 36 16 47 9 43 27 14 49 9 45 33 19 47 8 29 24 21 56 9 45 31 16 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	0.2788
R-squared	0.0778
RMSE	2.6254

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	11	9.92	1.08
2	8	9.92	-1.92
3	10	9.92	0.08
4	12	9.92	2.08
5	12	9.92	2.08
6	10	10.2941176470588	-0.294117647058824
7	8	9.92	-1.92
8	10	9.92	0.08
9	11	9.92	1.08
10	7	9.92	-2.92
11	10	9.92	0.08
12	9	9.92	-0.92
13	9	9.92	-0.92
14	11	10.2941176470588	0.705882352941176
15	12	9.92	2.08
16	5	9.92	-4.92
17	10	9.92	0.08
18	11	10.2941176470588	0.705882352941176
19	12	9.92	2.08
20	9	9.92	-0.92
21	3	9.92	-6.92
22	10	11.8620689655172	-1.86206896551724
23	7	9.92	-2.92
24	9	9.92	-0.92
25	9	9.92	-0.92
26	10	11.8620689655172	-1.86206896551724
27	9	9.92	-0.92
28	19	11.8620689655172	7.13793103448276
29	14	9.92	4.08
30	5	9.92	-4.92
31	13	11.8620689655172	1.13793103448276
32	7	9.92	-2.92
33	8	9.92	-1.92
34	11	10.2941176470588	0.705882352941176
35	11	11.8620689655172	-0.862068965517242
36	12	9.92	2.08
37	9	9.92	-0.92
38	13	11.8620689655172	1.13793103448276
39	12	11.8620689655172	0.137931034482758
40	11	11.8620689655172	-0.862068965517242
41	18	9.92	8.08
42	8	9.92	-1.92
43	14	11.8620689655172	2.13793103448276
44	10	9.92	0.08
45	13	9.92	3.08
46	13	9.92	3.08
47	8	9.92	-1.92
48	10	10.2941176470588	-0.294117647058824
49	8	9.92	-1.92
50	9	9.92	-0.92
51	10	9.92	0.08
52	9	9.92	-0.92
53	9	9.92	-0.92
54	9	11.8620689655172	-2.86206896551724
55	10	11.8620689655172	-1.86206896551724
56	8	9.92	-1.92
57	11	9.92	1.08
58	11	11.8620689655172	-0.862068965517242
59	10	11.8620689655172	-1.86206896551724
60	23	9.92	13.08
61	9	10.2941176470588	-1.29411764705882
62	12	11.8620689655172	0.137931034482758
63	9	9.92	-0.92
64	9	10.2941176470588	-1.29411764705882
65	8	9.92	-1.92
66	9	9.92	-0.92
67	9	9.92	-0.92
68	9	9.92	-0.92
69	11	10.2941176470588	0.705882352941176
70	12	9.92	2.08
71	8	10.2941176470588	-2.29411764705882
72	9	9.92	-0.92
73	10	11.8620689655172	-1.86206896551724
74	8	9.92	-1.92
75	9	9.92	-0.92
76	9	9.92	-0.92
77	13	11.8620689655172	1.13793103448276
78	11	11.8620689655172	-0.862068965517242
79	18	9.92	8.08
80	10	9.92	0.08
81	14	9.92	4.08
82	7	9.92	-2.92
83	10	9.92	0.08
84	9	9.92	-0.92
85	9	9.92	-0.92
86	12	10.2941176470588	1.70588235294118
87	8	9.92	-1.92
88	9	9.92	-0.92
89	8	9.92	-1.92
90	13	10.2941176470588	2.70588235294118
91	6	9.92	-3.92
92	11	9.92	1.08
93	10	10.2941176470588	-0.294117647058824
94	10	11.8620689655172	-1.86206896551724
95	14	9.92	4.08
96	13	9.92	3.08
97	10	9.92	0.08
98	8	9.92	-1.92
99	10	11.8620689655172	-1.86206896551724
100	8	10.2941176470588	-2.29411764705882
101	10	11.8620689655172	-1.86206896551724
102	7	9.92	-2.92
103	11	9.92	1.08
104	10	10.2941176470588	-0.294117647058824
105	8	9.92	-1.92
106	12	11.8620689655172	0.137931034482758
107	12	9.92	2.08
108	11	9.92	1.08
109	11	9.92	1.08
110	6	9.92	-3.92
111	14	11.8620689655172	2.13793103448276
112	9	9.92	-0.92
113	11	10.2941176470588	0.705882352941176
114	10	9.92	0.08
115	10	11.8620689655172	-1.86206896551724
116	8	9.92	-1.92
117	9	9.92	-0.92
118	10	11.8620689655172	-1.86206896551724
119	10	9.92	0.08
120	12	9.92	2.08
121	10	11.8620689655172	-1.86206896551724
122	11	9.92	1.08
123	16	11.8620689655172	4.13793103448276
124	12	9.92	2.08
125	10	10.2941176470588	-0.294117647058824
126	13	11.8620689655172	1.13793103448276
127	8	9.92	-1.92
128	12	9.92	2.08
129	10	9.92	0.08
130	8	9.92	-1.92
131	14	9.92	4.08
132	9	9.92	-0.92
133	12	9.92	2.08
134	10	9.92	0.08
135	9	9.92	-0.92
136	10	11.8620689655172	-1.86206896551724
137	11	10.2941176470588	0.705882352941176
138	11	9.92	1.08
139	10	9.92	0.08
140	10	9.92	0.08
141	20	11.8620689655172	8.13793103448276
142	10	9.92	0.08
143	8	9.92	-1.92
144	8	9.92	-1.92
145	9	9.92	-0.92
146	18	9.92	8.08

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292919981kqewm56zt6788qi/2q3gp1292920093.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292919981kqewm56zt6788qi/2q3gp1292920093.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292919981kqewm56zt6788qi/3q3gp1292920093.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292919981kqewm56zt6788qi/3q3gp1292920093.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292919981kqewm56zt6788qi/41dys1292920093.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292919981kqewm56zt6788qi/41dys1292920093.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 2 ; par2 = none ; 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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