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verbetering WS 10 - recursive partitioning

*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 08:22:35 +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/t1292919692pn69o5l8jfbburl.htm/, Retrieved Tue, 21 Dec 2010 09:21:36 +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/t1292919692pn69o5l8jfbburl.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 «

25 11 7 8 23 25 17 6 17 8 25 30 18 8 12 9 19 22 16 10 12 7 29 22 20 10 11 4 25 25 16 11 11 11 21 23 18 16 12 7 22 17 17 11 13 7 25 21 30 12 16 10 18 19 23 8 11 10 22 15 18 12 10 8 15 16 21 9 9 9 20 22 31 14 17 11 20 23 27 15 11 9 21 23 21 9 14 13 21 19 16 8 15 9 24 23 20 9 15 6 24 25 17 9 13 6 23 22 25 16 18 16 24 26 26 11 18 5 18 29 25 8 12 7 25 32 17 9 17 9 21 25 32 12 18 12 22 28 22 9 14 9 23 25 17 9 16 5 23 25 20 14 14 10 24 18 29 10 12 8 23 25 23 14 17 7 21 25 20 10 12 8 28 20 11 6 6 4 16 15 26 13 12 8 29 24 22 10 12 8 27 26 14 15 13 8 16 14 19 12 14 7 28 24 20 11 11 8 25 25 28 8 12 7 22 20 19 9 9 7 23 21 30 9 15 9 26 27 29 15 18 11 23 23 26 9 15 6 25 25 23 10 12 8 21 20 21 12 14 9 24 22 28 11 13 6 22 25 23 14 13 10 27 25 18 6 11 8 26 17 20 8 16 10 24 25 21 10 11 5 24 26 28 12 16 14 22 27 10 5 8 6 24 19 22 10 15 6 20 22 31 10 21 12 26 32 29 13 18 12 21 21 22 10 13 8 19 18 23 10 15 10 21 23 20 9 19 10 16 20 18 8 15 10 22 21 25 14 11 5 15 17 21 8 10 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	10 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135
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	0.6552
R-squared	0.4292
RMSE	4.31

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	25	20.4230769230769	4.57692307692308
2	17	21.4615384615385	-4.46153846153846
3	18	20.4230769230769	-2.42307692307692
4	16	17.2105263157895	-1.21052631578947
5	20	21.5	-1.5
6	16	20.4230769230769	-4.42307692307692
7	18	17.2105263157895	0.789473684210527
8	17	17.2105263157895	-0.210526315789473
9	30	20.4230769230769	9.57692307692308
10	23	20.4230769230769	2.57692307692308
11	18	20.4230769230769	-2.42307692307692
12	21	20.4230769230769	0.576923076923077
13	31	25.0714285714286	5.92857142857143
14	27	25.0714285714286	1.92857142857143
15	21	20.4230769230769	0.576923076923077
16	16	20.4230769230769	-4.42307692307692
17	20	21.5	-1.5
18	17	17.2105263157895	-0.210526315789473
19	25	29.8125	-4.8125
20	26	29.8125	-3.8125
21	25	21.4615384615385	3.53846153846154
22	17	20.4230769230769	-3.42307692307692
23	32	29.8125	2.1875
24	22	20.4230769230769	1.57692307692308
25	17	21.5	-4.5
26	20	25.0714285714286	-5.07142857142857
27	29	20.4230769230769	8.57692307692308
28	23	21.5	1.5
29	20	20.4230769230769	-0.423076923076923
30	11	17.2105263157895	-6.21052631578947
31	26	25.0714285714286	0.928571428571427
32	22	21.4615384615385	0.53846153846154
33	14	25.0714285714286	-11.0714285714286
34	19	21.5	-2.5
35	20	20.4230769230769	-0.423076923076923
36	28	17.2105263157895	10.7894736842105
37	19	17.2105263157895	1.78947368421053
38	30	21.4615384615385	8.53846153846154
39	29	25.0714285714286	3.92857142857143
40	26	21.5	4.5
41	23	20.4230769230769	2.57692307692308
42	21	20.4230769230769	0.576923076923077
43	28	21.5	6.5
44	23	25.0714285714286	-2.07142857142857
45	18	20.4230769230769	-2.42307692307692
46	20	20.4230769230769	-0.423076923076923
47	21	21.4615384615385	-0.46153846153846
48	28	29.8125	-1.8125
49	10	17.2105263157895	-7.21052631578947
50	22	17.2105263157895	4.78947368421053
51	31	21.4615384615385	9.53846153846154
52	29	25.0714285714286	3.92857142857143
53	22	20.4230769230769	1.57692307692308
54	23	20.4230769230769	2.57692307692308
55	20	20.4230769230769	-0.423076923076923
56	18	20.4230769230769	-2.42307692307692
57	25	17.2105263157895	7.78947368421053
58	21	17.2105263157895	3.78947368421053
59	24	20.4230769230769	3.57692307692308
60	25	25.0714285714286	-0.071428571428573
61	13	17.2105263157895	-4.21052631578947
62	28	20.4230769230769	7.57692307692308
63	25	21.4615384615385	3.53846153846154
64	9	21.4615384615385	-12.4615384615385
65	16	17.2105263157895	-1.21052631578947
66	19	20.4230769230769	-1.42307692307692
67	29	29.8125	-0.8125
68	14	17.2105263157895	-3.21052631578947
69	22	25.0714285714286	-3.07142857142857
70	15	17.2105263157895	-2.21052631578947
71	15	17.2105263157895	-2.21052631578947
72	20	20.4230769230769	-0.423076923076923
73	18	20.4230769230769	-2.42307692307692
74	33	29.8125	3.1875
75	22	20.4230769230769	1.57692307692308
76	16	20.4230769230769	-4.42307692307692
77	16	17.2105263157895	-1.21052631578947
78	18	17.2105263157895	0.789473684210527
79	18	20.4230769230769	-2.42307692307692
80	22	25.0714285714286	-3.07142857142857
81	30	25.0714285714286	4.92857142857143
82	30	29.8125	0.1875
83	24	25.0714285714286	-1.07142857142857
84	21	25.0714285714286	-4.07142857142857
85	29	29.8125	-0.8125
86	31	20.4230769230769	10.5769230769231
87	20	17.2105263157895	2.78947368421053
88	16	20.4230769230769	-4.42307692307692
89	22	20.4230769230769	1.57692307692308
90	20	21.4615384615385	-1.46153846153846
91	28	25.0714285714286	2.92857142857143
92	38	29.8125	8.1875
93	22	17.2105263157895	4.78947368421053
94	20	20.4230769230769	-0.423076923076923
95	17	17.2105263157895	-0.210526315789473
96	22	25.0714285714286	-3.07142857142857
97	31	25.0714285714286	5.92857142857143
98	24	25.0714285714286	-1.07142857142857
99	18	20.4230769230769	-2.42307692307692
100	23	25.0714285714286	-2.07142857142857
101	15	20.4230769230769	-5.42307692307692
102	12	21.4615384615385	-9.46153846153846
103	15	20.4230769230769	-5.42307692307692
104	20	17.2105263157895	2.78947368421053
105	34	25.0714285714286	8.92857142857143
106	31	25.0714285714286	5.92857142857143
107	19	20.4230769230769	-1.42307692307692
108	21	20.4230769230769	0.576923076923077
109	22	20.4230769230769	1.57692307692308
110	24	21.5	2.5
111	32	29.8125	2.1875
112	33	29.8125	3.1875
113	13	17.2105263157895	-4.21052631578947
114	25	29.8125	-4.8125
115	29	29.8125	-0.8125
116	18	20.4230769230769	-2.42307692307692
117	20	21.4615384615385	-1.46153846153846
118	15	20.4230769230769	-5.42307692307692
119	33	29.8125	3.1875
120	26	21.5	4.5
121	18	20.4230769230769	-2.42307692307692
122	28	21.4615384615385	6.53846153846154
123	17	21.5	-4.5
124	12	17.2105263157895	-5.21052631578947
125	17	20.4230769230769	-3.42307692307692
126	21	21.5	-0.5
127	18	25.0714285714286	-7.07142857142857
128	10	17.2105263157895	-7.21052631578947
129	29	29.8125	-0.8125
130	31	20.4230769230769	10.5769230769231
131	19	21.4615384615385	-2.46153846153846
132	9	17.2105263157895	-8.21052631578947
133	13	17.2105263157895	-4.21052631578947
134	19	20.4230769230769	-1.42307692307692
135	21	20.4230769230769	0.576923076923077
136	23	17.2105263157895	5.78947368421053
137	21	17.2105263157895	3.78947368421053
138	15	25.0714285714286	-10.0714285714286
139	19	17.2105263157895	1.78947368421053
140	26	25.0714285714286	0.928571428571427
141	16	17.2105263157895	-1.21052631578947
142	19	20.4230769230769	-1.42307692307692
143	31	25.0714285714286	5.92857142857143
144	19	17.2105263157895	1.78947368421053
145	15	17.2105263157895	-2.21052631578947
146	23	25.0714285714286	-2.07142857142857
147	17	17.2105263157895	-0.210526315789473
148	21	20.4230769230769	0.576923076923077
149	17	17.2105263157895	-0.210526315789473
150	25	25.0714285714286	-0.071428571428573
151	20	17.2105263157895	2.78947368421053
152	19	17.2105263157895	1.78947368421053
153	20	20.4230769230769	-0.423076923076923
154	17	21.5	-4.5
155	21	20.4230769230769	0.576923076923077
156	26	29.8125	-3.8125
157	17	20.4230769230769	-3.42307692307692
158	21	17.2105263157895	3.78947368421053
159	28	25.0714285714286	2.92857142857143

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292919692pn69o5l8jfbburl/2qd4l1292919744.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292919692pn69o5l8jfbburl/2qd4l1292919744.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292919692pn69o5l8jfbburl/3qd4l1292919744.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292919692pn69o5l8jfbburl/3qd4l1292919744.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292919692pn69o5l8jfbburl/4043o1292919744.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292919692pn69o5l8jfbburl/4043o1292919744.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 0 ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = 0 ; 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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