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W10- RP (1)

*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: Sat, 11 Dec 2010 18:30:10 +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/11/t1292092233f1dgw818i4jaxem.htm/, Retrieved Sat, 11 Dec 2010 19:30:36 +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/11/t1292092233f1dgw818i4jaxem.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 «

0 24 14 11 12 24 26 0 25 11 7 8 25 23 0 17 6 17 8 30 25 1 18 12 10 8 19 23 1 18 8 12 9 22 19 1 16 10 12 7 22 29 1 20 10 11 4 25 25 1 16 11 11 11 23 21 1 18 16 12 7 17 22 1 17 11 13 7 21 25 0 23 13 14 12 19 24 0 30 12 16 10 19 18 1 23 8 11 10 15 22 1 18 12 10 8 16 15 1 15 11 11 8 23 22 1 12 4 15 4 27 28 0 21 9 9 9 22 20 1 15 8 11 8 14 12 1 20 8 17 7 22 24 0 31 14 17 11 23 20 0 27 15 11 9 23 21 1 34 16 18 11 21 20 1 21 9 14 13 19 21 1 31 14 10 8 18 23 1 19 11 11 8 20 28 0 16 8 15 9 23 24 1 20 9 15 6 25 24 1 21 9 13 9 19 24 1 22 9 16 9 24 23 1 17 9 13 6 22 23 1 24 10 9 6 25 29 0 25 16 18 16 26 24 0 26 11 18 5 29 18 1 25 8 12 7 32 25 1 17 9 17 9 25 21 1 32 16 9 6 29 26 1 33 11 9 6 28 22 1 13 16 12 5 17 22 1 32 12 18 12 28 22 1 25 12 12 7 29 23 1 29 14 18 10 26 30 1 22 9 14 9 25 23 1 18 10 15 8 14 17 1 17 9 16 5 25 23 0 20 10 10 8 26 23 1 15 12 11 8 20 25 1 20 14 14 10 18 24 1 33 14 9 6 32 24 0 29 10 12 8 25 23 1 23 14 17 7 25 21 0 26 16 5 4 23 24 1 18 9 12 8 21 2 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	18 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	0.8356
R-squared	0.6982
RMSE	3.6424

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	24	26.1428571428571	-2.14285714285714
2	25	20.6842105263158	4.31578947368421
3	17	20.6842105263158	-3.68421052631579
4	18	20.6842105263158	-2.68421052631579
5	18	20.6842105263158	-2.68421052631579
6	16	20.6842105263158	-4.68421052631579
7	20	20.6842105263158	-0.684210526315791
8	16	20.6842105263158	-4.68421052631579
9	18	26.1428571428571	-8.14285714285714
10	17	20.6842105263158	-3.68421052631579
11	23	20.6842105263158	2.31578947368421
12	30	20.6842105263158	9.3157894736842
13	23	20.6842105263158	2.31578947368421
14	18	20.6842105263158	-2.68421052631579
15	15	20.6842105263158	-5.68421052631579
16	12	20.6842105263158	-8.6842105263158
17	21	20.6842105263158	0.315789473684209
18	15	10.1857142857143	4.81428571428571
19	20	20.6842105263158	-0.684210526315791
20	31	26.1428571428571	4.85714285714286
21	27	26.1428571428571	0.857142857142858
22	34	26.1428571428571	7.85714285714286
23	21	20.6842105263158	0.315789473684209
24	31	26.1428571428571	4.85714285714286
25	19	20.6842105263158	-1.68421052631579
26	16	20.6842105263158	-4.68421052631579
27	20	20.6842105263158	-0.684210526315791
28	21	20.6842105263158	0.315789473684209
29	22	20.6842105263158	1.31578947368421
30	17	20.6842105263158	-3.68421052631579
31	24	20.6842105263158	3.31578947368421
32	25	26.1428571428571	-1.14285714285714
33	26	20.6842105263158	5.31578947368421
34	25	20.6842105263158	4.31578947368421
35	17	20.6842105263158	-3.68421052631579
36	32	26.1428571428571	5.85714285714286
37	33	20.6842105263158	12.3157894736842
38	13	26.1428571428571	-13.1428571428571
39	32	20.6842105263158	11.3157894736842
40	25	20.6842105263158	4.31578947368421
41	29	26.1428571428571	2.85714285714286
42	22	20.6842105263158	1.31578947368421
43	18	20.6842105263158	-2.68421052631579
44	17	20.6842105263158	-3.68421052631579
45	20	20.6842105263158	-0.684210526315791
46	15	20.6842105263158	-5.68421052631579
47	20	26.1428571428571	-6.14285714285714
48	33	26.1428571428571	6.85714285714286
49	29	20.6842105263158	8.3157894736842
50	23	26.1428571428571	-3.14285714285714
51	26	26.1428571428571	-0.142857142857142
52	18	20.6842105263158	-2.68421052631579
53	20	20.6842105263158	-0.684210526315791
54	6	10.1857142857143	-4.18571428571429
55	8	12.0270270270270	-4.02702702702703
56	13	12.0270270270270	0.972972972972974
57	10	10.1857142857143	-0.185714285714285
58	8	10.1857142857143	-2.18571428571429
59	7	10.1857142857143	-3.18571428571429
60	15	10.1857142857143	4.81428571428571
61	9	10.1857142857143	-1.18571428571429
62	10	10.1857142857143	-0.185714285714285
63	12	10.1857142857143	1.81428571428571
64	13	10.1857142857143	2.81428571428571
65	10	10.1857142857143	-0.185714285714285
66	11	12.0270270270270	-1.02702702702703
67	8	12.0270270270270	-4.02702702702703
68	9	10.1857142857143	-1.18571428571429
69	13	10.1857142857143	2.81428571428571
70	11	10.1857142857143	0.814285714285715
71	8	12.0270270270270	-4.02702702702703
72	9	10.1857142857143	-1.18571428571429
73	9	12.0270270270270	-3.02702702702703
74	15	12.0270270270270	2.97297297297297
75	9	12.0270270270270	-3.02702702702703
76	10	12.0270270270270	-2.02702702702703
77	14	10.1857142857143	3.81428571428571
78	12	10.1857142857143	1.81428571428571
79	12	10.1857142857143	1.81428571428571
80	11	12.0270270270270	-1.02702702702703
81	14	12.0270270270270	1.97297297297297
82	6	10.1857142857143	-4.18571428571429
83	12	10.1857142857143	1.81428571428571
84	8	10.1857142857143	-2.18571428571429
85	14	12.0270270270270	1.97297297297297
86	11	10.1857142857143	0.814285714285715
87	10	10.1857142857143	-0.185714285714285
88	14	10.1857142857143	3.81428571428571
89	12	12.0270270270270	-0.0270270270270263
90	10	10.1857142857143	-0.185714285714285
91	14	12.0270270270270	1.97297297297297
92	5	10.1857142857143	-5.18571428571429
93	11	10.1857142857143	0.814285714285715
94	10	10.1857142857143	-0.185714285714285
95	9	10.1857142857143	-1.18571428571429
96	10	12.0270270270270	-2.02702702702703
97	16	12.0270270270270	3.97297297297297
98	13	12.0270270270270	0.972972972972974
99	9	10.1857142857143	-1.18571428571429
100	10	10.1857142857143	-0.185714285714285
101	10	12.0270270270270	-2.02702702702703
102	7	10.1857142857143	-3.18571428571429
103	9	10.1857142857143	-1.18571428571429
104	8	10.1857142857143	-2.18571428571429
105	14	12.0270270270270	1.97297297297297
106	14	12.0270270270270	1.97297297297297
107	8	10.1857142857143	-2.18571428571429
108	9	12.0270270270270	-3.02702702702703
109	14	12.0270270270270	1.97297297297297
110	14	10.1857142857143	3.81428571428571
111	8	10.1857142857143	-2.18571428571429
112	8	12.0270270270270	-4.02702702702703
113	8	10.1857142857143	-2.18571428571429
114	7	12.0270270270270	-5.02702702702703
115	6	10.1857142857143	-4.18571428571429
116	8	10.1857142857143	-2.18571428571429
117	6	10.1857142857143	-4.18571428571429
118	11	10.1857142857143	0.814285714285715
119	14	12.0270270270270	1.97297297297297
120	11	10.1857142857143	0.814285714285715
121	11	12.0270270270270	-1.02702702702703
122	11	10.1857142857143	0.814285714285715
123	14	10.1857142857143	3.81428571428571
124	8	10.1857142857143	-2.18571428571429
125	20	10.1857142857143	9.81428571428571
126	11	10.1857142857143	0.814285714285715
127	8	10.1857142857143	-2.18571428571429
128	11	10.1857142857143	0.814285714285715
129	10	10.1857142857143	-0.185714285714285
130	14	12.0270270270270	1.97297297297297
131	11	10.1857142857143	0.814285714285715
132	9	10.1857142857143	-1.18571428571429
133	9	10.1857142857143	-1.18571428571429
134	8	10.1857142857143	-2.18571428571429
135	10	10.1857142857143	-0.185714285714285
136	13	12.0270270270270	0.972972972972974
137	13	10.1857142857143	2.81428571428571
138	12	10.1857142857143	1.81428571428571
139	8	10.1857142857143	-2.18571428571429
140	13	10.1857142857143	2.81428571428571
141	14	12.0270270270270	1.97297297297297
142	12	12.0270270270270	-0.0270270270270263
143	14	12.0270270270270	1.97297297297297
144	15	10.1857142857143	4.81428571428571
145	13	10.1857142857143	2.81428571428571
146	16	12.0270270270270	3.97297297297297
147	9	12.0270270270270	-3.02702702702703
148	9	10.1857142857143	-1.18571428571429
149	9	10.1857142857143	-1.18571428571429
150	8	10.1857142857143	-2.18571428571429
151	7	10.1857142857143	-3.18571428571429
152	16	12.0270270270270	3.97297297297297
153	11	12.0270270270270	-1.02702702702703
154	9	10.1857142857143	-1.18571428571429
155	11	10.1857142857143	0.814285714285715
156	9	10.1857142857143	-1.18571428571429
157	14	12.0270270270270	1.97297297297297
158	13	10.1857142857143	2.81428571428571
159	16	12.0270270270270	3.97297297297297

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292092233f1dgw818i4jaxem/2jccy1292092191.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292092233f1dgw818i4jaxem/2jccy1292092191.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292092233f1dgw818i4jaxem/3jccy1292092191.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292092233f1dgw818i4jaxem/3jccy1292092191.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292092233f1dgw818i4jaxem/4tlu11292092191.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292092233f1dgw818i4jaxem/4tlu11292092191.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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