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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: Fri, 10 Dec 2010 09:15:12 +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/10/t1291972442mqr6gn04bbt43v6.htm/, Retrieved Fri, 10 Dec 2010 10:14:02 +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/10/t1291972442mqr6gn04bbt43v6.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 «

40399 44164 44496 43110 43880 195722 198563 229139 229527 211868 36763 40399 44164 44496 43110 202196 195722 198563 229139 229527 37903 36763 40399 44164 44496 205816 202196 195722 198563 229139 35532 37903 36763 40399 44164 212588 205816 202196 195722 198563 35533 35532 37903 36763 40399 214320 212588 205816 202196 195722 32110 35533 35532 37903 36763 220375 214320 212588 205816 202196 33374 32110 35533 35532 37903 204442 220375 214320 212588 205816 35462 33374 32110 35533 35532 206903 204442 220375 214320 212588 33508 35462 33374 32110 35533 214126 206903 204442 220375 214320 36080 33508 35462 33374 32110 226899 214126 206903 204442 220375 34560 36080 33508 35462 33374 223532 226899 214126 206903 204442 38737 34560 36080 33508 35462 195309 223532 226899 214126 206903 38144 38737 34560 36080 33508 186005 195309 223532 226899 214126 37594 38144 38737 34560 36080 188906 186005 195309 223532 226899 36424 37594 38144 38737 34560 191563 188906 186005 195309 223532 36843 36424 37594 38144 38737 1892 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	7 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.9643
R-squared	0.9299
RMSE	2061.7021

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	40399	42347.5625	-1948.5625
2	36763	37993.6470588235	-1230.64705882353
3	37903	37993.6470588235	-90.6470588235316
4	35532	37993.6470588235	-2461.64705882353
5	35533	33985	1548
6	32110	33985	-1875
7	33374	33985	-611
8	35462	33985	1477
9	33508	33985	-477
10	36080	33985	2095
11	34560	33985	575
12	38737	33985	4752
13	38144	37993.6470588235	150.352941176468
14	37594	37993.6470588235	-399.647058823532
15	36424	37993.6470588235	-1569.64705882353
16	36843	37993.6470588235	-1150.64705882353
17	37246	37993.6470588235	-747.647058823532
18	38661	37993.6470588235	667.352941176468
19	40454	37993.6470588235	2460.35294117647
20	44928	42347.5625	2580.4375
21	48441	46626.8888888889	1814.11111111111
22	48140	46626.8888888889	1513.11111111111
23	45998	46626.8888888889	-628.88888888889
24	47369	46626.8888888889	742.11111111111
25	49554	46626.8888888889	2927.11111111111
26	47510	46626.8888888889	883.11111111111
27	44873	46626.8888888889	-1753.88888888889
28	45344	46626.8888888889	-1282.88888888889
29	42413	46626.8888888889	-4213.88888888889
30	36912	42347.5625	-5435.5625
31	43452	37993.6470588235	5458.35294117647
32	42142	42347.5625	-205.5625
33	44382	42347.5625	2034.4375
34	43636	42347.5625	1288.4375
35	44167	42347.5625	1819.4375
36	44423	42347.5625	2075.4375
37	42868	42347.5625	520.4375
38	43908	42347.5625	1560.4375
39	42013	42347.5625	-334.5625
40	38846	42347.5625	-3501.5625
41	35087	37993.6470588235	-2906.64705882353
42	33026	33985	-959
43	34646	33985	661
44	37135	33985	3150
45	37985	37993.6470588235	-8.64705882353155
46	43121	37993.6470588235	5127.35294117647
47	43722	42347.5625	1374.4375
48	43630	42347.5625	1282.4375
49	42234	42347.5625	-113.5625
50	39351	42347.5625	-2996.5625
51	39327	37993.6470588235	1333.35294117647
52	35704	37993.6470588235	-2289.64705882353
53	30466	33985	-3519
54	28155	28761.96	-606.959999999999
55	29257	28761.96	495.040000000001
56	29998	28761.96	1236.04
57	32529	28761.96	3767.04
58	34787	33985	802
59	33855	33985	-130
60	34556	33985	571
61	31348	33985	-2637
62	30805	28761.96	2043.04
63	28353	28761.96	-408.959999999999
64	24514	28761.96	-4247.96
65	21106	24059.85	-2953.85
66	21346	20819.9	526.099999999999
67	23335	20819.9	2515.1
68	24379	24059.85	319.150000000001
69	26290	24059.85	2230.15
70	30084	28761.96	1322.04
71	29429	28761.96	667.040000000001
72	30632	28761.96	1870.04
73	27349	28761.96	-1412.96
74	27264	28761.96	-1497.96
75	27474	28761.96	-1287.96
76	24482	28761.96	-4279.96
77	21453	24059.85	-2606.85
78	18788	20819.9	-2031.9
79	19282	20819.9	-1537.9
80	19713	20819.9	-1106.90000000000
81	21917	20819.9	1097.10000000000
82	23812	24059.85	-247.849999999999
83	23785	24059.85	-274.849999999999
84	24696	24059.85	636.150000000001
85	24562	24059.85	502.150000000001
86	23580	24059.85	-479.849999999999
87	24939	24059.85	879.150000000001
88	23899	24059.85	-160.849999999999
89	21454	24059.85	-2605.85
90	19761	20819.9	-1058.90000000000
91	19815	20819.9	-1004.90000000000
92	20780	20819.9	-39.9000000000015
93	23462	20819.9	2642.1
94	25005	24059.85	945.150000000001
95	24725	24059.85	665.150000000001
96	26198	24059.85	2138.15
97	27543	28761.96	-1218.96
98	26471	28761.96	-2290.96
99	26558	28761.96	-2203.96
100	25317	28761.96	-3444.96
101	22896	24059.85	-1163.85000000000
102	22248	24059.85	-1811.85
103	23406	24059.85	-653.849999999999
104	25073	24059.85	1013.15000000000
105	27691	24059.85	3631.15
106	30599	28761.96	1837.04
107	31948	28761.96	3186.04
108	32946	33985	-1039
109	34012	33985	27
110	32936	33985	-1049
111	32974	33985	-1011
112	30951	33985	-3034
113	29812	28761.96	1050.04
114	29010	28761.96	248.040000000001
115	31068	28761.96	2306.04
116	32447	28761.96	3685.04
117	34844	33985	859
118	35676	33985	1691
119	35387	33985	1402
120	36488	33985	2503
121	35652	37993.6470588235	-2341.64705882353
122	33488	33985	-497
123	32914	33985	-1071
124	29781	33985	-4204
125	27951	28761.96	-810.959999999999

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291972442mqr6gn04bbt43v6/2hvnl1291972502.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291972442mqr6gn04bbt43v6/2hvnl1291972502.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291972442mqr6gn04bbt43v6/3sm561291972502.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291972442mqr6gn04bbt43v6/3sm561291972502.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291972442mqr6gn04bbt43v6/43em91291972502.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291972442mqr6gn04bbt43v6/43em91291972502.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 1 ; 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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