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Recursive Partitioning - Kijkcijfers

*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 17:52:54 +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/t1292953883fkaj5fhdui4nicb.htm/, Retrieved Tue, 21 Dec 2010 18:51:23 +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/t1292953883fkaj5fhdui4nicb.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 «

15561600 15.73 3.56 142.86 14917500 16.17 1.33 380.71 14805920 12.00 0.00 460.00 16958000 12.86 0.69 361.43 17605000 10.30 10.05 140.00 17131200 12.97 0.51 275.00 18474600 12.06 0.91 274.29 17286700 10.49 2.67 212.86 18574400 5.97 1.39 172.86 18056000 9.26 1.24 186.43 19701600 9.74 2.79 77.14 19061700 5.46 3.37 17.86 19681900 2.71 1.60 37.14 34521200 3.90 4.73 42.86 19922700 1.51 0.79 85.00 20177900 5.01 0.67 45.00 19759900 2.96 0.00 206.43 23076700 -1.97 0.60 178.57 22532000 -4.61 0.40 285.71 22029400 4.27 2.24 58.57 22587000 4.01 5.74 88.57 23256600 0.04 0.06 309.29 22680300 3.04 0.87 58.57 21916400 2.29 4.91 132.14 19640200 4.37 1.93 3.57 18813100 6.39 0.41 102.86 18730000 5.74 1.21 185.71 18154700 7.64 2.01 177.14 17848800 7.07 0.00 530.00 18077500 6.23 6.49 162.86 17133100 10.20 0.00 553.57 16602600 14.07 0.31 258.57 15878900 12.83 4.87 326.43 15789100 12.04 1.37 580.00 15422000 11.97 0.19 286.43 14661400 12.63 0.34 310.71 15879200 13.56 3.60 148.57 14339300 15.66 0.1 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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.8876
R-squared	0.7879
RMSE	1720765.7442

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	15561600	17065558.75	-1503958.75
2	14917500	12899698.2857143	2017801.71428571
3	14805920	15320817.3571429	-514897.357142856
4	16958000	15320817.3571429	1637182.64285714
5	17605000	17896694.0454545	-291694.045454547
6	17131200	17065558.75	65641.25
7	18474600	17065558.75	1409041.25
8	17286700	17896694.0454545	-609994.045454547
9	18574400	17896694.0454545	677705.954545453
10	18056000	17896694.0454545	159305.954545453
11	19701600	19331636	369964
12	19061700	19331636	-269936
13	19681900	21978075.3043478	-2296175.30434782
14	34521200	21978075.3043478	12543124.6956522
15	19922700	21978075.3043478	-2055375.30434782
16	20177900	19331636	846264
17	19759900	21978075.3043478	-2218175.30434782
18	23076700	21978075.3043478	1098624.69565218
19	22532000	21978075.3043478	553924.695652176
20	22029400	21978075.3043478	51324.6956521757
21	22587000	21978075.3043478	608924.695652176
22	23256600	21978075.3043478	1278524.69565218
23	22680300	21978075.3043478	702224.695652176
24	21916400	21978075.3043478	-61675.3043478243
25	19640200	19331636	308564
26	18813100	19331636	-518536
27	18730000	17896694.0454545	833305.954545453
28	18154700	17896694.0454545	258005.954545453
29	17848800	17896694.0454545	-47894.0454545468
30	18077500	17896694.0454545	180805.954545453
31	17133100	17896694.0454545	-763594.045454547
32	16602600	17065558.75	-462958.75
33	15878900	15320817.3571429	558082.642857144
34	15789100	15320817.3571429	468282.642857144
35	15422000	15320817.3571429	101182.642857144
36	14661400	15320817.3571429	-659417.357142856
37	15879200	17065558.75	-1186358.75
38	14339300	15320817.3571429	-981517.357142856
39	13169600	12899698.2857143	269901.714285715
40	14528900	15320817.3571429	-791917.357142856
41	13375800	15320817.3571429	-1945017.35714286
42	12309900	12899698.2857143	-589798.285714285
43	11933900	12899698.2857143	-965798.285714285
44	10061900	12899698.2857143	-2837798.28571429
45	12609600	12899698.2857143	-290098.285714285
46	11156500	12899698.2857143	-1743198.28571429
47	12187200	12899698.2857143	-712498.285714285
48	11284300	12899698.2857143	-1615398.28571429
49	10177000	12899698.2857143	-2722698.28571429
50	10970720	12899698.2857143	-1928978.28571429
51	10820680	12899698.2857143	-2079018.28571429
52	11492390	12899698.2857143	-1407308.28571429
53	14573750	12899698.2857143	1674051.71428571
54	13992820	12899698.2857143	1093121.71428571
55	14727070	12899698.2857143	1827371.71428571
56	15685360	15320817.3571429	364542.642857144
57	16736210	17065558.75	-329348.75
58	17950180	17065558.75	884621.25
59	17002730	17896694.0454545	-893964.045454547
60	17415160	17896694.0454545	-481534.045454547
61	17929810	17896694.0454545	33115.9545454532
62	17865790	17896694.0454545	-30904.0454545468
63	19202360	19331636	-129276
64	19085000	17896694.0454545	1188305.95454545
65	18188880	17065558.75	1123321.25
66	18466410	19331636	-865226
67	18520400	19331636	-811236
68	20025500	21978075.3043478	-1952575.30434782
69	20636100	21978075.3043478	-1341975.30434782
70	20672000	21978075.3043478	-1306075.30434782
71	22589100	21978075.3043478	611024.695652176
72	21864800	21978075.3043478	-113275.304347824
73	22750100	21978075.3043478	772024.695652176
74	22548746	21978075.3043478	570670.695652176
75	21325495	21978075.3043478	-652580.304347824
76	21556563	21978075.3043478	-421512.304347824
77	21415269	21978075.3043478	-562806.304347824
78	20401054	19331636	1069418
79	19062253	21978075.3043478	-2915822.30434782
80	19085706	21978075.3043478	-2892369.30434782
81	19279967	17896694.0454545	1383272.95454545
82	18552045	17896694.0454545	655350.954545453
83	17800733	17896694.0454545	-95961.0454545468
84	17142490	17896694.0454545	-754204.045454547
85	17593173	17896694.0454545	-303521.045454547
86	17633859	17896694.0454545	-262835.045454547
87	17336613	15320817.3571429	2015795.64285714
88	17008347	17896694.0454545	-888347.045454547
89	17951965	17896694.0454545	55270.9545454532
90	14520929	15320817.3571429	-799888.357142856
91	16941217	15320817.3571429	1620399.64285714
92	15436824	12899698.2857143	2537125.71428571
93	14744261	12899698.2857143	1844562.71428571
94	14248004	15320817.3571429	-1072813.35714286
95	11540953	12899698.2857143	-1358745.28571429
96	12881661	12899698.2857143	-18037.2857142854
97	15185757	12899698.2857143	2286058.71428571
98	13554339	12899698.2857143	654640.714285715
99	13575106	12899698.2857143	675407.714285715
100	12238400	12899698.2857143	-661298.285714285
101	13303614	12899698.2857143	403915.714285715
102	14151478	12899698.2857143	1251779.71428571
103	14172009	12899698.2857143	1272310.71428571
104	14022320	12899698.2857143	1122621.71428571

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292953883fkaj5fhdui4nicb/218va1292953966.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292953883fkaj5fhdui4nicb/218va1292953966.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292953883fkaj5fhdui4nicb/3tiud1292953966.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292953883fkaj5fhdui4nicb/3tiud1292953966.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292953883fkaj5fhdui4nicb/4mrcg1292953966.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292953883fkaj5fhdui4nicb/4mrcg1292953966.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 1 ; 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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