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Recursieve Partitioning: arbeidshandicap

*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: Mon, 20 Dec 2010 14:38:00 +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/20/t1292855799v00axwvc3mue6z0.htm/, Retrieved Mon, 20 Dec 2010 15:36:43 +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/20/t1292855799v00axwvc3mue6z0.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 «

22.037 17.759 14.116 104.708 158.620 21.732 17.530 13.504 101.817 154.583 21.172 17.139 13.168 97.898 149.377 21.388 16.916 13.064 95.559 146.927 22.053 16.543 12.828 92.822 144.246 22.687 16.317 12.541 90.848 142.393 24.793 18.161 13.547 101.141 157.642 26.113 19.144 13.710 105.841 164.808 23.708 16.947 12.535 93.647 146.837 23.554 16.491 12.386 90.923 143.354 23.222 16.428 12.253 89.130 141.033 23.363 16.639 12.484 90.212 142.698 24.023 16.821 12.966 93.196 147.006 23.355 16.765 12.770 91.861 144.751 23.276 16.533 12.660 90.593 143.062 23.085 16.554 12.514 89.895 142.048 23.173 16.494 12.430 88.819 140.916 23.487 16.612 12.372 87.924 140.395 25.576 17.933 13.085 96.906 153.500 26.311 19.070 13.454 101.217 160.052 27.109 18.179 13.361 98.709 157.358 27.060 17.830 13.713 98.139 156.742 26.490 17.349 13.601 95.529 152.969 27.157 17.919 14.090 98.577 157.743 26.973 18.269 14.452 100.772 160.466 27.589 18.385 14.108 100.180 160.262 27.246 18.260 14.036 99.200 158.742 26.845 17.905 13.3 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	15 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Goodness of Fit
Correlation	0.9923
R-squared	0.9847
RMSE	0.6235

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	17.759	17.8651666666667	-0.106166666666667
2	17.53	17.8651666666667	-0.335166666666666
3	17.139	16.6570714285714	0.481928571428572
4	16.916	16.6570714285714	0.258928571428573
5	16.543	16.6570714285714	-0.114071428571428
6	16.317	16.6570714285714	-0.340071428571427
7	18.161	17.8651666666667	0.295833333333334
8	19.144	18.8695	0.2745
9	16.947	16.6570714285714	0.289928571428572
10	16.491	16.6570714285714	-0.166071428571428
11	16.428	16.6570714285714	-0.229071428571427
12	16.639	16.6570714285714	-0.0180714285714281
13	16.821	16.6570714285714	0.163928571428574
14	16.765	16.6570714285714	0.107928571428573
15	16.533	16.6570714285714	-0.124071428571426
16	16.554	16.6570714285714	-0.103071428571429
17	16.494	16.6570714285714	-0.163071428571428
18	16.612	16.6570714285714	-0.0450714285714291
19	17.933	17.8651666666667	0.0678333333333327
20	19.07	18.8695	0.200500000000002
21	18.179	17.8651666666667	0.313833333333331
22	17.83	17.8651666666667	-0.0351666666666688
23	17.349	17.8651666666667	-0.516166666666667
24	17.919	17.8651666666667	0.0538333333333334
25	18.269	18.8695	-0.6005
26	18.385	18.8695	-0.484499999999997
27	18.26	17.8651666666667	0.394833333333334
28	17.905	17.8651666666667	0.0398333333333341
29	17.73	17.8651666666667	-0.135166666666667
30	17.827	17.8651666666667	-0.0381666666666653
31	19.978	20.1624444444444	-0.184444444444445
32	20.315	20.1624444444444	0.152555555555555
33	18.931	18.8695	0.0615000000000023
34	18.732	18.8695	-0.137499999999999
35	19.155	18.8695	0.285500000000003
36	19.27	18.8695	0.400500000000001
37	19.754	20.1624444444444	-0.408444444444445
38	19.845	20.1624444444444	-0.317444444444448
39	19.937	20.1624444444444	-0.225444444444445
40	20.097	20.1624444444444	-0.0654444444444451
41	19.981	20.1624444444444	-0.181444444444445
42	20.502	20.1624444444444	0.339555555555553
43	22.712	22.123	0.589000000000002
44	23.101	22.123	0.978000000000002
45	21.381	22.123	-0.741999999999997
46	21.255	22.123	-0.867999999999999
47	21.053	20.1624444444444	0.890555555555554
48	21.561	22.123	-0.561999999999998
49	21.923	22.123	-0.199999999999999
50	22.001	22.123	-0.121999999999996
51	22.369	22.123	0.246000000000002
52	22.32	22.123	0.197000000000003
53	22.149	22.123	0.0260000000000034
54	22.581	22.123	0.458000000000002
55	24.896	26.6273333333333	-1.73133333333334
56	26.61	26.6273333333333	-0.0173333333333368
57	25.417	26.6273333333333	-1.21033333333333
58	26.484	26.6273333333333	-0.143333333333334
59	26.329	26.6273333333333	-0.298333333333336
60	26.989	26.6273333333333	0.361666666666665
61	27.18	26.6273333333333	0.552666666666664
62	27.284	26.6273333333333	0.656666666666663
63	27.436	26.6273333333333	0.808666666666664
64	27.082	26.6273333333333	0.454666666666665
65	26.818	26.6273333333333	0.190666666666665
66	27.003	26.6273333333333	0.375666666666664
67	29.344	28.4949259259259	0.849074074074078
68	29.777	28.4949259259259	1.28207407407408
69	28.07	28.4949259259259	-0.424925925925923
70	27.993	28.4949259259259	-0.501925925925924
71	27.672	28.4949259259259	-0.822925925925922
72	27.802	28.4949259259259	-0.692925925925923
73	27.328	28.4949259259259	-1.16692592592592
74	27.666	28.4949259259259	-0.828925925925923
75	27.456	28.4949259259259	-1.03892592592592
76	27.796	28.4949259259259	-0.698925925925924
77	27.642	28.4949259259259	-0.852925925925923
78	27.651	28.4949259259259	-0.843925925925923
79	29.604	28.4949259259259	1.10907407407408
80	29.196	28.4949259259259	0.701074074074079
81	28.328	28.4949259259259	-0.166925925925923
82	27.986	27.454375	0.531625000000002
83	27.738	27.454375	0.283625000000001
84	27.867	27.454375	0.412625000000002
85	27.58	28.4949259259259	-0.914925925925925
86	27.381	28.4949259259259	-1.11392592592592
87	27.292	27.454375	-0.162374999999997
88	26.944	27.454375	-0.510375
89	26.329	27.454375	-1.12537500000000
90	29.023	28.4949259259259	0.528074074074077
91	28.705	28.4949259259259	0.210074074074075
92	27.213	27.454375	-0.241374999999998
93	27.063	27.454375	-0.391375
94	27.01	27.454375	-0.444374999999997
95	27.709	27.454375	0.254625000000001
96	27.802	27.454375	0.347625000000001
97	27.687	27.454375	0.232625000000002
98	27.719	27.454375	0.264625000000002
99	27.961	27.454375	0.506625
100	27.203	27.454375	-0.251374999999999
101	27.747	27.454375	0.292625000000001
102	30.713	28.4949259259259	2.21807407407408
103	30.395	28.4949259259259	1.90007407407408
104	28.895	28.4949259259259	0.400074074074077
105	28.46	28.4949259259259	-0.034925925925922
106	28.286	28.4949259259259	-0.208925925925922
107	28.984	28.4949259259259	0.489074074074079
108	29.624	28.4949259259259	1.12907407407408
109	29.734	29.9608	-0.226800000000001
110	30.603	29.9608	0.642199999999999
111	30.427	29.9608	0.466199999999997
112	30.269	29.9608	0.308199999999996
113	30.798	29.9608	0.837199999999996
114	32.676	31.3735714285714	1.30242857142857
115	32.68	31.3735714285714	1.30642857142857
116	30.737	29.9608	0.776199999999996
117	30.3	29.9608	0.339199999999998
118	30.321	29.9608	0.360199999999999
119	31.282	31.3735714285714	-0.0915714285714309
120	30.868	31.3735714285714	-0.505571428571432
121	30.749	31.3735714285714	-0.624571428571432
122	30.236	29.9608	0.275199999999998
123	29.99	29.9608	0.0291999999999959
124	29.427	29.9608	-0.533800000000003
125	29.376	29.9608	-0.584800000000001
126	30.828	31.3735714285714	-0.545571428571431
127	30.532	31.3735714285714	-0.84157142857143
128	29.166	29.9608	-0.794800000000002
129	29.124	29.9608	-0.836800000000004
130	28.904	29.9608	-1.05680000000000
131	27.992	28.4949259259259	-0.502925925925922

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292855799v00axwvc3mue6z0/2ude91292855864.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292855799v00axwvc3mue6z0/2ude91292855864.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292855799v00axwvc3mue6z0/3ude91292855864.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292855799v00axwvc3mue6z0/3ude91292855864.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292855799v00axwvc3mue6z0/4nmvu1292855864.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292855799v00axwvc3mue6z0/4nmvu1292855864.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 2 ; 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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We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

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