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BEL20-RP1(no cat)

*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: Wed, 22 Dec 2010 17:58:31 +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/22/t12930430811usqngnfibvyhfh.htm/, Retrieved Wed, 22 Dec 2010 19:38:06 +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/22/t12930430811usqngnfibvyhfh.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 «

3.04 493 9 3.030 9.026 25.64 104.8 3.28 481 11 2.803 9.787 27.97 105.2 3.51 462 13 2.768 9.536 27.62 105.6 3.69 457 12 2.883 9.490 23.31 105.8 3.92 442 13 2.863 9.736 29.07 106.1 4.29 439 15 2.897 9.694 29.58 106.5 4.31 488 13 3.013 9.647 28.63 106.71 4.42 521 16 3.143 9.753 29.92 106.68 4.59 501 10 3.033 10.070 32.68 107.41 4.76 485 14 3.046 10.137 31.54 107.15 4.83 464 14 3.111 9.984 32.43 107.5 4.83 460 45 3.013 9.732 26.54 107.22 4.76 467 13 2.987 9.103 25.85 107.11 4.99 460 8 2.996 9.155 27.60 107.57 4.78 448 7 2.833 9.308 25.71 107.81 5.06 443 3 2.849 9.394 25.38 108.75 4.65 436 3 2.795 9.948 28.57 109.43 4.54 431 4 2.845 10.177 27.64 109.62 4.51 484 4 2.915 10.002 25.36 109.54 4.49 510 0 2.893 9.728 25.90 109.53 3.99 513 -4 2.604 10.002 26.29 109.84 3.97 503 -14 2.642 10.063 21.74 109.67 3.51 471 -18 2.660 10.018 19.20 109.79 3.34 471 -8 2.639 9.960 19.32 109.56 3.29 476 -1 2.720 10.236 19.82 110.22 3.28 475 1 2.746 10.893 20.36 110.4 3.26 470 2 2.736 10.756 24.31 110.69 3.32 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	8 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Goodness of Fit
Correlation	0.9377
R-squared	0.8793
RMSE	0.2628

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	3.03	2.74488888888889	0.285111111111111
2	2.803	2.74488888888889	0.0581111111111108
3	2.768	2.74488888888889	0.0231111111111106
4	2.883	2.94794117647059	-0.0649411764705885
5	2.863	2.94794117647059	-0.0849411764705885
6	2.897	2.94794117647059	-0.0509411764705887
7	3.013	2.94794117647059	0.0650588235294114
8	3.143	2.94794117647059	0.195058823529411
9	3.033	2.94794117647059	0.0850588235294114
10	3.046	2.94794117647059	0.0980588235294113
11	3.111	2.94794117647059	0.163058823529412
12	3.013	2.94794117647059	0.0650588235294114
13	2.987	2.94794117647059	0.0390588235294116
14	2.996	2.94794117647059	0.0480588235294115
15	2.833	2.94794117647059	-0.114941176470588
16	2.849	2.94794117647059	-0.0989411764705883
17	2.795	2.94794117647059	-0.152941176470589
18	2.845	2.94794117647059	-0.102941176470588
19	2.915	2.94794117647059	-0.0329411764705885
20	2.893	2.94794117647059	-0.0549411764705887
21	2.604	2.30661111111111	0.297388888888888
22	2.642	1.976125	0.665875
23	2.66	1.976125	0.683875
24	2.639	2.30661111111111	0.332388888888888
25	2.72	2.30661111111111	0.413388888888889
26	2.746	2.74488888888889	0.00111111111111084
27	2.736	2.74488888888889	-0.00888888888888895
28	2.812	2.74488888888889	0.0671111111111107
29	2.799	2.74488888888889	0.0541111111111108
30	2.555	2.74488888888889	-0.189888888888889
31	2.305	2.30661111111111	-0.00161111111111145
32	2.215	2.30661111111111	-0.0916111111111118
33	2.066	2.30661111111111	-0.240611111111112
34	1.94	2.30661111111111	-0.366611111111112
35	2.042	2.30661111111111	-0.264611111111112
36	1.995	2.30661111111111	-0.311611111111112
37	1.947	1.976125	-0.0291249999999998
38	1.766	1.976125	-0.210125
39	1.635	1.976125	-0.341125
40	1.833	1.976125	-0.143125
41	1.91	1.976125	-0.066125
42	1.96	1.976125	-0.0161249999999999
43	1.97	1.976125	-0.00612499999999994
44	2.061	1.976125	0.084875
45	2.093	2.30661111111111	-0.213611111111112
46	2.121	1.976125	0.144875
47	2.175	2.30661111111111	-0.131611111111112
48	2.197	2.30661111111111	-0.109611111111112
49	2.35	2.30661111111111	0.0433888888888885
50	2.44	2.30661111111111	0.133388888888888
51	2.409	2.30661111111111	0.102388888888888
52	2.473	2.30661111111111	0.166388888888888
53	2.408	2.30661111111111	0.101388888888888
54	2.455	2.74488888888889	-0.289888888888889
55	2.448	2.30661111111111	0.141388888888888
56	2.498	2.81914285714286	-0.321142857142857
57	2.646	2.81914285714286	-0.173142857142857
58	2.757	2.81914285714286	-0.0621428571428568
59	2.849	2.81914285714286	0.0298571428571432
60	2.921	2.81914285714286	0.101857142857143
61	2.982	2.81914285714286	0.162857142857143
62	3.081	2.81914285714286	0.261857142857143
63	3.106	2.77866666666667	0.327333333333333
64	3.119	2.77866666666667	0.340333333333333
65	3.061	2.9345	0.1265
66	3.097	2.9345	0.1625
67	3.162	2.9345	0.2275
68	3.257	2.9345	0.3225
69	3.277	2.9345	0.3425
70	3.295	2.77866666666667	0.516333333333333
71	3.364	2.9345	0.4295
72	3.494	3.7693	-0.275300000000000
73	3.667	3.7693	-0.1023
74	3.813	3.7693	0.0437000000000003
75	3.918	3.7693	0.148700000000000
76	3.896	3.7693	0.1267
77	3.801	3.7693	0.0317000000000003
78	3.57	3.7693	-0.1993
79	3.702	3.7693	-0.0672999999999999
80	3.862	3.7693	0.0927000000000002
81	3.97	3.7693	0.200700000000000
82	4.139	4.24521052631579	-0.10621052631579
83	4.2	4.24521052631579	-0.04521052631579
84	4.291	4.24521052631579	0.0457894736842102
85	4.444	4.24521052631579	0.198789473684210
86	4.503	4.24521052631579	0.25778947368421
87	4.357	4.24521052631579	0.11178947368421
88	4.591	4.24521052631579	0.34578947368421
89	4.697	4.24521052631579	0.45178947368421
90	4.621	4.24521052631579	0.37578947368421
91	4.563	4.24521052631579	0.317789473684210
92	4.203	4.24521052631579	-0.0422105263157899
93	4.296	4.24521052631579	0.05078947368421
94	4.435	4.24521052631579	0.189789473684209
95	4.105	4.24521052631579	-0.140210526315790
96	4.117	4.24521052631579	-0.128210526315790
97	3.844	4.24521052631579	-0.401210526315790
98	3.721	4.24521052631579	-0.52421052631579
99	3.674	4.24521052631579	-0.57121052631579
100	3.858	4.24521052631579	-0.38721052631579
101	3.801	2.9345	0.8665
102	3.504	2.9345	0.5695
103	3.033	2.9345	0.0985
104	3.047	2.9345	0.112500000000000
105	2.962	2.9345	0.0275000000000003
106	2.198	2.236375	-0.0383749999999998
107	2.014	2.236375	-0.222375
108	1.863	1.976125	-0.113125
109	1.905	1.976125	-0.0711249999999999
110	1.811	1.976125	-0.165125
111	1.67	1.976125	-0.306125
112	1.864	1.976125	-0.112125000000000
113	2.052	2.236375	-0.184375000000000
114	2.03	2.236375	-0.206375
115	2.071	2.236375	-0.165375000000000
116	2.293	2.9345	-0.6415
117	2.443	2.9345	-0.4915
118	2.513	2.9345	-0.4215
119	2.467	2.9345	-0.4675
120	2.503	2.236375	0.266625000000000
121	2.54	2.236375	0.303625
122	2.483	2.236375	0.246625000000000
123	2.626	2.9345	-0.3085
124	2.656	2.77866666666667	-0.122666666666667
125	2.447	2.9345	-0.4875
126	2.467	2.9345	-0.4675
127	2.462	2.77866666666667	-0.316666666666667
128	2.505	2.77866666666667	-0.273666666666667
129	2.579	2.77866666666667	-0.199666666666667
130	2.649	2.77866666666667	-0.129666666666667
131	2.637	2.77866666666667	-0.141666666666667

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/22/t12930430811usqngnfibvyhfh/28f1w1293040702.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t12930430811usqngnfibvyhfh/28f1w1293040702.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t12930430811usqngnfibvyhfh/38f1w1293040702.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t12930430811usqngnfibvyhfh/38f1w1293040702.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t12930430811usqngnfibvyhfh/4joiy1293040702.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/22/t12930430811usqngnfibvyhfh/4joiy1293040702.ps (open in new window)

Parameters (Session):

par1 = 4 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 4 ; 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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