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Recursieve 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: Mon, 20 Dec 2010 13:43:03 +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/t1292852632a1krzwwp3jnlsia.htm/, Retrieved Mon, 20 Dec 2010 14:43:52 +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/t1292852632a1krzwwp3jnlsia.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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Goodness of Fit
Correlation	0.9808
R-squared	0.9619
RMSE	1.395

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	22.037	23.2437222222222	-1.20672222222222
2	21.732	23.2437222222222	-1.51172222222222
3	21.172	23.2437222222222	-2.07172222222222
4	21.388	23.2437222222222	-1.85572222222222
5	22.053	23.2437222222222	-1.19072222222222
6	22.687	23.2437222222222	-0.55672222222222
7	24.793	27.1006875	-2.3076875
8	26.113	27.1006875	-0.9876875
9	23.708	23.2437222222222	0.464277777777777
10	23.554	23.2437222222222	0.310277777777777
11	23.222	23.2437222222222	-0.0217222222222198
12	23.363	23.2437222222222	0.119277777777778
13	24.023	23.2437222222222	0.779277777777779
14	23.355	23.2437222222222	0.111277777777779
15	23.276	23.2437222222222	0.0322777777777787
16	23.085	23.2437222222222	-0.158722222222220
17	23.173	23.2437222222222	-0.0707222222222228
18	23.487	23.2437222222222	0.243277777777777
19	25.576	27.1006875	-1.5246875
20	26.311	27.1006875	-0.7896875
21	27.109	27.1006875	0.00831250000000239
22	27.06	27.1006875	-0.0406875000000007
23	26.49	23.2437222222222	3.24627777777778
24	27.157	27.1006875	0.0563125000000007
25	26.973	27.1006875	-0.127687500000000
26	27.589	27.1006875	0.488312499999999
27	27.246	27.1006875	0.145312499999999
28	26.845	27.1006875	-0.255687500000001
29	26.582	23.2437222222222	3.33827777777778
30	26.544	27.1006875	-0.556687499999999
31	29.105	27.1006875	2.0043125
32	28.703	27.1006875	1.6023125
33	27.921	27.1006875	0.8203125
34	28.566	27.1006875	1.4653125
35	29.86	31.01125	-1.15125
36	30.194	31.01125	-0.817250000000001
37	31.33	31.01125	0.318749999999998
38	31.018	31.01125	0.00675000000000026
39	30.954	31.01125	-0.0572499999999998
40	31.398	31.01125	0.386749999999999
41	31.267	31.01125	0.255749999999999
42	32.069	31.01125	1.05775000000000
43	34.665	34.9148461538462	-0.249846153846157
44	35.834	34.9148461538462	0.919153846153847
45	34.034	34.9148461538462	-0.880846153846157
46	34.435	34.9148461538462	-0.479846153846154
47	34	34.9148461538462	-0.914846153846156
48	35.216	34.9148461538462	0.301153846153845
49	35.734	34.9148461538462	0.819153846153846
50	35.347	34.9148461538462	0.432153846153845
51	35.357	34.9148461538462	0.442153846153843
52	34.802	34.9148461538462	-0.112846153846156
53	34.493	34.9148461538462	-0.421846153846154
54	35.047	34.9148461538462	0.132153846153841
55	37.386	35.7877647058824	1.59823529411765
56	38.691	37.5239523809524	1.16704761904762
57	37.249	35.7877647058824	1.46123529411765
58	37.668	37.5239523809524	0.144047619047619
59	36.764	37.5239523809524	-0.759952380952377
60	37.926	37.5239523809524	0.402047619047622
61	38.145	37.5239523809524	0.621047619047623
62	37.664	37.5239523809524	0.140047619047621
63	37.449	37.5239523809524	-0.0749523809523822
64	37.389	37.5239523809524	-0.134952380952377
65	37.121	37.5239523809524	-0.402952380952378
66	37.447	37.5239523809524	-0.0769523809523776
67	39.751	41.6092857142857	-1.85828571428571
68	40.154	41.6092857142857	-1.45528571428571
69	38.814	37.5239523809524	1.29004761904762
70	38.673	37.5239523809524	1.14904761904762
71	37.948	37.5239523809524	0.42404761904762
72	39.161	41.6092857142857	-2.44828571428571
73	37.408	37.5239523809524	-0.115952380952379
74	37.356	37.5239523809524	-0.167952380952379
75	36.606	37.5239523809524	-0.917952380952379
76	37.04	37.5239523809524	-0.483952380952381
77	36.349	37.5239523809524	-1.17495238095238
78	36.158	37.5239523809524	-1.36595238095238
79	37.342	41.6092857142857	-4.26728571428571
80	36.8	41.663875	-4.863875
81	37.135	37.5239523809524	-0.388952380952382
82	34.265	35.7877647058824	-1.52276470588235
83	33.226	35.7877647058824	-2.56176470588235
84	32.357	35.7877647058824	-3.43076470588235
85	36.87	35.7877647058824	1.08223529411764
86	35.88	35.7877647058824	0.0922352941176499
87	34.808	35.7877647058824	-0.979764705882353
88	34.025	33.7393	0.285699999999999
89	33.901	33.7393	0.161700000000003
90	37.459	35.7877647058824	1.67123529411765
91	37.152	35.7877647058824	1.36423529411765
92	34.929	34.9148461538462	0.0141538461538460
93	34.116	33.7393	0.376700000000000
94	33.71	33.7393	-0.0292999999999992
95	34.264	33.7393	0.524700000000003
96	34.826	35.7877647058824	-0.961764705882352
97	34.096	33.7393	0.356699999999996
98	33.955	33.7393	0.215699999999998
99	34.111	33.7393	0.371699999999997
100	32.344	33.7393	-1.3953
101	32.871	33.7393	-0.868299999999998
102	36.244	35.7877647058824	0.456235294117647
103	35.988	35.7877647058824	0.200235294117647
104	35.439	35.7877647058824	-0.348764705882353
105	35.692	35.7877647058824	-0.0957647058823525
106	35.804	35.7877647058824	0.0162352941176493
107	37.747	35.7877647058824	1.95923529411765
108	40.673	41.663875	-0.990874999999996
109	41.601	41.663875	-0.0628749999999982
110	42.273	41.663875	0.609125000000006
111	41.952	41.663875	0.288125000000001
112	41.463	41.663875	-0.200874999999996
113	42.759	41.663875	1.09512500000000
114	45.434	41.6092857142857	3.82471428571429
115	45.776	47.4929230769231	-1.71692307692307
116	44.63	41.6092857142857	3.02071428571429
117	44.793	41.6092857142857	3.18371428571429
118	44.757	47.4929230769231	-2.73592307692308
119	49.099	47.4929230769231	1.60607692307692
120	47.974	47.4929230769231	0.48107692307692
121	47.919	47.4929230769231	0.42607692307692
122	47.519	47.4929230769231	0.0260769230769213
123	47.136	47.4929230769231	-0.356923076923074
124	45.91	47.4929230769231	-1.58292307692308
125	46.436	47.4929230769231	-1.05692307692308
126	50.334	47.4929230769231	2.84107692307693
127	50.294	47.4929230769231	2.80107692307692
128	47.224	47.4929230769231	-0.268923076923080
129	47.03	47.4929230769231	-0.462923076923076
130	45.79	41.663875	4.126125
131	38.252	37.5239523809524	0.728047619047622

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292852632a1krzwwp3jnlsia/2wslg1292852574.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292852632a1krzwwp3jnlsia/2wslg1292852574.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292852632a1krzwwp3jnlsia/37j221292852574.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292852632a1krzwwp3jnlsia/37j221292852574.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292852632a1krzwwp3jnlsia/4za1m1292852574.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/20/t1292852632a1krzwwp3jnlsia/4za1m1292852574.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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We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

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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