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*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, 14 Dec 2010 00:35:45 +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/14/t1292286936xq98p5kz7ft1h9l.htm/, Retrieved Tue, 14 Dec 2010 01:35:37 +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/14/t1292286936xq98p5kz7ft1h9l.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 «

1579 0 4,0 45,7 2146 0 5,9 81,9 2462 0 7,1 56,8 3695 0 10,5 65,1 4831 0 15,1 86,2 5134 0 16,8 35,1 6250 0 15,3 133,8 5760 0 18,4 34,5 6249 0 16,1 69,9 2917 0 11,3 98,3 1741 0 7,9 86,7 2359 0 5,6 58,2 1511 1 3,4 83,6 2059 0 4,8 83,5 2635 0 6,5 112,3 2867 0 8,5 134,3 4403 0 15,1 30,0 5720 0 15,7 44,5 4502 0 18,7 120,1 5749 0 19,2 43,4 5627 0 12,9 199,4 2846 0 14,4 68,1 1762 0 6,2 99,8 2429 0 3,3 69,5 1169 0 4,6 71,3 2154 1 7,2 167,8 2249 0 7,8 66,3 2687 0 9,9 41,9 4359 0 13,6 57,2 5382 0 17,1 72,3 4459 0 17,8 96,5 6398 0 18,6 172,1 4596 0 14,7 25,8 3024 0 10,5 105,1 1887 0 8,6 92,2 2070 0 4,4 109,3 1351 0 2,3 101,7 2218 0 2,8 29,1 2461 1 8,8 34,6 3028 0 10,7 46,7 4784 0 13,9 82,0 4975 0 19,3 34,4 4607 0 19,5 72,7 6249 0 20,4 44,4 4809 0 15,3 31,0 3157 0 7,9 64,0 1910 0 8,3 65,4 2228 0 4,5 64,5 1594 0 3,2 153,8 2467 0 5,0 48,8 2222 0 6,6 25,0 3607 1 11,1 37,2 4685 0 12,8 40,8 4962 0 16,3 78,4 5770 0 17,4 112,4 5480 0 18,9 122,7 5000 0 15,8 82,9 3228 0 11,7 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	4 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Goodness of Fit
Correlation	0.9294
R-squared	0.8638
RMSE	596.4812

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	1579	2093.64705882353	-514.647058823529
2	2146	2093.64705882353	52.3529411764707
3	2462	2093.64705882353	368.352941176471
4	3695	3188.86666666667	506.133333333333
5	4831	4414.70588235294	416.294117647059
6	5134	5554.37837837838	-420.378378378378
7	6250	5554.37837837838	695.621621621622
8	5760	5554.37837837838	205.621621621622
9	6249	5554.37837837838	694.621621621622
10	2917	3188.86666666667	-271.866666666667
11	1741	2093.64705882353	-352.647058823529
12	2359	2093.64705882353	265.352941176471
13	1511	2093.64705882353	-582.647058823529
14	2059	2093.64705882353	-34.6470588235293
15	2635	2093.64705882353	541.352941176471
16	2867	2093.64705882353	773.352941176471
17	4403	4414.70588235294	-11.7058823529414
18	5720	5554.37837837838	165.621621621622
19	4502	5554.37837837838	-1052.37837837838
20	5749	5554.37837837838	194.621621621622
21	5627	4414.70588235294	1212.29411764706
22	2846	4414.70588235294	-1568.70588235294
23	1762	2093.64705882353	-331.647058823529
24	2429	2093.64705882353	335.352941176471
25	1169	2093.64705882353	-924.647058823529
26	2154	2093.64705882353	60.3529411764707
27	2249	2093.64705882353	155.352941176471
28	2687	3188.86666666667	-501.866666666667
29	4359	4414.70588235294	-55.7058823529414
30	5382	5554.37837837838	-172.378378378378
31	4459	5554.37837837838	-1095.37837837838
32	6398	5554.37837837838	843.621621621622
33	4596	4414.70588235294	181.294117647059
34	3024	3188.86666666667	-164.866666666667
35	1887	2093.64705882353	-206.647058823529
36	2070	2093.64705882353	-23.6470588235293
37	1351	2093.64705882353	-742.647058823529
38	2218	2093.64705882353	124.352941176471
39	2461	2093.64705882353	367.352941176471
40	3028	3188.86666666667	-160.866666666667
41	4784	4414.70588235294	369.294117647059
42	4975	5554.37837837838	-579.378378378378
43	4607	5554.37837837838	-947.378378378378
44	6249	5554.37837837838	694.621621621622
45	4809	5554.37837837838	-745.378378378378
46	3157	2093.64705882353	1063.35294117647
47	1910	2093.64705882353	-183.647058823529
48	2228	2093.64705882353	134.352941176471
49	1594	2093.64705882353	-499.647058823529
50	2467	2093.64705882353	373.352941176471
51	2222	2093.64705882353	128.352941176471
52	3607	3188.86666666667	418.133333333333
53	4685	4414.70588235294	270.294117647059
54	4962	5554.37837837838	-592.378378378378
55	5770	5554.37837837838	215.621621621622
56	5480	5554.37837837838	-74.3783783783783
57	5000	5554.37837837838	-554.378378378378
58	3228	3188.86666666667	39.1333333333332
59	1993	2093.64705882353	-100.647058823529
60	2288	2093.64705882353	194.352941176471
61	1580	2093.64705882353	-513.647058823529
62	2111	2093.64705882353	17.3529411764707
63	2192	2093.64705882353	98.3529411764707
64	3601	3188.86666666667	412.133333333333
65	4665	4414.70588235294	250.294117647059
66	4876	5554.37837837838	-678.378378378378
67	5813	5554.37837837838	258.621621621622
68	5589	5554.37837837838	34.6216216216217
69	5331	5554.37837837838	-223.378378378378
70	3075	4414.70588235294	-1339.70588235294
71	2002	2093.64705882353	-91.6470588235293
72	2306	2093.64705882353	212.352941176471
73	1507	2093.64705882353	-586.647058823529
74	1992	2093.64705882353	-101.647058823529
75	2487	2093.64705882353	393.352941176471
76	3490	3188.86666666667	301.133333333333
77	4647	4414.70588235294	232.294117647059
78	5594	5554.37837837838	39.6216216216217
79	5611	5554.37837837838	56.6216216216217
80	5788	5554.37837837838	233.621621621622
81	6204	5554.37837837838	649.621621621622
82	3013	4414.70588235294	-1401.70588235294
83	1931	2093.64705882353	-162.647058823529
84	2549	2093.64705882353	455.352941176471
85	1504	2093.64705882353	-589.647058823529
86	2090	2093.64705882353	-3.64705882352928
87	2702	2093.64705882353	608.352941176471
88	2939	4414.70588235294	-1475.70588235294
89	4500	4414.70588235294	85.2941176470586
90	6208	5554.37837837838	653.621621621622
91	6415	5554.37837837838	860.621621621622
92	5657	5554.37837837838	102.621621621622
93	5964	4414.70588235294	1549.29411764706
94	3163	3188.86666666667	-25.8666666666668
95	1997	2093.64705882353	-96.6470588235293
96	2422	2093.64705882353	328.352941176471
97	1376	2093.64705882353	-717.647058823529
98	2202	2093.64705882353	108.352941176471
99	2683	2093.64705882353	589.352941176471
100	3303	3188.86666666667	114.133333333333
101	5202	5554.37837837838	-352.378378378378
102	5231	5554.37837837838	-323.378378378378
103	4880	5554.37837837838	-674.378378378378
104	7998	5554.37837837838	2443.62162162162
105	4977	4414.70588235294	562.294117647059
106	3531	3188.86666666667	342.133333333333
107	2025	2093.64705882353	-68.6470588235293
108	2205	2093.64705882353	111.352941176471
109	1442	2093.64705882353	-651.647058823529
110	2238	2093.64705882353	144.352941176471
111	2179	2093.64705882353	85.3529411764707
112	3218	3188.86666666667	29.1333333333332
113	5139	4414.70588235294	724.294117647059
114	4990	5554.37837837838	-564.378378378378
115	4914	5554.37837837838	-640.378378378378
116	6084	5554.37837837838	529.621621621622
117	5672	5554.37837837838	117.621621621622
118	3548	3188.86666666667	359.133333333333
119	1793	3188.86666666667	-1395.86666666667
120	2086	2093.64705882353	-7.64705882352928

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292286936xq98p5kz7ft1h9l/2c56z1292286939.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292286936xq98p5kz7ft1h9l/2c56z1292286939.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292286936xq98p5kz7ft1h9l/3c56z1292286939.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292286936xq98p5kz7ft1h9l/3c56z1292286939.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292286936xq98p5kz7ft1h9l/44wn21292286939.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292286936xq98p5kz7ft1h9l/44wn21292286939.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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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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