Home » date » 2010 » Dec » 24 »

workshop 10 - recursive partitioning 5 (jonas poels)

*Unverified author*

R Software Module: /rwasp_regression_trees1.wasp (opens new window with default values)

Title produced by software: Recursive Partitioning (Regression Trees)

Date of computation: Fri, 24 Dec 2010 19:26:46 +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/24/t1293218675628jpcrj1tq1yh4.htm/, Retrieved Fri, 24 Dec 2010 20:24:39 +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/24/t1293218675628jpcrj1tq1yh4.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 «

1 162556 162556 1081 1081 213118 213118 230380558 6282929 1 29790 29790 309 309 81767 81767 25266003 4324047 1 87550 87550 458 458 153198 153198 70164684 4108272 0 84738 0 588 0 -26007 0 -15292116 -1212617 1 54660 54660 299 299 126942 126942 37955658 1485329 1 42634 42634 156 156 157214 157214 24525384 1779876 0 40949 0 481 0 129352 0 62218312 1367203 1 42312 42312 323 323 234817 234817 75845891 2519076 1 37704 37704 452 452 60448 60448 27322496 912684 1 16275 16275 109 109 47818 47818 5212162 1443586 0 25830 0 115 0 245546 0 28237790 1220017 0 12679 0 110 0 48020 0 5282200 984885 1 18014 18014 239 239 -1710 -1710 -408690 1457425 0 43556 0 247 0 32648 0 8064056 -572920 1 24524 24524 497 497 95350 95350 47388950 929144 0 6532 0 103 0 151352 0 15589256 1151176 0 7123 0 109 0 288170 0 31410530 790090 1 20813 20813 502 502 114337 114337 57397174 774497 1 37597 37597 248 248 37884 37884 9395232 990576 0 17821 0 373 0 122844 0 45820812 454195 1 12988 12988 119 119 82340 82340 9798460 876607 1 2 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	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135
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	0.7609
R-squared	0.579
RMSE	564995.4435

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	6282929	2800348.625	3482580.375
2	4324047	2800348.625	1523698.375
3	4108272	2800348.625	1307923.375
4	-1212617	-7838.57142857143	-1204778.42857143
5	1485329	2800348.625	-1315019.625
6	1779876	2800348.625	-1020472.625
7	1367203	597500.333333333	769702.666666667
8	2519076	2800348.625	-281272.625
9	912684	2800348.625	-1887664.625
10	1443586	968870.285714286	474715.714285714
11	1220017	597500.333333333	622516.666666667
12	984885	597500.333333333	387384.666666667
13	1457425	968870.285714286	488554.714285714
14	-572920	-7838.57142857143	-565081.428571429
15	929144	968870.285714286	-39726.2857142857
16	1151176	597500.333333333	553675.666666667
17	790090	597500.333333333	192589.666666667
18	774497	968870.285714286	-194373.285714286
19	990576	2800348.625	-1809772.625
20	454195	597500.333333333	-143305.333333333
21	876607	968870.285714286	-92263.2857142857
22	711969	968870.285714286	-256901.285714286
23	702380	597500.333333333	104879.666666667
24	264449	597500.333333333	-333051.333333333
25	450033	597500.333333333	-147467.333333333
26	541063	597500.333333333	-56437.3333333334
27	588864	968870.285714286	-380006.285714286
28	-37216	-7838.57142857143	-29377.4285714286
29	783310	-7838.57142857143	791148.571428571
30	467359	597500.333333333	-130141.333333333
31	688779	449145.555555556	239633.444444444
32	608419	597500.333333333	10918.6666666666
33	696348	597500.333333333	98847.6666666666
34	597793	449145.555555556	148647.444444444
35	821730	566119.714285714	255610.285714286
36	377934	597500.333333333	-219566.333333333
37	651939	597500.333333333	54438.6666666666
38	697458	449145.555555556	248312.444444444
39	700368	566119.714285714	134248.285714286
40	225986	335014.2	-109028.2
41	348695	597500.333333333	-248805.333333333
42	373683	597500.333333333	-223817.333333333
43	501709	449145.555555556	52563.4444444444
44	413743	597500.333333333	-183757.333333333
45	379825	335014.2	44810.8
46	336260	449145.555555556	-112885.555555556
47	636765	566119.714285714	70645.2857142857
48	481231	597500.333333333	-116269.333333333
49	469107	449145.555555556	19961.4444444444
50	211928	335014.2	-123086.2
51	563925	449145.555555556	114779.444444444
52	511939	566119.714285714	-54180.7142857143
53	521016	597500.333333333	-76484.3333333334
54	543856	566119.714285714	-22263.7142857143
55	329304	566119.714285714	-236815.714285714
56	423262	335014.2	88247.8
57	509665	335014.2	174650.8
58	455881	449145.555555556	6735.44444444444
59	367772	449145.555555556	-81373.5555555556
60	406339	597500.333333333	-191161.333333333
61	493408	449145.555555556	44262.4444444444
62	232942	335014.2	-102072.2
63	416002	449145.555555556	-33143.5555555556
64	337430	597500.333333333	-260070.333333333
65	361517	335014.2	26502.8
66	360962	597500.333333333	-236538.333333333
67	235561	335014.2	-99453.2
68	408247	449145.555555556	-40898.5555555556
69	450296	449145.555555556	1150.44444444444
70	418799	449145.555555556	-30346.5555555556
71	247405	-7838.57142857143	255243.571428571
72	378519	-7838.57142857143	386357.571428571
73	326638	449145.555555556	-122507.555555556
74	328233	335014.2	-6781.20000000001
75	386225	449145.555555556	-62920.5555555556
76	283662	335014.2	-51352.2
77	370225	449145.555555556	-78920.5555555556
78	269236	335014.2	-65778.2
79	365732	335014.2	30717.8
80	420383	449145.555555556	-28762.5555555556
81	345811	335014.2	10796.8000000000
82	431809	449145.555555556	-17336.5555555556
83	418876	566119.714285714	-147243.714285714
84	297476	335014.2	-37538.2
85	416776	449145.555555556	-32369.5555555556
86	357257	335014.2	22242.8
87	458343	449145.555555556	9197.44444444444
88	388386	449145.555555556	-60759.5555555556
89	358934	449145.555555556	-90211.5555555556
90	407560	449145.555555556	-41585.5555555556
91	392558	449145.555555556	-56587.5555555556
92	373177	335014.2	38162.8
93	428370	335014.2	93355.8
94	369419	597500.333333333	-228081.333333333
95	358649	-7838.57142857143	366487.571428571
96	376641	335014.2	41626.8
97	467427	449145.555555556	18281.4444444444
98	364885	335014.2	29870.8
99	436230	449145.555555556	-12915.5555555556
100	329118	335014.2	-5896.20000000001

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293218675628jpcrj1tq1yh4/2egy11293218798.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293218675628jpcrj1tq1yh4/2egy11293218798.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293218675628jpcrj1tq1yh4/3egy11293218798.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293218675628jpcrj1tq1yh4/3egy11293218798.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293218675628jpcrj1tq1yh4/4p7f41293218798.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293218675628jpcrj1tq1yh4/4p7f41293218798.ps (open in new window)

Parameters (Session):

par1 = 9 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 9 ; 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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