Home » date » 2010 » Dec » 24 »

workshop 10 - recursive partitioning 7 (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 19:53:47 +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/t12932203259tbf7atwietnr60.htm/, Retrieved Fri, 24 Dec 2010 20:52:06 +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/24/t12932203259tbf7atwietnr60.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	6 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24
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	0.8743
R-squared	0.7643
RMSE	91.6934

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	1081	608.666666666667	472.333333333333
2	309	320.75	-11.75
3	458	608.666666666667	-150.666666666667
4	588	320.75	267.25
5	299	320.75	-21.75
6	156	320.75	-164.75
7	481	608.666666666667	-127.666666666667
8	323	608.666666666667	-285.666666666667
9	452	320.75	131.25
10	109	113.714285714286	-4.71428571428571
11	115	198.545454545455	-83.5454545454545
12	110	113.714285714286	-3.71428571428571
13	239	113.714285714286	125.285714285714
14	247	320.75	-73.75
15	497	608.666666666667	-111.666666666667
16	103	103.772727272727	-0.772727272727266
17	109	198.545454545455	-89.5454545454545
18	502	608.666666666667	-106.666666666667
19	248	320.75	-72.75
20	373	608.666666666667	-235.666666666667
21	119	103.772727272727	15.2272727272727
22	84	113.714285714286	-29.7142857142857
23	102	103.772727272727	-1.77272727272727
24	295	198.545454545455	96.4545454545455
25	105	103.772727272727	1.22727272727273
26	64	113.714285714286	-49.7142857142857
27	267	320.75	-53.75
28	129	113.714285714286	15.2857142857143
29	37	15.1	21.9
30	361	198.545454545455	162.454545454545
31	28	37.3125	-9.3125
32	85	103.772727272727	-18.7727272727273
33	44	37.3125	6.6875
34	49	61.4705882352941	-12.4705882352941
35	22	61.4705882352941	-39.4705882352941
36	155	103.772727272727	51.2272727272727
37	91	103.772727272727	-12.7727272727273
38	81	103.772727272727	-22.7727272727273
39	79	103.772727272727	-24.7727272727273
40	145	103.772727272727	41.2272727272727
41	816	608.666666666667	207.333333333333
42	61	113.714285714286	-52.7142857142857
43	226	198.545454545455	27.4545454545455
44	105	103.772727272727	1.22727272727273
45	62	61.4705882352941	0.529411764705884
46	24	37.3125	-13.3125
47	26	61.4705882352941	-35.4705882352941
48	322	198.545454545455	123.454545454545
49	84	103.772727272727	-19.7727272727273
50	33	37.3125	-4.3125
51	108	103.772727272727	4.22727272727273
52	150	198.545454545455	-48.5454545454545
53	115	198.545454545455	-83.5454545454545
54	162	198.545454545455	-36.5454545454545
55	158	198.545454545455	-40.5454545454545
56	97	61.4705882352941	35.5294117647059
57	9	15.1	-6.1
58	66	61.4705882352941	4.52941176470588
59	107	103.772727272727	3.22727272727273
60	101	103.772727272727	-2.77272727272727
61	47	61.4705882352941	-14.4705882352941
62	38	37.3125	0.6875
63	34	37.3125	-3.3125
64	84	103.772727272727	-19.7727272727273
65	79	61.4705882352941	17.5294117647059
66	947	608.666666666667	338.333333333333
67	74	61.4705882352941	12.5294117647059
68	53	61.4705882352941	-8.47058823529412
69	94	103.772727272727	-9.77272727272727
70	63	61.4705882352941	1.52941176470588
71	58	37.3125	20.6875
72	49	37.3125	11.6875
73	34	37.3125	-3.3125
74	11	15.1	-4.1
75	35	37.3125	-2.3125
76	17	15.1	1.9
77	47	61.4705882352941	-14.4705882352941
78	43	37.3125	5.6875
79	117	103.772727272727	13.2272727272727
80	171	198.545454545455	-27.5454545454545
81	26	37.3125	-11.3125
82	73	61.4705882352941	11.5294117647059
83	59	103.772727272727	-44.7727272727273
84	18	15.1	2.9
85	15	15.1	-0.0999999999999996
86	72	61.4705882352941	10.5294117647059
87	86	103.772727272727	-17.7727272727273
88	14	15.1	-1.1
89	64	61.4705882352941	2.52941176470588
90	11	15.1	-4.1
91	52	61.4705882352941	-9.47058823529412
92	41	37.3125	3.6875
93	99	61.4705882352941	37.5294117647059
94	75	103.772727272727	-28.7727272727273
95	45	37.3125	7.6875
96	43	37.3125	5.6875
97	8	15.1	-7.1
98	198	103.772727272727	94.2272727272727
99	22	37.3125	-15.3125
100	11	15.1	-4.1

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t12932203259tbf7atwietnr60/2gb3x1293220418.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t12932203259tbf7atwietnr60/2gb3x1293220418.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t12932203259tbf7atwietnr60/3gb3x1293220418.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t12932203259tbf7atwietnr60/3gb3x1293220418.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t12932203259tbf7atwietnr60/4922i1293220418.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t12932203259tbf7atwietnr60/4922i1293220418.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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