Home » date » 2010 » Dec » 24 »

workshop 10 - recursive partitioning 3 (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 19:21:55 +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/t1293218467i0f6t4oqgtwzssf.htm/, Retrieved Fri, 24 Dec 2010 20:21:07 +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/t1293218467i0f6t4oqgtwzssf.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	9 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	0.6109
R-squared	0.3733
RMSE	42100.526

Actuals, Predictions, and Residuals
#	Actuals	Forecasts	Residuals
1	213118	140120.466666667	72997.5333333333
2	81767	140120.466666667	-58353.4666666667
3	153198	140120.466666667	13077.5333333333
4	-26007	42866	-68873
5	126942	140120.466666667	-13178.4666666667
6	157214	140120.466666667	17093.5333333333
7	129352	140120.466666667	-10768.4666666667
8	234817	140120.466666667	94696.5333333333
9	60448	140120.466666667	-79672.4666666667
10	47818	42866	4952
11	245546	140120.466666667	105425.533333333
12	48020	42866	5154
13	-1710	42866	-44576
14	32648	42866	-10218
15	95350	140120.466666667	-44770.4666666667
16	151352	140120.466666667	11231.5333333333
17	288170	140120.466666667	148049.533333333
18	114337	140120.466666667	-25783.4666666667
19	37884	42866	-4982
20	122844	140120.466666667	-17276.4666666667
21	82340	42866	39474
22	79801	105024.411764706	-25223.4117647059
23	165548	140120.466666667	25427.5333333333
24	116384	140120.466666667	-23736.4666666667
25	134028	140120.466666667	-6092.46666666667
26	63838	105024.411764706	-41186.4117647059
27	74996	140120.466666667	-65124.4666666667
28	31080	42866	-11786
29	32168	105024.411764706	-72856.4117647059
30	49857	140120.466666667	-90263.4666666667
31	87161	105024.411764706	-17863.4117647059
32	106113	105024.411764706	1088.58823529411
33	80570	105024.411764706	-24454.4117647059
34	102129	105024.411764706	-2895.41176470589
35	301670	105024.411764706	196645.588235294
36	102313	140120.466666667	-37807.4666666667
37	88577	105024.411764706	-16447.4117647059
38	112477	105024.411764706	7452.58823529411
39	191778	140120.466666667	51657.5333333333
40	79804	42866	36938
41	128294	140120.466666667	-11826.4666666667
42	96448	101063.142857143	-4615.14285714286
43	93811	140120.466666667	-46309.4666666667
44	117520	105024.411764706	12495.5882352941
45	69159	66089.8888888889	3069.11111111111
46	101792	66089.8888888889	35702.1111111111
47	210568	105024.411764706	105543.588235294
48	136996	140120.466666667	-3124.46666666667
49	121920	105024.411764706	16895.5882352941
50	76403	66089.8888888889	10313.1111111111
51	108094	105024.411764706	3069.58823529411
52	134759	140120.466666667	-5361.46666666667
53	188873	140120.466666667	48752.5333333333
54	146216	140120.466666667	6095.53333333333
55	156608	140120.466666667	16487.5333333333
56	61348	105024.411764706	-43676.4117647059
57	50350	105024.411764706	-54674.4117647059
58	87720	105024.411764706	-17304.4117647059
59	99489	101063.142857143	-1574.14285714286
60	87419	105024.411764706	-17605.4117647059
61	94355	105024.411764706	-10669.4117647059
62	60326	66089.8888888889	-5763.88888888889
63	94670	105024.411764706	-10354.4117647059
64	82425	101063.142857143	-18638.1428571429
65	59017	66089.8888888889	-7072.88888888889
66	90829	140120.466666667	-49291.4666666667
67	80791	101063.142857143	-20272.1428571429
68	100423	105024.411764706	-4601.41176470589
69	131116	105024.411764706	26091.5882352941
70	100269	105024.411764706	-4755.41176470589
71	27330	66089.8888888889	-38759.8888888889
72	39039	66089.8888888889	-27050.8888888889
73	106885	66089.8888888889	40795.1111111111
74	79285	66089.8888888889	13195.1111111111
75	118881	105024.411764706	13856.5882352941
76	77623	66089.8888888889	11533.1111111111
77	114768	101063.142857143	13704.8571428571
78	74015	66089.8888888889	7925.11111111111
79	69465	42866	26599
80	117869	140120.466666667	-22251.4666666667
81	60982	66089.8888888889	-5107.88888888889
82	90131	105024.411764706	-14893.4117647059
83	138971	105024.411764706	33946.5882352941
84	39625	66089.8888888889	-26464.8888888889
85	102725	105024.411764706	-2299.41176470589
86	64239	66089.8888888889	-1850.88888888889
87	90262	105024.411764706	-14762.4117647059
88	103960	105024.411764706	-1064.41176470589
89	106611	101063.142857143	5547.85714285714
90	103345	105024.411764706	-1679.41176470589
91	95551	105024.411764706	-9473.41176470589
92	82903	66089.8888888889	16813.1111111111
93	63593	105024.411764706	-41431.4117647059
94	126910	101063.142857143	25846.8571428571
95	37527	66089.8888888889	-28562.8888888889
96	60247	66089.8888888889	-5842.88888888889
97	112995	105024.411764706	7970.58823529411
98	70184	42866	27318
99	130140	105024.411764706	25115.5882352941
100	73221	66089.8888888889	7131.11111111111

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293218467i0f6t4oqgtwzssf/29so31293218503.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293218467i0f6t4oqgtwzssf/29so31293218503.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293218467i0f6t4oqgtwzssf/39so31293218503.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293218467i0f6t4oqgtwzssf/39so31293218503.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293218467i0f6t4oqgtwzssf/4dtmr1293218503.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293218467i0f6t4oqgtwzssf/4dtmr1293218503.ps (open in new window)

Parameters (Session):

par1 = 6 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 6 ; 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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