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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationTue, 21 Dec 2010 15:33:41 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/21/t1292945477zmt0kz6tfyqw989.htm/, Retrieved Fri, 17 May 2024 10:29:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113678, Retrieved Fri, 17 May 2024 10:29:33 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact100

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 19:35:21] [b98453cac15ba1066b407e146608df68]
-   PD  [Recursive Partitioning (Regression Trees)] [] [2010-12-14 17:50:05] [8ef75e99f9f5061c72c54640f2f1c3e7]
-   PD      [Recursive Partitioning (Regression Trees)] [] [2010-12-21 15:33:41] [e26438ba7029caa0090c95690001dbf5] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4,031636	0,5215052	9,166456	1,303763
3,702076	0,4248284	7,970589	1,416094
3,056176	0,4250311	7,104091	1,052458
3,280707	0,4771938	6,621064	1,312283
2,984728	0,8280212	7,529215	1,309429
3,693712	0,6156186	8,170938	1,492409
3,226317	0,366627	8,15745	1,026556
2,190349	0,4308883	7,378962	1,005406
2,599515	0,2810287	7,921496	1,334886
3,080288	0,4646245	8,15674	1,393873
2,929672	0,2693951	8,856365	1,128092
2,922548	0,5779049	8,817177	1,122787
3,234943	0,5661151	8,734347	1,213104
2,983081	0,5077584	9,345927	1,253528
3,284389	0,7507175	8,99297	1,094796
3,806511	0,6808395	10,78512	0,9129438
3,784579	0,7661091	8,886867	1,19513
2,645654	0,4561473	8,818847	0,9274994
3,092081	0,4977496	8,823744	0,9653326
3,204859	0,4193273	9,165298	1,198078
3,107225	0,6095514	8,652657	0,966362
3,466909	0,457337	8,173054	0,9736851
2,984404	0,5705478	7,563416	0,9948013
3,218072	0,3478996	7,595809	0,8262616
2,82731	0,3874993	8,381467	0,6888877
3,182049	0,5824285	7,216432	0,7813066
2,236319	0,2391033	6,540178	0,6047907
2,033218	0,2367445	6,238914	1,08624
1,644804	0,2626158	5,487288	0,7740255
1,627971	0,4240934	5,759462	1,026032
1,677559	0,365275	5,993215	0,6764351
2,330828	0,3750758	7,474726	0,830525
2,493615	0,4090056	7,348907	0,7916238
2,257172	0,3891676	7,303379	0,7523907
2,655517	0,240261	7,119314	0,6702018
2,298655	0,1589496	6,99378	0,8803359
2,600402	0,4393373	6,958153	0,9142966
3,04523	0,5094681	7,595706	0,9610421
2,790583	0,3743465	8,088153	0,9301944
3,227052	0,4339828	7,555753	0,8679657
2,967479	0,4130557	7,315433	0,9891596
2,938817	0,3288928	7,893427	0,9972879
3,277961	0,5186648	8,858794	0,7987437
3,423985	0,5486504	8,839367	0,9753785
3,072646	0,5469111	8,014733	0,9347208
2,754253	0,4963494	7,873465	0,9732341
2,910431	0,5308929	8,930377	0,8152998
3,174369	0,5957761	10,50055	0,9402092
3,068387	0,5570584	12,61144	0,794493
3,089543	0,5731325	11,41787	0,9313403
2,906654	0,5005416	11,87249	0,9220503
2,931161	0,5431269	11,06082	0,7845167
3,02566	0,5593657	12,04331	0,8220981
2,939551	0,6911693	9,776299	0,8910255
2,691019	0,4403485	9,557194	0,8073056
3,19812	0,5676662	9,20259	0,9514406
3,07639	0,5969114	10,22402	1,147907
2,863873	0,4735537	9,350807	1,172609
3,013802	0,5923935	8,300913	1,281051
3,053364	0,5975556	8,365779	1,165962
2,864753	0,6334127	8,133595	0,9789106
3,057062	0,6057115	7,66047	1,410951
2,959365	0,7046107	8,074839	1,197838
3,252258	0,4805263	7,848597	1,288368
3,602988	0,702686	7,99822	1,102253
3,497704	0,7009017	7,396895	1,197657
3,296867	0,6030854	7,900419	1,299984
3,602417	0,6980919	8,1005	1,198611
3,3001	0,597656	7,899453	1,299252
3,40193	0,8023421	7,599783	1,097604
3,502591	0,6017109	8,100929	1,39977
3,402348	0,5993127	9,002175	1,398396
3,498551	0,6025625	10,2989	1,40188
3,199823	0,7016625	10,10152	1,699717
2,700064	0,4995714	10,69915	1,39761
2,801034	0,4980918	9,69814	1,500135
2,898628	0,497569	9,800951	1,400136
2,800854	0,600183	10,90047	1,400427
2,399942	0,3339542	10,69785	1,341477
2,402724	0,274437	9,297252	1,33858
2,202331	0,3209428	10,39744	1,482977
2,102594	0,5406671	10,90072	1,163253
1,798293	0,4050209	12,90127	1,328468
1,202484	0,2885961	13,09906	1,23455
1,400201	0,3275942	11,69828	1,484741
1,200832	0,3132606	11,09987	1,336579
1,298083	0,2575562	11,30157	1,339292
1,099742	0,2138386	10,70211	1,405225
1,001377	0,1861856	10,09931	1,333491
0,8361743	0,1592713	9,591119	1,14974

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113678&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113678&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113678&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'George Udny Yule' @ 72.249.76.132






Confusion Matrix (predicted in columns / actuals in rows)
C1C2C3C4
C120003
C250017
C320021
C400022

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 & C3 & C4 \tabularnewline
C1 & 20 & 0 & 0 & 3 \tabularnewline
C2 & 5 & 0 & 0 & 17 \tabularnewline
C3 & 2 & 0 & 0 & 21 \tabularnewline
C4 & 0 & 0 & 0 & 22 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113678&T=1

[TABLE]
[ROW][C]Confusion Matrix (predicted in columns / actuals in rows)[/C][/ROW]
[ROW][C][/C][C]C1[/C][C]C2[/C][C]C3[/C][C]C4[/C][/ROW]
[ROW][C]C1[/C][C]20[/C][C]0[/C][C]0[/C][C]3[/C][/ROW]
[ROW][C]C2[/C][C]5[/C][C]0[/C][C]0[/C][C]17[/C][/ROW]
[ROW][C]C3[/C][C]2[/C][C]0[/C][C]0[/C][C]21[/C][/ROW]
[ROW][C]C4[/C][C]0[/C][C]0[/C][C]0[/C][C]22[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113678&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113678&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Confusion Matrix (predicted in columns / actuals in rows)
C1C2C3C4
C120003
C250017
C320021
C400022


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = quantiles ; par3 = 4 ; par4 = no ;
Parameters (R input):
par1 = 1 ; par2 = quantiles ; par3 = 4 ; 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')
}