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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, 13 Dec 2011 15:38:30 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/13/t1323808733hydwuvg8m5kvw5n.htm/, Retrieved Thu, 02 May 2024 16:49:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154701, Retrieved Thu, 02 May 2024 16:49:56 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact111

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 20:30:15] [b98453cac15ba1066b407e146608df68]
-   PD    [Recursive Partitioning (Regression Trees)] [WS 10 connected r...] [2011-12-13 20:38:30] [cb05b01fd3da20a46af540a30bcf4c06] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	41	38	14	12
2	39	32	18	11
2	30	35	11	14
1	31	33	12	12
2	34	37	16	21
2	35	29	18	12
2	39	31	14	22
2	34	36	14	11
2	36	35	15	10
2	37	38	15	13
1	38	31	17	10
2	36	34	19	8
1	38	35	10	15
2	39	38	16	14
2	33	37	18	10
1	32	33	14	14
1	36	32	14	14
2	38	38	17	11
1	39	38	14	10
2	32	32	16	13
1	32	33	18	7
2	31	31	11	14
2	39	38	14	12
2	37	39	12	14
1	39	32	17	11
2	41	32	9	9
1	36	35	16	11
2	33	37	14	15
2	33	33	15	14
1	34	33	11	13
2	31	28	16	9
1	27	32	13	15
2	37	31	17	10
2	34	37	15	11
1	34	30	14	13
1	32	33	16	8
1	29	31	9	20
1	36	33	15	12
2	29	31	17	10
1	35	33	13	10
1	37	32	15	9
2	34	33	16	14
1	38	32	16	8
1	35	33	12	14
2	38	28	12	11
2	37	35	11	13
2	38	39	15	9
2	33	34	15	11
2	36	38	17	15
1	38	32	13	11
2	32	38	16	10
1	32	30	14	14
1	32	33	11	18
2	34	38	12	14
1	32	32	12	11
2	37	32	15	12
2	39	34	16	13
2	29	34	15	9
1	37	36	12	10
2	35	34	12	15
1	30	28	8	20
1	38	34	13	12
2	34	35	11	12
2	31	35	14	14
2	34	31	15	13
1	35	37	10	11
2	36	35	11	17
1	30	27	12	12
2	39	40	15	13
1	35	37	15	14
1	38	36	14	13
2	31	38	16	15
2	34	39	15	13
1	38	41	15	10
1	34	27	13	11
2	39	30	12	19
2	37	37	17	13
2	34	31	13	17
1	28	31	15	13
1	37	27	13	9
1	33	36	15	11
1	37	38	16	10
2	35	37	15	9
1	37	33	16	12
2	32	34	15	12
2	33	31	14	13
1	38	39	15	13
2	33	34	14	12
2	29	32	13	15
2	33	33	7	22
2	31	36	17	13
2	36	32	13	15
2	35	41	15	13
2	32	28	14	15
2	29	30	13	10
2	39	36	16	11
2	37	35	12	16
2	35	31	14	11
1	37	34	17	11
1	32	36	15	10
2	38	36	17	10
1	37	35	12	16
2	36	37	16	12
1	32	28	11	11
2	33	39	15	16
1	40	32	9	19
2	38	35	16	11
1	41	39	15	16
1	36	35	10	15
2	43	42	10	24
2	30	34	15	14
2	31	33	11	15
2	32	41	13	11
1	32	33	14	15
2	37	34	18	12
1	37	32	16	10
2	33	40	14	14
2	34	40	14	13
2	33	35	14	9
2	38	36	14	15
2	33	37	12	15
2	31	27	14	14
2	38	39	15	11
2	37	38	15	8
2	33	31	15	11
2	31	33	13	11
1	39	32	17	8
2	44	39	17	10
2	33	36	19	11
2	35	33	15	13
1	32	33	13	11
1	28	32	9	20
2	40	37	15	10
1	27	30	15	15
1	37	38	15	12
2	32	29	16	14
1	28	22	11	23
1	34	35	14	14
2	30	35	11	16
2	35	34	15	11
1	31	35	13	12
2	32	34	15	10
1	30	34	16	14
2	30	35	14	12
1	31	23	15	12
2	40	31	16	11
2	32	27	16	12
1	36	36	11	13
1	32	31	12	11
1	35	32	9	19
2	38	39	16	12
2	42	37	13	17
1	34	38	16	9
2	35	39	12	12
2	35	34	9	19
2	33	31	13	18
2	36	32	13	15
2	32	37	14	14
2	33	36	19	11
2	34	32	13	9
2	32	35	12	18
2	34	36	13	16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154701&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154701&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154701&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 time3 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






10-Fold Cross Validation
Prediction (training)Prediction (testing)
ActualC1C2CVC1C2CV
C12235130.30327570.3214
C2756400.895115700.8235
Overall--0.5948--0.574

\begin{tabular}{lllllllll}
\hline
10-Fold Cross Validation \tabularnewline
 & Prediction (training) & Prediction (testing) \tabularnewline
Actual & C1 & C2 & CV & C1 & C2 & CV \tabularnewline
C1 & 223 & 513 & 0.303 & 27 & 57 & 0.3214 \tabularnewline
C2 & 75 & 640 & 0.8951 & 15 & 70 & 0.8235 \tabularnewline
Overall & - & - & 0.5948 & - & - & 0.574 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154701&T=1

[TABLE]
[ROW][C]10-Fold Cross Validation[/C][/ROW]
[ROW][C][/C][C]Prediction (training)[/C][C]Prediction (testing)[/C][/ROW]
[ROW][C]Actual[/C][C]C1[/C][C]C2[/C][C]CV[/C][C]C1[/C][C]C2[/C][C]CV[/C][/ROW]
[ROW][C]C1[/C][C]223[/C][C]513[/C][C]0.303[/C][C]27[/C][C]57[/C][C]0.3214[/C][/ROW]
[ROW][C]C2[/C][C]75[/C][C]640[/C][C]0.8951[/C][C]15[/C][C]70[/C][C]0.8235[/C][/ROW]
[ROW][C]Overall[/C][C]-[/C][C]-[/C][C]0.5948[/C][C]-[/C][C]-[/C][C]0.574[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154701&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154701&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:

10-Fold Cross Validation
Prediction (training)Prediction (testing)
ActualC1C2CVC1C2CV
C12235130.30327570.3214
C2756400.895115700.8235
Overall--0.5948--0.574






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C12557
C2971

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 25 & 57 \tabularnewline
C2 & 9 & 71 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154701&T=2

[TABLE]
[ROW][C]Confusion Matrix (predicted in columns / actuals in rows)[/C][/ROW]
[ROW][C][/C][C]C1[/C][C]C2[/C][/ROW]
[ROW][C]C1[/C][C]25[/C][C]57[/C][/ROW]
[ROW][C]C2[/C][C]9[/C][C]71[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154701&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154701&T=2

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)
C1C2
C12557
C2971


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
Parameters (R input):
par1 = 2 ; par2 = quantiles ; par3 = 2 ; par4 = yes ;
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')
}