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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_regression_trees1.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationThu, 16 Dec 2010 21:20:38 +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/16/t1292534528pvdbw04tqj0grbb.htm/, Retrieved Fri, 03 May 2024 06:31:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=111289, Retrieved Fri, 03 May 2024 06:31:35 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact154

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 20:13:50] [b98453cac15ba1066b407e146608df68]
F   PD  [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-13 19:39:27] [2843717cd92615903379c14ebee3c5df]
-   PD    [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-16 21:06:34] [74be16979710d4c4e7c6647856088456]
-             [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-16 21:20:38] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0	13	26	9	15	25	25
0	16	20	9	15	25	24
0	19	21	9	14	19	21
1	15	31	14	10	18	23
0	14	21	8	10	18	17
0	13	18	8	12	22	19
0	19	26	11	18	29	18
0	15	22	10	12	26	27
0	14	22	9	14	25	23
0	15	29	15	18	23	23
1	16	15	14	9	23	29
0	16	16	11	11	23	21
1	16	24	14	11	24	26
0	17	17	6	17	30	25
1	15	19	20	8	19	25
1	15	22	9	16	24	23
0	20	31	10	21	32	26
1	18	28	8	24	30	20
0	16	38	11	21	29	29
1	16	26	14	14	17	24
0	19	25	11	7	25	23
0	16	25	16	18	26	24
1	17	29	14	18	26	30
0	17	28	11	13	25	22
1	16	15	11	11	23	22
0	15	18	12	13	21	13
1	14	21	9	13	19	24
0	15	25	7	18	35	17
1	12	23	13	14	19	24
0	14	23	10	12	20	21
0	16	19	9	9	21	23
1	14	18	9	12	21	24
1	10	26	16	5	23	24
1	14	18	12	10	19	23
0	16	18	6	11	17	26
1	16	28	14	11	24	24
1	16	17	14	12	15	21
0	14	29	10	12	25	23
1	20	12	4	15	27	28
1	14	25	12	12	29	23
0	14	28	12	16	27	22
0	11	20	14	14	18	24
0	15	17	9	17	25	21
0	16	17	9	13	22	23
1	14	20	10	10	26	23
0	16	31	14	17	23	20
1	14	21	10	12	16	23
1	12	19	9	13	27	21
0	16	23	14	13	25	27
1	9	15	8	11	14	12
0	14	24	9	13	19	15
0	16	28	8	12	20	22
0	16	16	9	12	16	21
1	15	19	9	12	18	21
0	16	21	9	9	22	20
1	12	21	15	7	21	24
1	16	20	8	17	22	24
0	16	16	10	12	22	29
0	14	25	8	12	32	25
0	16	30	14	9	23	14
1	17	29	11	9	31	30
0	18	22	10	13	18	19
1	18	19	12	10	23	29
0	12	33	14	11	26	25
1	16	17	9	12	24	25
1	10	9	13	10	19	25
0	14	14	15	13	14	16
0	18	15	8	6	20	25
1	18	12	7	7	22	28
1	16	21	10	13	24	24
0	16	20	10	11	25	25
0	16	29	13	18	21	21
1	13	33	11	9	28	22
1	16	21	8	9	24	20
1	16	15	12	11	20	25
1	20	19	9	11	21	27
0	16	23	10	15	23	21
1	15	20	11	8	13	13
0	15	20	11	11	24	26
0	16	18	10	14	21	26
1	14	31	16	14	21	25
0	15	18	16	12	17	22
0	12	13	8	12	14	19
0	17	9	6	8	29	23
0	16	20	11	11	25	25
0	15	18	12	10	16	15
0	13	23	14	17	25	21
0	16	17	9	16	25	23
0	16	17	11	13	21	25
0	16	16	8	15	23	24
1	16	31	8	11	22	24
1	14	15	7	12	19	21
0	16	28	16	16	24	24
1	16	26	13	20	26	22
0	20	20	8	16	25	24
1	15	19	11	11	20	28
0	16	25	14	15	22	21
1	13	18	10	15	14	17
0	17	20	10	12	20	28
1	16	33	14	9	32	24
0	12	24	14	24	21	10
0	16	22	10	15	22	20
0	16	32	12	18	28	22
0	17	31	9	17	25	19
1	13	13	16	12	17	22
0	12	18	8	15	21	22
1	18	17	9	11	23	26
0	14	29	16	11	27	24
0	14	22	13	15	22	22
0	13	18	13	12	19	20
0	16	22	8	14	20	20
0	13	25	14	11	17	15
0	16	20	11	20	24	20
0	13	20	9	11	21	20
0	16	17	8	12	21	24
0	15	21	13	17	23	22
0	16	26	13	12	24	29
1	15	10	10	11	19	23
0	17	15	8	10	22	24
0	15	20	7	11	26	22
0	12	14	11	12	17	16
1	16	16	11	9	17	23
1	10	23	14	8	19	27
0	16	11	6	6	15	16
1	14	19	10	12	17	21
0	15	30	9	15	27	26
1	13	21	12	13	19	22
1	15	20	11	17	21	23
0	11	22	14	14	25	19
0	12	30	12	16	19	18
0	16	28	8	16	18	24
1	15	23	14	11	20	29
0	17	23	8	11	15	22
1	16	21	11	16	20	24
0	10	30	12	15	29	22
0	18	22	9	14	19	12
1	13	32	16	9	29	26
0	15	22	11	13	24	18
1	16	15	11	11	23	22
0	16	21	12	14	22	24
0	14	27	15	11	23	21
0	10	22	13	12	22	15
0	17	9	6	8	29	23
0	13	29	11	7	26	22
0	15	20	7	11	26	22
0	16	16	8	13	21	24
0	12	16	8	9	18	23
0	13	16	9	12	10	13

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'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111289&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111289&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111289&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'Gwilym Jenkins' @ 72.249.127.135






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C18014
C22232

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 80 & 14 \tabularnewline
C2 & 22 & 32 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111289&T=1

[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]80[/C][C]14[/C][/ROW]
[ROW][C]C2[/C][C]22[/C][C]32[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111289&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111289&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)
C1C2
C18014
C22232


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = quantiles ; par3 = 2 ; par4 = no ;
Parameters (R input):
par1 = 4 ; par2 = quantiles ; par3 = 2 ; 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')
}