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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 13:14:12 -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/t132380007139m0tq6b99ut2nq.htm/, Retrieved Thu, 02 May 2024 15:10:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154602, Retrieved Thu, 02 May 2024 15:10:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact76

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 18:59:57] [b98453cac15ba1066b407e146608df68]
- R PD  [Recursive Partitioning (Regression Trees)] [RP learning] [2011-12-13 17:59:07] [90a803f646514fc2f7a5d6de952a552a]
-   P     [Recursive Partitioning (Regression Trees)] [RP learning met c...] [2011-12-13 18:09:03] [90a803f646514fc2f7a5d6de952a552a]
-   P         [Recursive Partitioning (Regression Trees)] [Confusion matrix ...] [2011-12-13 18:14:12] [1ec9c877c5c85e817bc4a3ecfdd6e5aa] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	41	38	13	12	14
2	39	32	16	11	18
2	30	35	19	15	11
1	31	33	15	6	12
2	34	37	14	13	16
2	35	29	13	10	18
2	39	31	19	12	14
2	34	36	15	14	14
2	36	35	14	12	15
2	37	38	15	6	15
1	38	31	16	10	17
2	36	34	16	12	19
1	38	35	16	12	10
2	39	38	16	11	16
2	33	37	17	15	18
1	32	33	15	12	14
1	36	32	15	10	14
2	38	38	20	12	17
1	39	38	18	11	14
2	32	32	16	12	16
1	32	33	16	11	18
2	31	31	16	12	11
2	39	38	19	13	14
2	37	39	16	11	12
1	39	32	17	9	17
2	41	32	17	13	9
1	36	35	16	10	16
2	33	37	15	14	14
2	33	33	16	12	15
1	34	33	14	10	11
2	31	28	15	12	16
1	27	32	12	8	13
2	37	31	14	10	17
2	34	37	16	12	15
1	34	30	14	12	14
1	32	33	7	7	16
1	29	31	10	6	9
1	36	33	14	12	15
2	29	31	16	10	17
1	35	33	16	10	13
1	37	32	16	10	15
2	34	33	14	12	16
1	38	32	20	15	16
1	35	33	14	10	12
2	38	28	14	10	12
2	37	35	11	12	11
2	38	39	14	13	15
2	33	34	15	11	15
2	36	38	16	11	17
1	38	32	14	12	13
2	32	38	16	14	16
1	32	30	14	10	14
1	32	33	12	12	11
2	34	38	16	13	12
1	32	32	9	5	12
2	37	32	14	6	15
2	39	34	16	12	16
2	29	34	16	12	15
1	37	36	15	11	12
2	35	34	16	10	12
1	30	28	12	7	8
1	38	34	16	12	13
2	34	35	16	14	11
2	31	35	14	11	14
2	34	31	16	12	15
1	35	37	17	13	10
2	36	35	18	14	11
1	30	27	18	11	12
2	39	40	12	12	15
1	35	37	16	12	15
1	38	36	10	8	14
2	31	38	14	11	16
2	34	39	18	14	15
1	38	41	18	14	15
1	34	27	16	12	13
2	39	30	17	9	12
2	37	37	16	13	17
2	34	31	16	11	13
1	28	31	13	12	15
1	37	27	16	12	13
1	33	36	16	12	15
1	37	38	20	12	16
2	35	37	16	12	15
1	37	33	15	12	16
2	32	34	15	11	15
2	33	31	16	10	14
1	38	39	14	9	15
2	33	34	16	12	14
2	29	32	16	12	13
2	33	33	15	12	7
2	31	36	12	9	17
2	36	32	17	15	13
2	35	41	16	12	15
2	32	28	15	12	14
2	29	30	13	12	13
2	39	36	16	10	16
2	37	35	16	13	12
2	35	31	16	9	14
1	37	34	16	12	17
1	32	36	14	10	15
2	38	36	16	14	17
1	37	35	16	11	12
2	36	37	20	15	16
1	32	28	15	11	11
2	33	39	16	11	15
1	40	32	13	12	9
2	38	35	17	12	16
1	41	39	16	12	15
1	36	35	16	11	10
2	43	42	12	7	10
2	30	34	16	12	15
2	31	33	16	14	11
2	32	41	17	11	13
1	32	33	13	11	14
2	37	34	12	10	18
1	37	32	18	13	16
2	33	40	14	13	14
2	34	40	14	8	14
2	33	35	13	11	14
2	38	36	16	12	14
2	33	37	13	11	12
2	31	27	16	13	14
2	38	39	13	12	15
2	37	38	16	14	15
2	33	31	15	13	15
2	31	33	16	15	13
1	39	32	15	10	17
2	44	39	17	11	17
2	33	36	15	9	19
2	35	33	12	11	15
1	32	33	16	10	13
1	28	32	10	11	9
2	40	37	16	8	15
1	27	30	12	11	15
1	37	38	14	12	15
2	32	29	15	12	16
1	28	22	13	9	11
1	34	35	15	11	14
2	30	35	11	10	11
2	35	34	12	8	15
1	31	35	8	9	13
2	32	34	16	8	15
1	30	34	15	9	16
2	30	35	17	15	14
1	31	23	16	11	15
2	40	31	10	8	16
2	32	27	18	13	16
1	36	36	13	12	11
1	32	31	16	12	12
1	35	32	13	9	9
2	38	39	10	7	16
2	42	37	15	13	13
1	34	38	16	9	16
2	35	39	16	6	12
2	35	34	14	8	9
2	33	31	10	8	13
2	36	32	17	15	13
2	32	37	13	6	14
2	33	36	15	9	19
2	34	32	16	11	13
2	32	35	12	8	12
2	34	36	13	8	13

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154602&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154602&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154602&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 time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






10-Fold Cross Validation
Prediction (training)Prediction (testing)
ActualC1C2CVC1C2CV
C1123160.995113301
C2187410.17982110.0455
Overall--0.8683--0.8645

\begin{tabular}{lllllllll}
\hline
10-Fold Cross Validation \tabularnewline
 & Prediction (training) & Prediction (testing) \tabularnewline
Actual & C1 & C2 & CV & C1 & C2 & CV \tabularnewline
C1 & 1231 & 6 & 0.9951 & 133 & 0 & 1 \tabularnewline
C2 & 187 & 41 & 0.1798 & 21 & 1 & 0.0455 \tabularnewline
Overall & - & - & 0.8683 & - & - & 0.8645 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154602&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]1231[/C][C]6[/C][C]0.9951[/C][C]133[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]C2[/C][C]187[/C][C]41[/C][C]0.1798[/C][C]21[/C][C]1[/C][C]0.0455[/C][/ROW]
[ROW][C]Overall[/C][C]-[/C][C]-[/C][C]0.8683[/C][C]-[/C][C]-[/C][C]0.8645[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154602&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154602&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
C1123160.995113301
C2187410.17982110.0455
Overall--0.8683--0.8645






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C11361
C2187

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 136 & 1 \tabularnewline
C2 & 18 & 7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154602&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]136[/C][C]1[/C][/ROW]
[ROW][C]C2[/C][C]18[/C][C]7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154602&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154602&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
C11361
C2187


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = quantiles ; par3 = 2 ; par4 = yes ;
Parameters (R input):
par1 = 4 ; 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')
}