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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 computationSat, 25 Dec 2010 21:55:07 +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/25/t1293313981fmjlnh8c6ee4abh.htm/, Retrieved Sun, 28 Apr 2024 19:18:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=115469, Retrieved Sun, 28 Apr 2024 19:18:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact188

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]
-   PD  [Recursive Partitioning (Regression Trees)] [WS 10 - recursive...] [2010-12-11 16:07:41] [033eb2749a430605d9b2be7c4aac4a0c]
-   P     [Recursive Partitioning (Regression Trees)] [WS 10 - recursive...] [2010-12-11 16:27:23] [033eb2749a430605d9b2be7c4aac4a0c]
-   P       [Recursive Partitioning (Regression Trees)] [WS 10 - recursive...] [2010-12-11 20:29:07] [033eb2749a430605d9b2be7c4aac4a0c]
-             [Recursive Partitioning (Regression Trees)] [] [2010-12-13 18:32:34] [d7b28a0391ab3b2ddc9f9fba95a43f33]
-                 [Recursive Partitioning (Regression Trees)] [] [2010-12-25 21:55:07] [4dbe485270073769796ed1462cddce37] [Current]
- R                 [Recursive Partitioning (Regression Trees)] [WS10.7] [2011-12-12 23:05:27] [74be16979710d4c4e7c6647856088456]
-                   [Recursive Partitioning (Regression Trees)] [] [2011-12-14 21:16:12] [7d86e24de0a0f8503ecffdef58e8c96c]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	24	14	11	12	24	26
2	25	11	7	8	25	23
2	17	6	17	8	30	25
1	18	12	10	8	19	23
2	18	8	12	9	22	19
2	16	10	12	7	22	29
2	20	10	11	4	25	25
2	16	11	11	11	23	21
2	18	16	12	7	17	22
2	17	11	13	7	21	25
1	23	13	14	12	19	24
2	30	12	16	10	19	18
1	23	8	11	10	15	22
2	18	12	10	8	16	15
2	15	11	11	8	23	22
1	12	4	15	4	27	28
1	21	9	9	9	22	20
2	15	8	11	8	14	12
1	20	8	17	7	22	24
2	31	14	17	11	23	20
1	27	15	11	9	23	21
2	34	16	18	11	21	20
2	21	9	14	13	19	21
2	31	14	10	8	18	23
1	19	11	11	8	20	28
2	16	8	15	9	23	24
1	20	9	15	6	25	24
2	21	9	13	9	19	24
2	22	9	16	9	24	23
1	17	9	13	6	22	23
2	24	10	9	6	25	29
1	25	16	18	16	26	24
2	26	11	18	5	29	18
2	25	8	12	7	32	25
1	17	9	17	9	25	21
1	32	16	9	6	29	26
1	33	11	9	6	28	22
1	13	16	12	5	17	22
2	32	12	18	12	28	22
1	25	12	12	7	29	23
1	29	14	18	10	26	30
2	22	9	14	9	25	23
1	18	10	15	8	14	17
1	17	9	16	5	25	23
2	20	10	10	8	26	23
2	15	12	11	8	20	25
2	20	14	14	10	18	24
2	33	14	9	6	32	24
2	29	10	12	8	25	23
1	23	14	17	7	25	21
2	26	16	5	4	23	24
1	18	9	12	8	21	24
1	20	10	12	8	20	28
2	11	6	6	4	15	16
1	28	8	24	20	30	20
2	26	13	12	8	24	29
2	22	10	12	8	26	27
2	17	8	14	6	24	22
1	12	7	7	4	22	28
2	14	15	13	8	14	16
1	17	9	12	9	24	25
1	21	10	13	6	24	24
2	19	12	14	7	24	28
2	18	13	8	9	24	24
2	10	10	11	5	19	23
1	29	11	9	5	31	30
2	31	8	11	8	22	24
1	19	9	13	8	27	21
2	9	13	10	6	19	25
1	20	11	11	8	25	25
1	28	8	12	7	20	22
2	19	9	9	7	21	23
2	30	9	15	9	27	26
1	29	15	18	11	23	23
1	26	9	15	6	25	25
2	23	10	12	8	20	21
2	13	14	13	6	21	25
2	21	12	14	9	22	24
1	19	12	10	8	23	29
1	28	11	13	6	25	22
1	23	14	13	10	25	27
1	18	6	11	8	17	26
2	21	12	13	8	19	22
1	20	8	16	10	25	24
2	23	14	8	5	19	27
2	21	11	16	7	20	24
1	21	10	11	5	26	24
2	15	14	9	8	23	29
2	28	12	16	14	27	22
2	19	10	12	7	17	21
2	26	14	14	8	17	24
2	10	5	8	6	19	24
2	16	11	9	5	17	23
2	22	10	15	6	22	20
2	19	9	11	10	21	27
2	31	10	21	12	32	26
2	31	16	14	9	21	25
2	29	13	18	12	21	21
1	19	9	12	7	18	21
1	22	10	13	8	18	19
2	23	10	15	10	23	21
1	15	7	12	6	19	21
2	20	9	19	10	20	16
1	18	8	15	10	21	22
2	23	14	11	10	20	29
1	25	14	11	5	17	15
2	21	8	10	7	18	17
1	24	9	13	10	19	15
1	25	14	15	11	22	21
2	17	14	12	6	15	21
2	13	8	12	7	14	19
2	28	8	16	12	18	24
2	21	8	9	11	24	20
1	25	7	18	11	35	17
2	9	6	8	11	29	23
1	16	8	13	5	21	24
2	19	6	17	8	25	14
2	17	11	9	6	20	19
2	25	14	15	9	22	24
2	20	11	8	4	13	13
2	29	11	7	4	26	22
2	14	11	12	7	17	16
2	22	14	14	11	25	19
2	15	8	6	6	20	25
2	19	20	8	7	19	25
2	20	11	17	8	21	23
1	15	8	10	4	22	24
2	20	11	11	8	24	26
2	18	10	14	9	21	26
2	33	14	11	8	26	25
1	22	11	13	11	24	18
1	16	9	12	8	16	21
2	17	9	11	5	23	26
1	16	8	9	4	18	23
1	21	10	12	8	16	23
2	26	13	20	10	26	22
1	18	13	12	6	19	20
1	18	12	13	9	21	13
2	17	8	12	9	21	24
2	22	13	12	13	22	15
1	30	14	9	9	23	14
2	30	12	15	10	29	22
1	24	14	24	20	21	10
2	21	15	7	5	21	24
1	21	13	17	11	23	22
2	29	16	11	6	27	24
2	31	9	17	9	25	19
1	20	9	11	7	21	20
1	16	9	12	9	10	13
1	22	8	14	10	20	20
2	20	7	11	9	26	22
2	28	16	16	8	24	24
1	38	11	21	7	29	29
2	22	9	14	6	19	12
2	20	11	20	13	24	20
2	17	9	13	6	19	21
2	28	14	11	8	24	24
2	22	13	15	10	22	22
2	31	16	19	16	17	20

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'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115469&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115469&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115469&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






10-Fold Cross Validation
Prediction (training)Prediction (testing)
ActualC1C2CVC1C2CV
C1724940.885157150.7917
C22403760.610438460.5476
Overall--0.7671--0.6603

\begin{tabular}{lllllllll}
\hline
10-Fold Cross Validation \tabularnewline
 & Prediction (training) & Prediction (testing) \tabularnewline
Actual & C1 & C2 & CV & C1 & C2 & CV \tabularnewline
C1 & 724 & 94 & 0.8851 & 57 & 15 & 0.7917 \tabularnewline
C2 & 240 & 376 & 0.6104 & 38 & 46 & 0.5476 \tabularnewline
Overall & - & - & 0.7671 & - & - & 0.6603 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115469&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]724[/C][C]94[/C][C]0.8851[/C][C]57[/C][C]15[/C][C]0.7917[/C][/ROW]
[ROW][C]C2[/C][C]240[/C][C]376[/C][C]0.6104[/C][C]38[/C][C]46[/C][C]0.5476[/C][/ROW]
[ROW][C]Overall[/C][C]-[/C][C]-[/C][C]0.7671[/C][C]-[/C][C]-[/C][C]0.6603[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115469&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115469&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
C1724940.885157150.7917
C22403760.610438460.5476
Overall--0.7671--0.6603






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C17118
C21951

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 71 & 18 \tabularnewline
C2 & 19 & 51 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115469&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]71[/C][C]18[/C][/ROW]
[ROW][C]C2[/C][C]19[/C][C]51[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115469&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115469&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
C17118
C21951


Figures (Output of Computation)

Input Parameters & R Code

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