FreeStatistics.org

Free Statistics

of Irreproducible Research!

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, 10 Dec 2011 14:56:39 -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/10/t13235470122a5ckjbveb8z99p.htm/, Retrieved Sat, 04 May 2024 23:13:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=153631, Retrieved Sat, 04 May 2024 23:13:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact104

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]
- R PD    [Recursive Partitioning (Regression Trees)] [ws 10- happiness] [2011-12-10 19:56:39] [2489a3445a7d2af96337a363cd642931] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	0	32	31	13	12	15
1	1	33	34	8	8	11
1	0	38	27	14	12	12
1	0	34	24	14	11	9
1	1	41	34	13	11	14
1	1	39	35	16	13	16
1	1	35	27	14	11	15
1	1	34	30	13	10	16
1	0	47	31	15	7	7
1	1	32	31	13	10	13
1	0	28	28	16	12	15
1	1	44	48	20	15	20
1	1	40	40	17	12	16
1	1	29	31	15	12	16
1	0	30	27	16	12	15
1	1	41	37	16	10	15
1	1	32	29	12	10	17
1	0	33	34	9	8	12
1	0	33	33	15	11	15
1	0	40	37	17	14	13
1	1	38	35	12	12	9
0	1	37	34	10	11	14
1	0	41	35	11	6	16
0	0	32	33	16	12	9
1	1	29	29	16	14	14
1	0	38	31	15	11	14
0	1	35	37	13	8	15
1	0	40	31	14	12	14
1	1	43	40	19	15	17
1	1	31	41	16	13	15
1	0	34	29	17	11	12
1	1	26	34	10	12	16
1	1	28	41	15	7	14
1	1	31	34	14	11	14
0	1	32	36	14	7	14
0	0	29	30	16	12	15
1	1	32	36	17	12	15
1	1	35	31	15	12	16
1	0	31	35	17	13	14
1	0	37	35	14	12	14
1	1	34	33	10	9	17
1	0	35	31	14	9	10
1	0	36	31	16	11	10
1	0	45	35	18	14	12
1	1	39	35	15	12	16
1	1	32	28	16	15	14
1	1	39	27	16	12	17
1	1	34	33	10	6	12
1	0	34	33	8	5	16
0	1	34	35	17	13	15
1	1	37	30	14	11	14
1	1	27	29	12	11	15
1	0	43	30	10	6	14
1	1	40	42	14	12	16
1	1	40	36	12	10	16
1	1	35	36	16	6	17
1	1	37	33	16	12	15
1	1	39	34	15	14	15
1	0	26	33	11	6	6
0	1	29	30	16	11	14
1	0	34	25	8	6	12
1	1	32	40	17	14	10
1	1	38	36	16	12	12
0	1	39	33	15	12	14
1	0	27	35	8	8	18
0	1	40	25	13	10	12
0	1	37	39	14	11	15
0	1	34	32	13	7	8
1	1	36	34	16	12	11
0	0	34	38	12	9	16
0	1	36	29	19	13	14
1	1	32	39	19	14	16
1	1	43	36	12	6	7
1	1	47	32	14	12	16
0	0	24	38	15	6	9
1	1	40	39	13	14	8
1	0	33	32	16	12	15
0	0	38	31	10	10	10
0	1	33	31	15	10	12
0	1	36	30	16	12	11
1	1	39	44	15	11	14
1	0	37	28	11	10	18
1	0	38	36	9	7	12
1	1	36	30	16	12	17
0	1	30	31	12	12	16
1	0	36	32	14	12	11
1	1	41	32	14	10	9
1	1	32	35	13	10	18
0	0	35	33	15	12	14
1	0	41	32	17	12	13
1	0	36	32	14	12	16
1	0	34	27	9	9	10
0	0	35	28	11	8	13
0	1	36	36	9	10	16
1	0	43	35	7	5	9
1	1	36	27	13	10	12
1	0	36	34	15	10	10
0	1	34	31	12	12	16
1	0	36	33	15	11	12
0	0	32	32	14	9	16
0	1	27	33	15	15	15
0	0	32	35	9	8	8
1	1	41	31	16	12	17
1	1	40	33	16	12	13
1	1	30	30	14	10	16
0	0	37	28	14	11	13
0	0	35	31	13	10	15
0	1	39	31	14	11	13
0	0	35	30	16	12	16
0	1	27	38	16	11	14
0	1	37	35	13	10	18
0	0	37	28	12	9	10
0	0	38	37	16	9	13
0	1	38	36	16	11	14
0	1	41	34	16	12	18
0	0	38	27	10	7	9
0	0	39	29	14	12	15
0	0	31	30	12	11	15
0	0	39	35	12	12	11
0	1	32	32	12	6	17
0	1	35	32	12	9	10
0	1	45	39	19	15	13
0	0	29	27	14	10	14
0	1	26	34	13	11	16
0	1	35	31	17	14	17
0	0	40	30	16	12	16
0	1	39	36	15	12	16
0	1	35	35	12	12	13
0	1	34	33	8	11	14
0	1	35	36	10	9	13
0	1	33	36	16	11	16
0	0	37	28	10	6	7
0	0	35	31	16	12	15
0	1	38	33	10	12	14
0	1	35	42	18	14	12
0	1	29	35	12	8	7
0	0	0	5	16	10	14
0	0	30	28	10	9	15
0	0	32	31	15	9	10
0	1	43	41	17	11	17
0	0	37	27	14	10	12
0	0	33	32	12	9	13
0	0	41	30	11	10	13
0	0	39	30	15	12	12
0	1	39	33	7	11	11

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'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153631&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153631&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153631&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'Gwilym Jenkins' @ jenkins.wessa.net






10-Fold Cross Validation
Prediction (training)Prediction (testing)
ActualC1C2CVC1C2CV
C12702610.508516330.3265
C22025970.747220510.7183
Overall--0.6519--0.5583

\begin{tabular}{lllllllll}
\hline
10-Fold Cross Validation \tabularnewline
 & Prediction (training) & Prediction (testing) \tabularnewline
Actual & C1 & C2 & CV & C1 & C2 & CV \tabularnewline
C1 & 270 & 261 & 0.5085 & 16 & 33 & 0.3265 \tabularnewline
C2 & 202 & 597 & 0.7472 & 20 & 51 & 0.7183 \tabularnewline
Overall & - & - & 0.6519 & - & - & 0.5583 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153631&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]270[/C][C]261[/C][C]0.5085[/C][C]16[/C][C]33[/C][C]0.3265[/C][/ROW]
[ROW][C]C2[/C][C]202[/C][C]597[/C][C]0.7472[/C][C]20[/C][C]51[/C][C]0.7183[/C][/ROW]
[ROW][C]Overall[/C][C]-[/C][C]-[/C][C]0.6519[/C][C]-[/C][C]-[/C][C]0.5583[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153631&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153631&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
C12702610.508516330.3265
C22025970.747220510.7183
Overall--0.6519--0.5583






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C13424
C22859

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 34 & 24 \tabularnewline
C2 & 28 & 59 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153631&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]34[/C][C]24[/C][/ROW]
[ROW][C]C2[/C][C]28[/C][C]59[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153631&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153631&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
C13424
C22859


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 7 ; par2 = equal ; par3 = 2 ; par4 = yes ;
Parameters (R input):
par1 = 7 ; par2 = equal ; 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')
}