FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Module--
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationTue, 13 Dec 2011 06:47:35 -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/t1323776881opnfc5i7fkcga32.htm/, Retrieved Thu, 02 May 2024 20:05:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154362, Retrieved Thu, 02 May 2024 20:05:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact121

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)] [Recursive Partiti...] [2010-12-14 20:32:49] [b8e188bcc949964bed729335b3416734]
-   P     [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-14 20:53:54] [b8e188bcc949964bed729335b3416734]
-   P       [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-14 21:32:40] [b8e188bcc949964bed729335b3416734]
- RM            [Recursive Partitioning (Regression Trees)] [] [2011-12-13 11:47:35] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	1	22	15	16	17	10
1	2	22	23	24	42	9
1	2	22	26	22	39	30
1	2	23	19	21	22	18
1	2	21	19	23	20	16
1	2	21	16	23	31	20
1	1	24	23	21	42	20
2	1	22	22	20	30	18
1	2	21	19	22	33	21
1	2	23	24	20	29	20
1	1	20	19	12	31	20
2	1	23	25	23	39	20
1	1	20	23	23	44	29
1	2	21	31	30	40	14
2	1	22	29	22	42	25
2	2	22	18	21	28	19
2	2	21	17	21	29	19
1	1	20	22	15	35	25
1	1	21	21	22	26	25
1	2	21	24	24	42	19
1	1	20	22	23	26	19
1	1	21	16	15	30	18
1	2	23	22	24	28	24
1	1	23	21	24	24	18
2	1	21	25	21	26	26
1	1	22	22	21	39	26
2	1	20	24	18	33	24
2	1	23	21	20	50	29
2	1	21	25	19	40	26
1	1	21	29	29	49	28
2	1	23	19	20	31	18
2	1	23	29	23	37	19
2	2	22	25	24	29	21
1	1	21	19	27	37	13
1	1	NA	27	28	16	19
1	2	21	25	24	28	26
1	2	21	23	29	29	17
1	1	22	24	24	31	19
2	2	22	23	22	34	28
1	2	22	25	25	30	15
1	1	22	26	24	31	16
1	1	23	23	14	44	18
2	1	NA	22	22	35	25
1	2	22	32	24	47	15
1	2	21	22	24	39	24
1	1	23	18	24	34	24
2	1	21	19	24	15	14
2	2	32	23	22	26	19
2	2	32	24	22	25	20
2	1	21	19	21	30	27
1	1	20	16	21	25	20
1	1	21	23	21	33	25
1	1	22	17	15	39	16
1	1	21	17	26	24	19
1	2	21	28	22	44	15
1	2	21	24	24	31	17
1	1	22	21	13	30	22
1	1	21	14	19	21	19
2	1	25	21	10	38	44
1	2	22	20	28	30	19
1	1	21	25	25	31	19
2	2	21	20	24	32	23
1	1	20	17	22	34	19
1	2	21	26	30	43	28
1	2	22	17	22	29	17
4	2	21	17	24	40	22
1	2	23	24	23	31	25
1	2	24	30	20	36	44
2	1	20	25	22	39	19
2	1	21	15	22	31	21
1	1	23	25	19	36	25
1	1	24	18	24	36	28
1	1	22	20	22	36	19
1	1	22	32	26	41	33
2	2	21	14	12	23	19
2	1	20	20	25	23	12
1	2	21	25	29	34	15
1	NA	21	25	23	31	23
1	1	22	25	23	26	21
1	1	21	35	17	32	29
1	2	21	29	26	48	15
1	1	22	25	27	37	23
1	2	23	21	23	31	25
1	1	23	21	20	29	18
1	1	21	24	24	44	24
1	1	22	26	22	27	20
1	1	24	24	26	31	19
1	2	21	20	29	37	21
1	1	25	24	20	31	33
2	1	24	18	17	29	17
1	1	21	17	16	25	19
1	2	21	22	24	39	19
1	1	22	22	24	33	23
1	1	22	22	19	26	21
2	1	21	24	29	39	20
1	1	24	32	25	33	19
1	2	21	19	25	22	16
1	2	21	21	24	27	20
1	2	22	23	29	31	18
2	1	22	26	22	40	11
1	2	21	18	23	45	18
2	1	21	19	15	33	23
1	1	22	22	29	26	19
1	2	21	27	21	28	21
2	1	21	21	23	28	16
2	1	22	20	20	30	24
2	2	21	21	25	47	23
1	1	21	20	28	30	20
1	1	21	29	18	37	23
1	1	23	30	25	23	25
1	1	22	23	24	24	24
2	1	21	29	23	15	19
1	2	22	19	25	39	15
1	1	22	26	27	32	20
1	1	22	22	24	23	14
2	1	23	26	24	41	34
2	1	24	27	26	39	24
2	1	22	19	18	42	26
2	2	21	24	26	38	23
1	2	21	26	23	30	18
2	2	22	22	28	19	11
1	1	20	23	20	45	28
1	1	23	25	23	36	15
1	2	21	19	24	36	26
1	1	22	20	21	33	20
2	1	23	25	25	35	21
2	1	21	14	16	29	21
1	2	21	20	19	28	15
1	1	23	27	22	40	30
1	2	21	21	27	28	21
1	2	21	21	24	36	12
1	2	21	14	17	28	23
2	1	23	21	21	42	30
1	1	21	23	21	27	22
2	1	22	18	19	32	21
1	2	22	20	25	27	19
1	2	21	19	24	30	22
1	2	21	15	21	31	18
1	2	24	23	26	26	16
1	1	21	26	25	47	19
1	1	21	21	25	28	20
1	2	20	13	13	31	12
2	2	21	24	25	26	21
1	2	NA	17	23	27	14
1	1	24	21	26	28	23
1	1	22	28	22	44	30
1	1	22	22	20	32	21
1	1	22	25	14	25	25
1	1	22	18	23	24	13
1	1	23	27	24	45	17
1	1	22	25	21	37	24
2	1	22	21	24	25	21

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'AstonUniversity' @ aston.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 & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154362&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]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154362&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154362&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'AstonUniversity' @ aston.wessa.net






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C1930
C2580

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 93 & 0 \tabularnewline
C2 & 58 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154362&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]93[/C][C]0[/C][/ROW]
[ROW][C]C2[/C][C]58[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154362&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154362&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
C1930
C2580


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = equal ; par3 = 2 ; par4 = no ;
Parameters (R input):
par1 = 2 ; par2 = equal ; par3 = 2 ; par4 = no ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}