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of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationMon, 10 Dec 2012 13:36:41 -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/2012/Dec/10/t1355164654nd25c5qhdwjcuih.htm/, Retrieved Fri, 29 Mar 2024 14:54:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=198279, Retrieved Fri, 29 Mar 2024 14:54:55 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact96
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 20:43:11] [b98453cac15ba1066b407e146608df68]
- R PD  [Recursive Partitioning (Regression Trees)] [WS10 geslacht] [2012-12-10 18:04:29] [f8ee2fa4f3a14474001c30fec05fcd2b]
-  M        [Recursive Partitioning (Regression Trees)] [WS10 Geslacht 3] [2012-12-10 18:36:41] [4c93b3a0c48c946a3a36627369b78a37] [Current]
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Dataseries X:
1	1	14	12	26	21	21	23	17	23	127
1	1	18	11	20	16	15	24	17	20	108
1	1	11	14	19	19	18	22	18	20	110
1	0	12	12	19	18	11	20	21	21	102
1	1	16	21	20	16	8	24	20	24	104
1	1	18	12	25	23	19	27	28	22	140
1	0	14	22	25	17	4	28	19	23	112
1	1	14	11	22	12	20	27	22	20	115
1	1	15	10	26	19	16	24	16	25	121
1	1	15	13	22	16	14	23	18	23	112
1	0	17	10	17	19	10	24	25	27	118
1	0	19	8	22	20	13	27	17	27	122
1	1	10	15	19	13	14	27	14	22	105
1	1	16	14	24	20	8	28	11	24	111
1	1	18	10	26	27	23	27	27	25	151
1	0	14	14	21	17	11	23	20	22	106
1	1	14	14	13	8	9	24	22	28	100
1	0	17	11	26	25	24	28	22	28	149
1	0	14	10	20	26	5	27	21	27	122
1	1	16	13	22	13	15	25	23	25	115
1	0	18	7	14	19	5	19	17	16	86
1	1	11	14	21	15	19	24	24	28	124
1	1	14	12	7	5	6	20	14	21	69
1	0	12	14	23	16	13	28	17	24	117
1	1	17	11	17	14	11	26	23	27	113
1	1	9	9	25	24	17	23	24	14	123
1	1	16	11	25	24	17	23	24	14	123
1	1	14	15	19	9	5	20	8	27	84
1	0	15	14	20	19	9	11	22	20	97
1	1	11	13	23	19	15	24	23	21	121
1	0	16	9	22	25	17	25	25	22	132
1	1	13	15	22	19	17	23	21	21	119
1	1	17	10	21	18	20	18	24	12	98
1	0	15	11	15	15	12	20	15	20	87
1	0	14	13	20	12	7	20	22	24	101
1	0	16	8	22	21	16	24	21	19	115
1	1	9	20	18	12	7	23	25	28	109
1	0	15	12	20	15	14	25	16	23	109
1	0	17	10	28	28	24	28	28	27	159
1	1	13	10	22	25	15	26	23	22	129
1	1	15	9	18	19	15	26	21	27	119
1	1	16	14	23	20	10	23	21	26	119
1	1	16	8	20	24	14	22	26	22	122
1	0	12	14	25	26	18	24	22	21	131
1	0	12	11	26	25	12	21	21	19	120
1	1	11	13	15	12	9	20	18	24	82
1	0	15	9	17	12	9	22	12	19	86
1	0	15	11	23	15	8	20	25	26	105
1	1	17	15	21	17	18	25	17	22	114
1	0	13	11	13	14	10	20	24	28	100
1	1	16	10	18	16	17	22	15	21	100
1	1	14	14	19	11	14	23	13	23	99
1	1	11	18	22	20	16	25	26	28	132
1	1	12	14	16	11	10	23	16	10	82
1	0	12	11	24	22	19	23	24	24	132
1	1	15	12	18	20	10	22	21	21	107
1	1	16	13	20	19	14	24	20	21	114
1	1	15	9	24	17	10	25	14	24	110
1	0	12	10	14	21	4	21	25	24	105
1	0	12	15	22	23	19	12	25	25	121
1	1	8	20	24	18	9	17	20	25	109
1	1	13	12	18	17	12	20	22	23	106
1	1	11	12	21	27	16	23	20	21	124
1	0	14	14	23	25	11	23	26	16	120
1	1	15	13	17	19	18	20	18	17	91
1	0	10	11	22	22	11	28	22	25	126
1	0	11	17	24	24	24	24	24	24	138
1	0	12	12	21	20	17	24	17	23	118
1	1	15	13	22	19	18	24	24	25	128
1	1	15	14	16	11	9	24	20	23	98
1	1	14	13	21	22	19	28	19	28	133
1	0	16	15	23	22	18	25	20	26	130
1	0	15	13	22	16	12	21	15	22	103
1	1	15	10	24	20	23	25	23	19	124
1	1	13	11	24	24	22	25	26	26	142
1	1	12	19	16	16	14	18	22	18	96
1	1	17	13	16	16	14	17	20	18	93
1	0	13	17	21	22	16	26	24	25	129
1	0	15	13	26	24	23	28	26	27	150
1	0	13	9	15	16	7	21	21	12	88
1	0	15	11	25	27	10	27	25	15	125
1	1	16	10	18	11	12	22	13	21	92
1	0	15	9	23	21	12	21	20	23	0
1	1	16	12	20	20	12	25	22	22	117
1	0	15	12	17	20	17	22	23	21	112
1	0	14	13	25	27	21	23	28	24	144
1	1	15	13	24	20	16	26	22	27	130
1	1	14	12	17	12	11	19	20	22	87
1	1	13	15	19	8	14	25	6	28	92
1	1	7	22	20	21	13	21	21	26	114
1	1	17	13	15	18	9	13	20	10	81
1	0	13	15	27	24	19	24	18	19	127
1	1	15	13	22	16	13	25	23	22	115
1	1	14	15	23	18	19	26	20	21	123
1	1	13	10	16	20	13	25	24	24	115
1	1	16	11	19	20	13	25	22	25	117
1	0	12	16	25	19	13	22	21	21	117
1	1	14	11	19	17	14	21	18	20	103
1	0	17	11	19	16	12	23	21	21	108
1	0	15	10	26	26	22	25	23	24	139
1	1	17	10	21	15	11	24	23	23	113
1	0	12	16	20	22	5	21	15	18	97
1	1	16	12	24	17	18	21	21	24	117
1	1	11	11	22	23	19	25	24	24	133
1	0	15	16	20	21	14	22	23	19	115
1	1	9	19	18	19	15	20	21	20	103
1	0	16	11	18	14	12	20	21	18	95
1	1	15	16	24	17	19	23	20	20	117
1	1	10	15	24	12	15	28	11	27	113
1	1	10	24	22	24	17	23	22	23	127
1	1	15	14	23	18	8	28	27	26	126
1	1	11	15	22	20	10	24	25	23	119
1	1	13	11	20	16	12	18	18	17	97
1	1	14	15	18	20	12	20	20	21	105
1	1	18	12	25	22	20	28	24	25	140
1	0	16	10	18	12	12	21	10	23	91
1	1	14	14	16	16	12	21	27	27	112
1	1	14	13	20	17	14	25	21	24	113
1	0	14	9	19	22	6	19	21	20	102
1	1	14	15	15	12	10	18	18	27	92
1	1	12	15	19	14	18	21	15	21	98
1	1	14	14	19	23	18	22	24	24	122
1	1	15	11	16	15	7	24	22	21	100
1	1	15	8	17	17	18	15	14	15	84
1	1	15	11	28	28	9	28	28	25	142
1	0	13	11	23	20	17	26	18	25	124
1	1	17	8	25	23	22	23	26	22	137
1	1	17	10	20	13	11	26	17	24	105
1	0	19	11	17	18	15	20	19	21	106
1	0	15	13	23	23	17	22	22	22	125
1	1	13	11	16	19	15	20	18	23	104
1	0	9	20	23	23	22	23	24	22	130
1	0	15	10	11	12	9	22	15	20	79
1	0	15	15	18	16	13	24	18	23	108
1	0	15	12	24	23	20	23	26	25	136
1	1	16	14	23	13	14	22	11	23	98
1	1	11	23	21	22	14	26	26	22	120
1	0	14	14	16	18	12	23	21	25	108
1	0	11	16	24	23	20	27	23	26	139
1	1	15	11	23	20	20	23	23	22	123
1	1	13	12	18	10	8	21	15	24	90
1	1	15	10	20	17	17	26	22	24	119
1	1	16	14	9	18	9	23	26	25	105
1	0	14	12	24	15	18	21	16	20	110
1	1	15	12	25	23	22	27	20	26	135
1	1	16	11	20	17	10	19	18	21	101
1	0	16	12	21	17	13	23	22	26	114
1	0	11	13	25	22	15	25	16	21	118
1	0	12	11	22	20	18	23	19	22	120
1	0	9	19	21	20	18	22	20	16	108
1	1	16	12	21	19	12	22	19	26	114
1	1	13	17	22	18	12	25	23	28	122
1	1	16	9	27	22	20	25	24	18	132
1	0	12	12	24	20	12	28	25	25	130
1	0	9	19	24	22	16	28	21	23	130
1	0	13	18	21	18	16	20	21	21	112
1	1	13	15	18	16	18	25	23	20	114
1	1	14	14	16	16	16	19	27	25	103
0	1	19	11	22	16	13	25	23	22	115
0	1	13	9	20	16	17	22	18	21	108
0	0	12	18	18	17	13	18	16	16	94
0	1	13	16	20	18	17	20	16	18	105




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=198279&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=198279&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198279&T=0

As an alternative you can also use a 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'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Confusion Matrix (predicted in columns / actuals in rows)
C1C2C3C4C5
C100004
C200000
C300000
C400000
C50000158

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 & C3 & C4 & C5 \tabularnewline
C1 & 0 & 0 & 0 & 0 & 4 \tabularnewline
C2 & 0 & 0 & 0 & 0 & 0 \tabularnewline
C3 & 0 & 0 & 0 & 0 & 0 \tabularnewline
C4 & 0 & 0 & 0 & 0 & 0 \tabularnewline
C5 & 0 & 0 & 0 & 0 & 158 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198279&T=1

[TABLE]
[ROW][C]Confusion Matrix (predicted in columns / actuals in rows)[/C][/ROW]
[ROW][C][/C][C]C1[/C][C]C2[/C][C]C3[/C][C]C4[/C][C]C5[/C][/ROW]
[ROW][C]C1[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]4[/C][/ROW]
[ROW][C]C2[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]C3[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]C4[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]C5[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]158[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198279&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198279&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Confusion Matrix (predicted in columns / actuals in rows)
C1C2C3C4C5
C100004
C200000
C300000
C400000
C50000158



Parameters (Session):
par1 = 1 ; par2 = equal ; par3 = 5 ; par4 = no ;
Parameters (R input):
par1 = 1 ; par2 = equal ; par3 = 5 ; 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')
}