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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 computationThu, 09 Dec 2010 17:50:36 +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/09/t1291916968szj8onjepx98pm1.htm/, Retrieved Sun, 28 Apr 2024 20:19:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=107300, Retrieved Sun, 28 Apr 2024 20:19:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsDMA
Estimated Impact235

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 19:50:12] [b98453cac15ba1066b407e146608df68]
-   PD    [Recursive Partitioning (Regression Trees)] [WS 10 Recursive P...] [2010-12-09 17:50:36] [f92ba2b01007f169e2985fcc57236bd0] [Current]
-    D      [Recursive Partitioning (Regression Trees)] [] [2010-12-13 10:00:28] [1251ac2db27b84d4a3ba43449388906b]
-    D        [Recursive Partitioning (Regression Trees)] [workshop 10: Tree] [2010-12-14 09:00:39] [814f53995537cd15c528d8efbf1cf544]
- R  D          [Recursive Partitioning (Regression Trees)] [ws10-4] [2011-12-13 16:19:21] [f7a862281046b7153543b12c78921b36]
- R PD        [Recursive Partitioning (Regression Trees)] [] [2010-12-14 09:26:41] [22937c5b58c14f6c22964f32d64ff823]
- R PD        [Recursive Partitioning (Regression Trees)] [] [2010-12-14 09:31:36] [22937c5b58c14f6c22964f32d64ff823]
-    D          [Recursive Partitioning (Regression Trees)] [] [2010-12-14 09:39:41] [22937c5b58c14f6c22964f32d64ff823]
F   PD      [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-13 21:06:08] [608064602fec1c42028cf50c6f981c88]
F             [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2010-12-13 21:09:30] [608064602fec1c42028cf50c6f981c88]
-   P           [Recursive Partitioning (Regression Trees)] [verbetering WS10] [2010-12-18 12:54:00] [c7506ced21a6c0dca45d37c8a93c80e0]
-    D      [Recursive Partitioning (Regression Trees)] [Paper DMA Recursi...] [2010-12-21 13:11:24] [2099aacba481f75a7f949aa310cab952]
-   PD      [Recursive Partitioning (Regression Trees)] [Paper DMA Recursi...] [2010-12-21 13:23:07] [2099aacba481f75a7f949aa310cab952]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3030,29	25,64
2803,47	27,97
2767,63	27,62
2882,6	23,31
2863,36	29,07
2897,06	29,58
3012,61	28,63
3142,95	29,92
3032,93	32,68
3045,78	31,54
3110,52	32,43
3013,24	26,54
2987,1	25,85
2995,55	27,6
2833,18	25,71
2848,96	25,38
2794,83	28,57
2845,26	27,64
2915,03	25,36
2892,63	25,9
2604,42	26,29
2641,65	21,74
2659,81	19,2
2638,53	19,32
2720,25	19,82
2745,88	20,36
2735,7	24,31
2811,7	25,97
2799,43	25,61
2555,28	24,67
2304,98	25,59
2214,95	26,09
2065,81	28,37
1940,49	27,34
2042	24,46
1995,37	27,46
1946,81	30,23
1765,9	32,33
1635,25	29,87
1833,42	24,87
1910,43	25,48
1959,67	27,28
1969,6	28,24
2061,41	29,58
2093,48	26,95
2120,88	29,08
2174,56	28,76
2196,72	29,59
2350,44	30,7
2440,25	30,52
2408,64	32,67
2472,81	33,19
2407,6	37,13
2454,62	35,54
2448,05	37,75
2497,84	41,84
2645,64	42,94
2756,76	49,14
2849,27	44,61
2921,44	40,22
2981,85	44,23
3080,58	45,85
3106,22	53,38
3119,31	53,26
3061,26	51,8
3097,31	55,3
3161,69	57,81
3257,16	63,96
3277,01	63,77
3295,32	59,15
3363,99	56,12
3494,17	57,42
3667,03	63,52
3813,06	61,71
3917,96	63,01
3895,51	68,18
3801,06	72,03
3570,12	69,75
3701,61	74,41
3862,27	74,33
3970,1	64,24
4138,52	60,03
4199,75	59,44
4290,89	62,5
4443,91	55,04
4502,64	58,34
4356,98	61,92
4591,27	67,65
4696,96	67,68
4621,4	70,3
4562,84	75,26
4202,52	71,44
4296,49	76,36
4435,23	81,71
4105,18	92,6
4116,68	90,6
3844,49	92,23
3720,98	94,09
3674,4	102,79
3857,62	109,65
3801,06	124,05
3504,37	132,69
3032,6	135,81
3047,03	116,07
2962,34	101,42
2197,82	75,73
2014,45	55,48
1862,83	43,8
1905,41	45,29

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'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107300&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107300&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107300&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'RServer@AstonUniversity' @ vre.aston.ac.uk






10-Fold Cross Validation
Prediction (training)Prediction (testing)
ActualC1C2CVC1C2CV
C1472180.96335820.9667
C21013980.79769320.7805
Overall--0.8797--0.8911

\begin{tabular}{lllllllll}
\hline
10-Fold Cross Validation \tabularnewline
 & Prediction (training) & Prediction (testing) \tabularnewline
Actual & C1 & C2 & CV & C1 & C2 & CV \tabularnewline
C1 & 472 & 18 & 0.9633 & 58 & 2 & 0.9667 \tabularnewline
C2 & 101 & 398 & 0.7976 & 9 & 32 & 0.7805 \tabularnewline
Overall & - & - & 0.8797 & - & - & 0.8911 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107300&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]472[/C][C]18[/C][C]0.9633[/C][C]58[/C][C]2[/C][C]0.9667[/C][/ROW]
[ROW][C]C2[/C][C]101[/C][C]398[/C][C]0.7976[/C][C]9[/C][C]32[/C][C]0.7805[/C][/ROW]
[ROW][C]Overall[/C][C]-[/C][C]-[/C][C]0.8797[/C][C]-[/C][C]-[/C][C]0.8911[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107300&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107300&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
C1472180.96335820.9667
C21013980.79769320.7805
Overall--0.8797--0.8911






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C1532
C21143

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 53 & 2 \tabularnewline
C2 & 11 & 43 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107300&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]53[/C][C]2[/C][/ROW]
[ROW][C]C2[/C][C]11[/C][C]43[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107300&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107300&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
C1532
C21143


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = quantiles ; par3 = 2 ; par4 = yes ;
Parameters (R input):
par1 = 1 ; 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')
}