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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 computationMon, 13 Dec 2010 18:26:41 +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/13/t1292264748xlr02gnrz3g1u1b.htm/, Retrieved Tue, 07 May 2024 00:57:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109033, Retrieved Tue, 07 May 2024 00:57:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact178

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]
F   P         [Recursive Partitioning (Regression Trees)] [] [2010-12-13 18:26:41] [7cc6e89f95359dcad314da35cb7f084f] [Current]
-   PD          [Recursive Partitioning (Regression Trees)] [ws 10 verbetering...] [2010-12-18 12:22:35] [05ab9592748364013445d860bb938e43]
Feedback Forum
2010-12-19 17:57:59 [00c625c7d009d84797af914265b614f9] [reply] 
Correct,
Er is een kans van ongeveer 95% dat de concern over mistakes score tussen 22 en 38 ligt indien PS groter is dan 24 en DAA groter is dan 10. Bij C1 zijn er 71 goede voorspellingen en 18 slechte. Bij C2 51 goede en 19 slechte.

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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 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=109033&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=109033&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109033&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






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=109033&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]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=109033&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109033&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
C17118
C21951


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = quantiles ; par3 = 2 ; par4 = no ;
Parameters (R input):
par1 = 2 ; par2 = quantiles ; par3 = 2 ; par4 = no ;
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')
}