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 computationFri, 10 Dec 2010 15:40:47 +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/10/t1291995613u39qua5nza8bwa4.htm/, Retrieved Mon, 29 Apr 2024 16:14:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=107775, Retrieved Mon, 29 Apr 2024 16:14:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact133

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]
F   PD    [Recursive Partitioning (Regression Trees)] [] [2010-12-10 15:40:47] [c2e23af56713b360851e64c7775b3f2b] [Current]
-   P       [Recursive Partitioning (Regression Trees)] [verbetering ws10] [2010-12-19 17:22:40] [c7506ced21a6c0dca45d37c8a93c80e0]
Feedback Forum
2010-12-19 17:25:17 [00c625c7d009d84797af914265b614f9] [reply] 
http://www.freestatistics.org/blog/index.php?v=date/2010/Dec/19/t1292779297uwi01unn6hbhk9v.htm/
Categorization based on quantiles.
dan krijgen we geen boomstructuur te zien. We kunnen enkel zeggen dat 50% een score op organisatie heeft tusse 10 en 24. En ook nog eens 50% tussen 24 en 30. Organisatie is dus niet afhankelijk van PS of PEC.

Post a new message

Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107775&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107775&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107775&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 time5 seconds
R Server'George Udny Yule' @ 72.249.76.132






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C11217
C2790

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 12 & 17 \tabularnewline
C2 & 7 & 90 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107775&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]12[/C][C]17[/C][/ROW]
[ROW][C]C2[/C][C]7[/C][C]90[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107775&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107775&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
C11217
C2790


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 ;
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')
}