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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 computationSun, 12 Dec 2010 19:56:19 +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/12/t1292183650uz4y5oo94t17gl2.htm/, Retrieved Tue, 07 May 2024 17:56:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108647, Retrieved Tue, 07 May 2024 17:56:00 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact166

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 20:06:20] [b98453cac15ba1066b407e146608df68]
-   PD  [Recursive Partitioning (Regression Trees)] [WS10] [2010-12-12 19:38:16] [87116ee6ef949037dfa02b8eb1a3bf97]
-           [Recursive Partitioning (Regression Trees)] [WS10] [2010-12-12 19:56:19] [66b4703b90a9701067ac75b10c82aca9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
10	11	16	1	24	14	33	12	24
14	11	13	2	25	11	30	8	25
18	15	16	2	17	6	30	8	30
15	9	15	1	18	12	26	8	19
11	17	15	2	16	10	24	7	22
17	16	14	2	20	10	28	4	25
19	9	11	2	16	11	24	11	23
7	12	15	2	18	16	27	7	17
12	14	13	2	17	11	28	7	21
15	4	6	2	30	12	42	10	19
14	13	11	2	23	8	31	10	15
14	12	9	2	18	12	25	8	16
16	13	14	1	12	4	23	4	27
12	15	5	2	21	9	27	9	22
12	10	8	1	15	8	23	8	14
13	9	6	1	20	8	34	7	22
9	11	15	2	27	15	36	9	23
11	15	12	2	21	9	31	13	19
12	10	10	1	31	14	39	8	18
11	9	8	1	19	11	27	8	20
14	15	16	2	16	8	27	9	23
18	12	8	2	20	9	31	6	25
11	12	12	1	21	9	31	9	19
17	14	14	2	17	9	26	6	22
14	16	13	1	22	9	34	9	24
14	5	8	2	26	11	39	5	29
12	10	11	2	25	16	39	16	26
14	9	12	2	25	8	35	7	32
15	14	13	2	17	9	30	9	25
10	5	4	1	33	14	40	6	32
11	12	16	1	32	16	38	6	29
14	14	17	1	13	16	21	5	17
11	16	14	2	32	12	45	12	28
15	11	8	2	22	9	32	9	25
16	6	6	2	17	9	29	5	25
15	11	15	1	33	11	40	6	28
16	9	11	2	31	14	44	11	23
13	16	16	1	20	10	28	8	26
15	13	5	1	15	12	24	8	20
16	10	5	2	29	10	37	8	25
13	6	9	1	23	13	33	12	19
9	12	7	1	26	16	30	4	23
14	15	14	1	18	9	26	8	21
15	15	12	2	11	6	16	4	15
14	11	7	1	28	8	48	20	30
16	16	16	2	20	10	30	8	20
13	12	10	2	26	13	35	8	24
17	11	8	1	29	14	43	10	26
16	14	15	1	15	11	22	8	23
15	7	8	1	12	7	16	4	22
16	11	12	2	14	15	25	8	14
15	13	14	1	17	9	27	9	24
13	16	16	1	21	10	31	6	24
11	17	15	2	16	10	24	7	22
16	12	14	1	18	13	25	9	24
17	14	16	1	10	10	18	5	19
10	6	15	1	29	11	36	5	31
17	8	7	1	31	8	39	8	22
11	8	10	1	19	9	29	8	27
14	14	13	1	9	13	16	6	19
15	12	13	2	20	11	29	8	25
11	13	8	2	20	14	30	10	18
15	9	6	2	19	9	26	7	21
16	12	6	2	30	9	41	9	27
16	13	14	2	28	8	37	7	20
15	15	16	2	29	15	43	11	23
14	11	11	2	26	9	37	6	25
17	14	15	2	23	10	33	8	20
12	16	12	2	21	12	31	9	22
13	14	8	2	23	14	36	7	25
12	8	8	1	19	12	26	8	23
9	16	16	2	28	11	37	6	25
17	13	14	2	18	6	26	8	17
11	4	4	1	21	12	31	8	19
16	11	5	2	20	8	32	10	25
14	16	16	2	22	10	32	8	26
9	8	9	1	23	14	29	5	19
15	14	15	1	21	11	33	7	20
17	16	14	2	20	10	28	4	25
17	12	7	1	15	14	22	8	23
15	16	15	1	19	10	28	7	17
18	7	12	1	26	14	36	8	17
13	14	15	1	16	11	23	5	17
15	13	11	2	22	10	34	6	22
12	12	10	2	23	14	34	10	25
16	7	7	1	19	9	27	10	21
17	14	19	2	31	10	47	12	32
13	14	13	2	29	13	44	12	21
15	11	11	1	31	16	43	9	21
12	14	13	1	19	9	27	7	18
11	13	12	2	22	10	32	8	18
15	15	13	2	23	10	34	10	23
15	12	11	1	15	7	24	6	19
18	14	10	2	18	8	31	10	21
16	14	14	1	23	14	31	10	20
12	16	14	2	25	14	34	5	17
16	12	7	2	21	8	28	7	18
15	16	14	2	24	9	35	10	19
15	11	14	1	17	14	27	6	15
17	10	13	2	13	8	21	7	14
16	11	7	2	25	7	38	11	35
13	12	14	2	9	6	15	11	29
13	13	7	1	21	8	29	11	24
13	14	12	1	25	14	35	9	22
16	11	14	1	20	11	25	4	13
11	11	10	2	22	14	33	11	25
15	12	12	2	14	11	23	7	17
15	15	15	2	15	8	19	6	20
9	10	9	1	18	10	30	8	14
14	12	12	1	19	20	25	7	19
14	8	8	1	20	11	33	8	21
15	15	14	2	20	11	28	8	24
14	13	13	2	18	10	29	9	21
15	12	14	2	33	14	41	8	26
14	12	14	2	29	11	33	4	26
13	10	4	2	22	11	31	11	24
15	11	12	2	16	9	25	8	16
16	10	15	1	17	9	24	5	23
14	8	10	1	21	10	31	8	16
14	8	10	2	18	13	28	6	19
14	12	11	2	18	12	27	9	21
15	9	15	1	18	12	26	8	19
15	15	12	2	17	8	26	9	21
13	16	15	2	22	13	31	13	22
15	13	16	2	30	14	37	9	23
16	7	13	2	30	12	43	10	29
10	8	4	2	24	14	43	20	21
8	8	10	1	21	15	26	5	21
14	9	11	2	29	16	37	6	27
12	16	8	2	28	12	40	14	27
13	16	15	2	31	9	45	9	25
15	9	9	2	20	9	28	7	21
14	8	9	2	22	8	32	10	20
15	14	10	2	25	14	36	11	22
19	16	14	2	20	7	27	9	26
17	12	15	2	15	8	21	4	22
16	10	8	2	38	11	55	7	29
17	10	8	2	28	16	40	8	24
13	12	11	2	16	8	26	5	21
16	19	15	2	22	9	32	6	19
14	12	15	2	20	11	35	13	24
12	15	13	1	26	13	42	10	26
12	15	5	2	21	9	27	9	22
13	15	17	1	28	14	36	8	24

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=108647&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=108647&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108647&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
C1962
C2541

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 96 & 2 \tabularnewline
C2 & 5 & 41 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108647&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]96[/C][C]2[/C][/ROW]
[ROW][C]C2[/C][C]5[/C][C]41[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108647&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108647&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
C1962
C2541


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = none ; par4 = no ;
Parameters (R input):
par1 = 5 ; 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')
}