Free Statistics

of Irreproducible Research!

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, 10 Dec 2012 12:23:08 -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/t1355160219fqgz611xszjlt4l.htm/, Retrieved Fri, 29 Mar 2024 11:20:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=198251, Retrieved Fri, 29 Mar 2024 11:20:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact92
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 19:35:21] [b98453cac15ba1066b407e146608df68]
- R PD    [Recursive Partitioning (Regression Trees)] [WS 10, Recursive ...] [2012-12-10 17:23:08] [e4c351aee2a0bb2c047702ea90f356fa] [Current]
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Dataseries X:
2	7	41	38	13	12	14	12	53
2	5	39	32	16	11	18	11	86
2	5	30	35	19	15	11	14	66
1	5	31	33	15	6	12	12	67
2	8	34	37	14	13	16	21	76
2	6	35	29	13	10	18	12	78
2	5	39	31	19	12	14	22	53
2	6	34	36	15	14	14	11	80
2	5	36	35	14	12	15	10	74
2	4	37	38	15	6	15	13	76
1	6	38	31	16	10	17	10	79
2	5	36	34	16	12	19	8	54
1	5	38	35	16	12	10	15	67
2	6	39	38	16	11	16	14	54
2	7	33	37	17	15	18	10	87
1	6	32	33	15	12	14	14	58
1	7	36	32	15	10	14	14	75
2	6	38	38	20	12	17	11	88
1	8	39	38	18	11	14	10	64
2	7	32	32	16	12	16	13	57
1	5	32	33	16	11	18	7	66
2	5	31	31	16	12	11	14	68
2	7	39	38	19	13	14	12	54
2	7	37	39	16	11	12	14	56
1	5	39	32	17	9	17	11	86
2	4	41	32	17	13	9	9	80
1	10	36	35	16	10	16	11	76
2	6	33	37	15	14	14	15	69
2	5	33	33	16	12	15	14	78
1	5	34	33	14	10	11	13	67
2	5	31	28	15	12	16	9	80
1	5	27	32	12	8	13	15	54
2	6	37	31	14	10	17	10	71
2	5	34	37	16	12	15	11	84
1	5	34	30	14	12	14	13	74
1	5	32	33	7	7	16	8	71
1	5	29	31	10	6	9	20	63
1	5	36	33	14	12	15	12	71
2	5	29	31	16	10	17	10	76
1	5	35	33	16	10	13	10	69
1	5	37	32	16	10	15	9	74
2	7	34	33	14	12	16	14	75
1	5	38	32	20	15	16	8	54
1	6	35	33	14	10	12	14	52
2	7	38	28	14	10	12	11	69
2	7	37	35	11	12	11	13	68
2	5	38	39	14	13	15	9	65
2	5	33	34	15	11	15	11	75
2	4	36	38	16	11	17	15	74
1	5	38	32	14	12	13	11	75
2	4	32	38	16	14	16	10	72
1	5	32	30	14	10	14	14	67
1	5	32	33	12	12	11	18	63
2	7	34	38	16	13	12	14	62
1	5	32	32	9	5	12	11	63
2	5	37	32	14	6	15	12	76
2	6	39	34	16	12	16	13	74
2	4	29	34	16	12	15	9	67
1	6	37	36	15	11	12	10	73
2	6	35	34	16	10	12	15	70
1	5	30	28	12	7	8	20	53
1	7	38	34	16	12	13	12	77
2	6	34	35	16	14	11	12	77
2	8	31	35	14	11	14	14	52
2	7	34	31	16	12	15	13	54
1	5	35	37	17	13	10	11	80
2	6	36	35	18	14	11	17	66
1	6	30	27	18	11	12	12	73
2	5	39	40	12	12	15	13	63
1	5	35	37	16	12	15	14	69
1	5	38	36	10	8	14	13	67
2	5	31	38	14	11	16	15	54
2	4	34	39	18	14	15	13	81
1	6	38	41	18	14	15	10	69
1	6	34	27	16	12	13	11	84
2	6	39	30	17	9	12	19	80
2	6	37	37	16	13	17	13	70
2	7	34	31	16	11	13	17	69
1	5	28	31	13	12	15	13	77
1	7	37	27	16	12	13	9	54
1	6	33	36	16	12	15	11	79
1	5	37	38	20	12	16	10	30
2	5	35	37	16	12	15	9	71
1	4	37	33	15	12	16	12	73
2	8	32	34	15	11	15	12	72
2	8	33	31	16	10	14	13	77
1	5	38	39	14	9	15	13	75
2	5	33	34	16	12	14	12	69
2	6	29	32	16	12	13	15	54
2	4	33	33	15	12	7	22	70
2	5	31	36	12	9	17	13	73
2	5	36	32	17	15	13	15	54
2	5	35	41	16	12	15	13	77
2	5	32	28	15	12	14	15	82
2	6	29	30	13	12	13	10	80
2	6	39	36	16	10	16	11	80
2	5	37	35	16	13	12	16	69
2	6	35	31	16	9	14	11	78
1	5	37	34	16	12	17	11	81
1	7	32	36	14	10	15	10	76
2	5	38	36	16	14	17	10	76
1	6	37	35	16	11	12	16	73
2	6	36	37	20	15	16	12	85
1	6	32	28	15	11	11	11	66
2	4	33	39	16	11	15	16	79
1	5	40	32	13	12	9	19	68
2	5	38	35	17	12	16	11	76
1	7	41	39	16	12	15	16	71
1	6	36	35	16	11	10	15	54
2	9	43	42	12	7	10	24	46
2	6	30	34	16	12	15	14	82
2	6	31	33	16	14	11	15	74
2	5	32	41	17	11	13	11	88
1	6	32	33	13	11	14	15	38
2	5	37	34	12	10	18	12	76
1	8	37	32	18	13	16	10	86
2	7	33	40	14	13	14	14	54
2	5	34	40	14	8	14	13	70
2	7	33	35	13	11	14	9	69
2	6	38	36	16	12	14	15	90
2	6	33	37	13	11	12	15	54
2	9	31	27	16	13	14	14	76
2	7	38	39	13	12	15	11	89
2	6	37	38	16	14	15	8	76
2	5	33	31	15	13	15	11	73
2	5	31	33	16	15	13	11	79
1	6	39	32	15	10	17	8	90
2	6	44	39	17	11	17	10	74
2	7	33	36	15	9	19	11	81
2	5	35	33	12	11	15	13	72
1	5	32	33	16	10	13	11	71
1	5	28	32	10	11	9	20	66
2	6	40	37	16	8	15	10	77
1	4	27	30	12	11	15	15	65
1	5	37	38	14	12	15	12	74
2	7	32	29	15	12	16	14	82
1	5	28	22	13	9	11	23	54
1	7	34	35	15	11	14	14	63
2	7	30	35	11	10	11	16	54
2	6	35	34	12	8	15	11	64
1	5	31	35	8	9	13	12	69
2	8	32	34	16	8	15	10	54
1	5	30	34	15	9	16	14	84
2	5	30	35	17	15	14	12	86
1	5	31	23	16	11	15	12	77
2	6	40	31	10	8	16	11	89
2	4	32	27	18	13	16	12	76
1	5	36	36	13	12	11	13	60
1	5	32	31	16	12	12	11	75
1	7	35	32	13	9	9	19	73
2	6	38	39	10	7	16	12	85
2	7	42	37	15	13	13	17	79
1	10	34	38	16	9	16	9	71
2	6	35	39	16	6	12	12	72
2	8	35	34	14	8	9	19	69
2	4	33	31	10	8	13	18	78
2	5	36	32	17	15	13	15	54
2	6	32	37	13	6	14	14	69
2	7	33	36	15	9	19	11	81
2	7	34	32	16	11	13	9	84
2	6	32	35	12	8	12	18	84
2	6	34	36	13	8	13	16	69




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'George Udny Yule' @ yule.wessa.net

\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 & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198251&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198251&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198251&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 time6 seconds
R Server'George Udny Yule' @ yule.wessa.net







Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C11361
C2187

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 136 & 1 \tabularnewline
C2 & 18 & 7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198251&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]136[/C][C]1[/C][/ROW]
[ROW][C]C2[/C][C]18[/C][C]7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198251&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198251&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)
C1C2
C11361
C2187



Parameters (Session):
par1 = 5 ; par2 = quantiles ; par3 = 2 ; par4 = no ;
Parameters (R input):
par1 = 5 ; 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')
}