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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 computationSat, 08 Dec 2012 09:01:22 -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/08/t1354975316ez8l7j9pl0gvflr.htm/, Retrieved Thu, 25 Apr 2024 15:13:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197594, Retrieved Thu, 25 Apr 2024 15:13:57 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86

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]
- R PD  [Recursive Partitioning (Regression Trees)] [] [2012-12-08 13:42:23] [6808ef3204f32b6b44f616bd4c52b0ae]
-   P       [Recursive Partitioning (Regression Trees)] [] [2012-12-08 14:01:22] [00ffffaac852cc6d7cd42123567c45a2] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	26	21	21	23	17	23	4	127
1	20	16	15	24	17	20	4	108
1	19	19	18	22	18	20	6	110
2	19	18	11	20	21	21	8	102
1	20	16	8	24	20	24	8	104
1	25	23	19	27	28	22	4	140
2	25	17	4	28	19	23	4	112
1	22	12	20	27	22	20	8	115
1	26	19	16	24	16	25	5	121
1	22	16	14	23	18	23	4	112
2	17	19	10	24	25	27	4	118
2	22	20	13	27	17	27	4	122
1	19	13	14	27	14	22	4	105
1	24	20	8	28	11	24	4	111
1	26	27	23	27	27	25	4	151
2	21	17	11	23	20	22	8	106
1	13	8	9	24	22	28	4	100
2	26	25	24	28	22	28	4	149
2	20	26	5	27	21	27	4	122
1	22	13	15	25	23	25	8	115
2	14	19	5	19	17	16	4	86
1	21	15	19	24	24	28	7	124
1	7	5	6	20	14	21	4	69
2	23	16	13	28	17	24	4	117
1	17	14	11	26	23	27	5	113
1	25	24	17	23	24	14	4	123
1	25	24	17	23	24	14	4	123
1	19	9	5	20	8	27	4	84
2	20	19	9	11	22	20	4	97
1	23	19	15	24	23	21	4	121
2	22	25	17	25	25	22	4	132
1	22	19	17	23	21	21	4	119
1	21	18	20	18	24	12	15	98
2	15	15	12	20	15	20	10	87
2	20	12	7	20	22	24	4	101
2	22	21	16	24	21	19	8	115
1	18	12	7	23	25	28	4	109
2	20	15	14	25	16	23	4	109
2	28	28	24	28	28	27	4	159
1	22	25	15	26	23	22	4	129
1	18	19	15	26	21	27	7	119
1	23	20	10	23	21	26	4	119
1	20	24	14	22	26	22	6	122
2	25	26	18	24	22	21	5	131
2	26	25	12	21	21	19	4	120
1	15	12	9	20	18	24	16	82
2	17	12	9	22	12	19	5	86
2	23	15	8	20	25	26	12	105
1	21	17	18	25	17	22	6	114
2	13	14	10	20	24	28	9	100
1	18	16	17	22	15	21	9	100
1	19	11	14	23	13	23	4	99
1	22	20	16	25	26	28	5	132
1	16	11	10	23	16	10	4	82
2	24	22	19	23	24	24	4	132
1	18	20	10	22	21	21	5	107
1	20	19	14	24	20	21	4	114
1	24	17	10	25	14	24	4	110
2	14	21	4	21	25	24	4	105
2	22	23	19	12	25	25	5	121
1	24	18	9	17	20	25	4	109
1	18	17	12	20	22	23	6	106
1	21	27	16	23	20	21	4	124
2	23	25	11	23	26	16	4	120
1	17	19	18	20	18	17	18	91
2	22	22	11	28	22	25	4	126
2	24	24	24	24	24	24	6	138
2	21	20	17	24	17	23	4	118
1	22	19	18	24	24	25	4	128
1	16	11	9	24	20	23	5	98
1	21	22	19	28	19	28	4	133
2	23	22	18	25	20	26	4	130
2	22	16	12	21	15	22	5	103
1	24	20	23	25	23	19	10	124
1	24	24	22	25	26	26	5	142
1	16	16	14	18	22	18	8	96
1	16	16	14	17	20	18	8	93
2	21	22	16	26	24	25	5	129
2	26	24	23	28	26	27	4	150
2	15	16	7	21	21	12	4	88
2	25	27	10	27	25	15	4	125
1	18	11	12	22	13	21	5	92
0	23	21	12	21	20	23	4	0
1	20	20	12	25	22	22	4	117
2	17	20	17	22	23	21	8	112
2	25	27	21	23	28	24	4	144
1	24	20	16	26	22	27	5	130
1	17	12	11	19	20	22	14	87
1	19	8	14	25	6	28	8	92
1	20	21	13	21	21	26	8	114
1	15	18	9	13	20	10	4	81
2	27	24	19	24	18	19	4	127
1	22	16	13	25	23	22	6	115
1	23	18	19	26	20	21	4	123
1	16	20	13	25	24	24	7	115
1	19	20	13	25	22	25	7	117
2	25	19	13	22	21	21	4	117
1	19	17	14	21	18	20	6	103
2	19	16	12	23	21	21	4	108
2	26	26	22	25	23	24	7	139
1	21	15	11	24	23	23	4	113
2	20	22	5	21	15	18	4	97
1	24	17	18	21	21	24	8	117
1	22	23	19	25	24	24	4	133
2	20	21	14	22	23	19	4	115
1	18	19	15	20	21	20	10	103
2	18	14	12	20	21	18	8	95
1	24	17	19	23	20	20	6	117
1	24	12	15	28	11	27	4	113
1	22	24	17	23	22	23	4	127
1	23	18	8	28	27	26	4	126
1	22	20	10	24	25	23	5	119
1	20	16	12	18	18	17	4	97
1	18	20	12	20	20	21	6	105
1	25	22	20	28	24	25	4	140
2	18	12	12	21	10	23	5	91
1	16	16	12	21	27	27	7	112
1	20	17	14	25	21	24	8	113
2	19	22	6	19	21	20	5	102
1	15	12	10	18	18	27	8	92
1	19	14	18	21	15	21	10	98
1	19	23	18	22	24	24	8	122
1	16	15	7	24	22	21	5	100
1	17	17	18	15	14	15	12	84
1	28	28	9	28	28	25	4	142
2	23	20	17	26	18	25	5	124
1	25	23	22	23	26	22	4	137
1	20	13	11	26	17	24	6	105
2	17	18	15	20	19	21	4	106
2	23	23	17	22	22	22	4	125
1	16	19	15	20	18	23	7	104
2	23	23	22	23	24	22	7	130
2	11	12	9	22	15	20	10	79
2	18	16	13	24	18	23	4	108
2	24	23	20	23	26	25	5	136
1	23	13	14	22	11	23	8	98
1	21	22	14	26	26	22	11	120
2	16	18	12	23	21	25	7	108
2	24	23	20	27	23	26	4	139
1	23	20	20	23	23	22	8	123
1	18	10	8	21	15	24	6	90
1	20	17	17	26	22	24	7	119
1	9	18	9	23	26	25	5	105
2	24	15	18	21	16	20	4	110
1	25	23	22	27	20	26	8	135
1	20	17	10	19	18	21	4	101
2	21	17	13	23	22	26	8	114
2	25	22	15	25	16	21	6	118
2	22	20	18	23	19	22	4	120
2	21	20	18	22	20	16	9	108
1	21	19	12	22	19	26	5	114
1	22	18	12	25	23	28	6	122
1	27	22	20	25	24	18	4	132
2	24	20	12	28	25	25	4	130
2	24	22	16	28	21	23	4	130
2	21	18	16	20	21	21	5	112
1	18	16	18	25	23	20	6	114
1	16	16	16	19	27	25	16	103
1	22	16	13	25	23	22	6	115
1	20	16	17	22	18	21	6	108
2	18	17	13	18	16	16	4	94
1	20	18	17	20	16	18	4	105

Tables (Output of Computation)





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=197594&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=197594&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197594&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 time6 seconds
R Server'George Udny Yule' @ yule.wessa.net






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C1795
C21365

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 79 & 5 \tabularnewline
C2 & 13 & 65 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197594&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]79[/C][C]5[/C][/ROW]
[ROW][C]C2[/C][C]13[/C][C]65[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197594&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197594&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
C1795
C21365


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 9 ; par2 = hclust ; par3 = 3 ; par4 = no ;
Parameters (R input):
par1 = 9 ; 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')
}