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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 computationThu, 15 Dec 2011 10:00:02 -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/2011/Dec/15/t1323961287h4ccc2qkd2m2v2w.htm/, Retrieved Wed, 08 May 2024 15:05:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155460, Retrieved Wed, 08 May 2024 15:05:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact100

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 19:35:21] [b98453cac15ba1066b407e146608df68]
-   PD    [Recursive Partitioning (Regression Trees)] [WS 10 Categorization] [2011-12-15 15:00:02] [312c935a345ba403c8b6fcfc5a5ec794] [Current]
- RMPD      [Testing Mean with unknown Variance - Critical Value] [Paper Hypothese 1] [2011-12-15 15:22:16] [a98cda5652e91ff8cb6c2e418e82d3de]
- RMPD        [Paired and Unpaired Two Samples Tests about the Mean] [Paper Hypothese 2...] [2011-12-15 15:49:46] [a98cda5652e91ff8cb6c2e418e82d3de]
-    D          [Paired and Unpaired Two Samples Tests about the Mean] [Paper Hypothese 2...] [2011-12-15 16:09:22] [a98cda5652e91ff8cb6c2e418e82d3de]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	53	41	7	2
16	86	39	5	2
19	66	30	5	2
15	67	31	5	1
14	76	34	8	2
13	78	35	6	2
19	53	39	5	2
15	80	34	6	2
14	74	36	5	2
15	76	37	4	2
16	79	38	6	1
16	54	36	5	2
16	67	38	5	1
16	54	39	6	2
17	87	33	7	2
15	58	32	6	1
15	75	36	7	1
20	88	38	6	2
18	64	39	8	1
16	57	32	7	2
16	66	32	5	1
16	68	31	5	2
19	54	39	7	2
16	56	37	7	2
17	86	39	5	1
17	80	41	4	2
16	76	36	10	1
15	69	33	6	2
16	78	33	5	2
14	67	34	5	1
15	80	31	5	2
12	54	27	5	1
14	71	37	6	2
16	84	34	5	2
14	74	34	5	1
7	71	32	5	1
10	63	29	5	1
14	71	36	5	1
16	76	29	5	2
16	69	35	5	1
16	74	37	5	1
14	75	34	7	2
20	54	38	5	1
14	52	35	6	1
14	69	38	7	2
11	68	37	7	2
14	65	38	5	2
15	75	33	5	2
16	74	36	4	2
14	75	38	5	1
16	72	32	4	2
14	67	32	5	1
12	63	32	5	1
16	62	34	7	2
9	63	32	5	1
14	76	37	5	2
16	74	39	6	2
16	67	29	4	2
15	73	37	6	1
16	70	35	6	2
12	53	30	5	1
16	77	38	7	1
16	77	34	6	2
14	52	31	8	2
16	54	34	7	2
17	80	35	5	1
18	66	36	6	2
18	73	30	6	1
12	63	39	5	2
16	69	35	5	1
10	67	38	5	1
14	54	31	5	2
18	81	34	4	2
18	69	38	6	1
16	84	34	6	1
17	80	39	6	2
16	70	37	6	2
16	69	34	7	2
13	77	28	5	1
16	54	37	7	1
16	79	33	6	1
20	30	37	5	1
16	71	35	5	2
15	73	37	4	1
15	72	32	8	2
16	77	33	8	2
14	75	38	5	1
16	69	33	5	2
16	54	29	6	2
15	70	33	4	2
12	73	31	5	2
17	54	36	5	2
16	77	35	5	2
15	82	32	5	2
13	80	29	6	2
16	80	39	6	2
16	69	37	5	2
16	78	35	6	2
16	81	37	5	1
14	76	32	7	1
16	76	38	5	2
16	73	37	6	1
20	85	36	6	2
15	66	32	6	1
16	79	33	4	2
13	68	40	5	1
17	76	38	5	2
16	71	41	7	1
16	54	36	6	1
12	46	43	9	2
16	82	30	6	2
16	74	31	6	2
17	88	32	5	2
13	38	32	6	1
12	76	37	5	2
18	86	37	8	1
14	54	33	7	2
14	70	34	5	2
13	69	33	7	2
16	90	38	6	2
13	54	33	6	2
16	76	31	9	2
13	89	38	7	2
16	76	37	6	2
15	73	33	5	2
16	79	31	5	2
15	90	39	6	1
17	74	44	6	2
15	81	33	7	2
12	72	35	5	2
16	71	32	5	1
10	66	28	5	1
16	77	40	6	2
12	65	27	4	1
14	74	37	5	1
15	82	32	7	2
13	54	28	5	1
15	63	34	7	1
11	54	30	7	2
12	64	35	6	2
8	69	31	5	1
16	54	32	8	2
15	84	30	5	1
17	86	30	5	2
16	77	31	5	1
10	89	40	6	2
18	76	32	4	2
13	60	36	5	1
16	75	32	5	1
13	73	35	7	1
10	85	38	6	2
15	79	42	7	2
16	71	34	10	1
16	72	35	6	2
14	69	35	8	2
10	78	33	4	2
17	54	36	5	2
13	69	32	6	2
15	81	33	7	2
16	84	34	7	2
12	84	32	6	2
13	69	34	6	2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 3 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155460&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155460&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155460&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 time3 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Confusion Matrix (predicted in columns / actuals in rows)
C1C2
C11370
C2250

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 \tabularnewline
C1 & 137 & 0 \tabularnewline
C2 & 25 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155460&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]137[/C][C]0[/C][/ROW]
[ROW][C]C2[/C][C]25[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155460&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155460&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
C11370
C2250


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = quantiles ; par3 = 2 ; par4 = no ;
Parameters (R input):
par1 = 1 ; 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')
}