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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_regression_trees1.wasp

Title produced by software

Recursive Partitioning (Regression Trees)

Date of computation

Sat, 18 Dec 2010 11:47:49 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/18/t1292672917jf7cxj3j1ec5812.htm/, Retrieved Tue, 30 Apr 2024 04:51:49 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=111858, Retrieved Tue, 30 Apr 2024 04:51:49 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

155

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]
-   PD  [Recursive Partitioning (Regression Trees)] [p_Stress_RP1] [2010-12-11 13:00:40] [19f9551d4d95750ef21e9f3cf8fe2131]
-   P     [Recursive Partitioning (Regression Trees)] [p_Stress_RP2] [2010-12-11 15:34:11] [19f9551d4d95750ef21e9f3cf8fe2131]
-           [Recursive Partitioning (Regression Trees)] [p_Stress_RP2b] [2010-12-11 16:34:56] [19f9551d4d95750ef21e9f3cf8fe2131]
-   P           [Recursive Partitioning (Regression Trees)] [p_Stress_RP2a] [2010-12-18 11:47:49] [fca744d17b21beb005bf086e7071b2bb] [Current]

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Dataseries X:

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23	10	53	7	12	2	4	0	0
21	6	86	4	11	4	3	0	0
21	13	66	6	14	7	5	0	0
21	12	67	5	12	3	3	0	1
24	8	76	4	21	7	6	0	0
22	6	78	3	12	2	5	0	0
21	10	53	5	22	7	6	0	0
22	10	80	6	11	2	6	0	0
21	9	74	5	10	1	5	0	0
20	9	76	6	13	2	5	0	0
22	7	79	7	10	6	3	0	1
21	5	54	6	8	1	5	0	0
21	14	67	7	15	1	7	0	1
23	6	87	6	10	1	5	0	0
22	10	58	4	14	2	5	0	1
23	10	75	6	14	2	3	0	1
22	7	88	4	11	2	5	0	0
24	10	64	5	10	1	6	0	1
23	8	57	3	13	7	5	0	0
21	6	66	3	7	1	2	0	1
23	10	54	4	12	2	5	0	0
23	12	56	5	14	4	4	0	0
21	7	86	3	11	2	6	0	1
20	15	80	7	9	1	3	0	0
32	8	76	7	11	1	5	0	1
22	10	69	4	15	5	4	0	0
21	13	67	4	13	2	5	0	1
21	8	80	5	9	1	2	0	0
21	11	54	6	15	3	2	0	1
22	7	71	5	10	1	5	0	0
21	9	84	4	11	2	2	0	0
21	10	74	6	13	5	2	0	1
21	8	71	5	8	2	2	0	1
22	15	63	5	20	6	5	0	1
21	9	71	6	12	4	5	0	1
21	7	76	2	10	1	1	0	0
21	11	69	6	10	3	5	0	1
21	9	74	7	9	6	2	0	1
23	8	75	5	14	7	6	0	0
21	8	54	5	8	4	1	0	1
23	12	69	5	11	5	3	0	0
23	13	68	6	13	3	2	0	0
21	9	75	4	11	2	5	0	0
21	11	75	6	11	2	3	0	1
20	8	72	5	10	2	4	0	0
21	10	67	5	14	2	3	0	1
21	13	63	3	18	1	6	0	1
22	12	62	4	14	2	4	0	0
21	12	63	4	11	1	5	0	1
21	9	76	2	12	2	2	0	0
22	8	74	3	13	2	5	0	0
20	9	67	6	9	5	5	0	0
22	12	73	5	10	5	3	0	1
22	12	70	6	15	2	5	0	0
21	16	53	2	20	1	7	0	1
23	11	77	3	12	1	4	0	1
22	13	77	6	12	2	2	0	0
24	10	52	3	14	3	3	0	0
23	9	54	6	13	7	6	0	0
21	14	80	6	11	4	7	1	1
22	13	66	4	17	4	4	1	0
22	12	73	7	12	1	4	1	1
21	9	63	6	13	2	4	1	0
21	9	69	3	14	2	5	1	1
21	10	67	7	13	2	2	1	1
21	8	54	2	15	5	3	1	0
20	9	81	4	13	1	3	1	0
22	9	69	6	10	6	4	1	1
22	11	84	4	11	2	3	1	1
22	7	70	1	13	2	4	1	0
23	11	69	4	17	4	6	1	0
21	9	77	7	13	6	2	1	1
23	11	54	4	9	2	4	1	1
22	9	79	4	11	2	5	1	1
21	8	30	4	10	2	2	1	1
21	9	71	6	9	1	1	1	0
20	8	73	2	12	1	2	1	1
24	9	72	3	12	2	5	1	0
24	10	77	4	13	2	4	1	0
21	9	75	4	13	3	4	1	1
20	17	70	4	22	3	6	1	0
21	7	73	6	13	5	1	1	0
21	11	54	2	15	2	4	1	0
21	9	77	4	13	5	5	1	0
21	10	82	3	15	3	2	1	0
22	11	80	7	10	1	3	1	0
22	8	80	4	11	2	3	1	0
21	12	69	5	16	2	6	1	0
22	10	78	6	11	1	5	1	0
21	7	81	5	11	2	4	1	1
23	9	76	4	10	2	4	1	1
21	7	76	5	10	5	5	1	0
22	12	73	4	16	5	5	1	1
22	8	85	5	12	2	6	1	0
22	13	66	7	11	3	6	1	1
20	9	79	7	16	5	5	1	0
21	15	68	4	19	5	7	1	1
21	8	76	6	11	6	5	1	0
22	14	54	4	15	2	5	1	1
25	14	46	1	24	7	7	1	0
22	9	82	3	14	1	5	1	0
22	13	74	6	15	1	6	1	0
21	11	88	7	11	6	6	1	0
22	10	38	6	15	6	4	1	1
21	6	76	6	12	2	5	1	0
24	8	86	6	10	1	1	1	1
23	10	54	4	14	2	6	1	0
23	10	69	1	9	1	5	1	0
22	10	90	3	15	2	2	1	0
22	12	54	7	15	1	1	1	0
25	10	76	2	14	3	5	1	0
23	9	89	7	11	3	6	1	0
22	9	76	4	8	6	5	1	0
21	11	79	5	11	4	5	1	0
21	7	90	6	8	1	4	1	1
22	7	74	6	10	2	2	1	0
22	5	81	5	11	5	3	1	0
21	9	72	5	13	6	3	1	0
0	11	71	4	11	3	5	1	1
21	15	66	2	20	5	3	1	1
22	9	77	2	10	3	2	1	0
21	9	74	4	12	2	2	1	1
24	8	82	4	14	3	3	1	0
21	13	54	6	23	2	6	1	1
23	10	63	5	14	5	5	1	1
23	13	54	5	16	5	6	1	0
22	9	64	6	11	7	2	1	0
21	11	69	5	12	4	5	1	1
21	8	84	7	14	5	5	1	1
21	10	86	5	12	1	1	1	0
21	9	77	3	12	4	4	1	1
22	8	89	5	11	1	2	1	0
20	8	76	1	12	4	2	1	0
21	13	60	5	13	6	7	1	1
23	11	79	7	17	7	6	1	0
32	8	76	7	11	1	5	0	1
22	12	72	6	12	3	5	1	0
24	15	69	4	19	5	5	0	0
21	11	54	2	15	2	4	1	0
22	10	69	6	14	4	3	1	0
22	5	81	5	11	5	3	1	0
23	11	84	1	9	1	3	1	0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111858&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111858&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111858&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Confusion Matrix (predicted in columns / actuals in rows)
	C1	C2	C3
C1	61	0	8
C2	21	0	18
C3	13	0	21

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 & C3 \tabularnewline
C1 & 61 & 0 & 8 \tabularnewline
C2 & 21 & 0 & 18 \tabularnewline
C3 & 13 & 0 & 21 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111858&T=1

[TABLE]
[ROW][C]Confusion Matrix (predicted in columns / actuals in rows)[/C][/ROW]
[ROW][C][/C][C]C1[/C][C]C2[/C][C]C3[/C][/ROW]
[ROW][C]C1[/C][C]61[/C][C]0[/C][C]8[/C][/ROW]
[ROW][C]C2[/C][C]21[/C][C]0[/C][C]18[/C][/ROW]
[ROW][C]C3[/C][C]13[/C][C]0[/C][C]21[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111858&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111858&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)
	C1	C2	C3
C1	61	0	8
C2	21	0	18
C3	13	0	21

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 2 ; par2 = quantiles ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 2 ; par2 = quantiles ; par3 = 3 ; 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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code