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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, 09 Dec 2010 21:27:00 +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/09/t1291929956awxdfo56zv7luh1.htm/, Retrieved Mon, 29 Apr 2024 00:31:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=107422, Retrieved Mon, 29 Apr 2024 00:31:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact233

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]
- R PD    [Recursive Partitioning (Regression Trees)] [recursive partiti...] [2010-12-09 21:27:00] [9ea95e194e0eb2a674315798620d5bc6] [Current]
-   P       [Recursive Partitioning (Regression Trees)] [Recursive partiti...] [2010-12-09 21:41:03] [97ad38b1c3b35a5feca8b85f7bc7b3ff]
- R PD        [Recursive Partitioning (Regression Trees)] [] [2011-12-11 11:24:23] [06c08141d7d783218a8164fd2ea166f2]
- R P           [Recursive Partitioning (Regression Trees)] [Cross Validation] [2011-12-13 11:01:21] [d1ce18d003fa52f731d1c3ce8b58d5f9]
-   PD            [Recursive Partitioning (Regression Trees)] [] [2011-12-20 21:27:44] [f1de53e71fac758e9834be8effee591f]
- R PD        [Recursive Partitioning (Regression Trees)] [] [2011-12-12 18:28:46] [ec2187f7727da5d5d939740b21b8b68a]
- R           [Recursive Partitioning (Regression Trees)] [WS10-Cross valida...] [2011-12-13 13:58:17] [69d59b79aaf660457acc70a0ef0bfdab]
- R           [Recursive Partitioning (Regression Trees)] [] [2011-12-13 18:04:19] [9401a40688cf36283be626153bc5a38b]
- R PD      [Recursive Partitioning (Regression Trees)] [] [2011-12-11 11:02:45] [06c08141d7d783218a8164fd2ea166f2]
- R           [Recursive Partitioning (Regression Trees)] [] [2011-12-12 17:55:35] [ec2187f7727da5d5d939740b21b8b68a]
- R           [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2011-12-13 10:43:17] [d1ce18d003fa52f731d1c3ce8b58d5f9]
- R         [Recursive Partitioning (Regression Trees)] [WS10-Recursive Pa...] [2011-12-13 13:32:58] [69d59b79aaf660457acc70a0ef0bfdab]
- R         [Recursive Partitioning (Regression Trees)] [] [2011-12-13 17:35:19] [9401a40688cf36283be626153bc5a38b]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	15	10	77	5	4	15	11	12	13	6
0	12	20	63	6	4	9	12	7	11	4
0	15	16	73	4	10	12	12	13	14	6
0	12	10	76	6	6	15	11	11	12	5
0	14	8	90	3	5	17	11	16	12	5
0	8	14	67	10	8	14	10	10	6	4
1	11	19	69	8	9	9	11	15	10	5
1	15	15	70	3	6	12	9	5	11	3
0	4	23	54	4	8	11	10	4	10	2
0	13	9	54	3	11	13	12	7	12	5
1	19	12	76	5	6	16	12	15	15	6
1	10	14	75	5	8	16	12	5	13	6
1	15	13	76	6	11	15	13	16	18	8
0	6	11	80	5	5	10	9	15	11	6
1	7	11	89	3	10	16	12	13	12	3
0	14	10	73	4	7	12	12	13	13	6
0	16	12	74	8	7	15	12	15	14	6
1	16	18	78	8	13	13	12	15	16	7
1	14	12	76	8	10	18	13	10	16	8
0	15	10	69	5	8	13	11	17	16	6
1	14	15	74	8	6	17	12	14	15	7
1	12	15	82	2	8	14	12	9	13	4
0	9	12	77	0	7	13	15	6	8	4
1	12	9	84	5	5	13	11	11	14	2
1	14	11	75	2	9	15	12	13	15	6
1	12	15	54	7	9	13	10	12	13	6
1	14	16	79	5	11	15	11	10	16	6
1	10	17	79	2	11	13	13	4	13	6
1	14	12	69	12	11	14	6	13	12	6
1	16	11	88	7	9	13	12	15	15	7
1	10	13	57	0	7	16	12	8	11	4
1	8	9	69	2	6	14	10	10	14	3
1	12	11	86	3	6	18	12	8	13	5
1	11	9	65	0	6	15	12	7	13	6
0	8	20	66	9	5	9	11	9	12	4
0	13	8	54	2	4	16	9	14	14	6
1	11	12	85	3	10	16	10	5	13	3
0	12	10	79	1	8	17	12	7	12	3
0	16	11	84	10	6	13	12	16	14	6
1	16	13	70	1	5	17	11	14	15	6
1	13	13	54	4	9	15	12	16	16	6
1	14	13	70	6	10	14	11	15	15	8
0	5	15	54	6	6	10	14	4	5	2
0	14	12	69	4	9	13	10	12	15	6
1	13	13	68	4	10	11	10	8	8	4
1	16	13	68	7	6	11	11	17	16	7
0	14	9	71	7	6	16	11	15	16	6
0	15	9	71	7	6	16	11	16	14	6
1	15	14	66	0	13	11	10	12	16	6
1	11	9	67	3	8	15	10	12	14	5
1	15	9	71	8	10	15	12	13	13	6
1	16	15	54	8	5	12	11	14	14	6
1	13	10	76	10	8	17	8	14	14	5
0	11	13	77	11	6	15	12	15	12	6
0	12	8	71	6	9	16	10	14	13	7
1	12	15	69	2	9	14	7	11	15	5
1	10	13	73	6	7	17	11	13	15	6
1	8	24	46	1	20	10	7	4	13	6
0	9	11	66	5	8	11	11	8	10	4
1	12	13	77	4	8	15	8	13	13	5
0	14	12	77	6	7	15	11	15	14	6
1	12	22	70	6	7	7	12	15	13	6
0	11	11	86	4	10	17	8	8	13	4
0	14	15	38	1	5	14	14	17	18	6
0	7	7	66	6	8	18	14	12	12	4
0	16	14	75	7	9	14	11	13	14	7
1	16	19	80	7	9	12	12	14	16	8
0	11	10	64	2	20	14	14	7	13	6
1	16	9	80	7	6	9	9	16	16	6
1	13	12	86	8	10	14	13	11	15	6
1	11	16	54	5	11	11	8	10	14	5
1	13	13	74	4	7	16	11	14	13	6
1	14	11	88	2	12	17	9	19	12	6
1	15	12	85	0	12	16	12	14	16	4
0	10	11	63	7	8	12	7	8	9	5
1	15	13	81	0	6	15	11	15	15	8
0	11	13	81	5	6	15	12	8	16	6
1	11	10	74	3	9	15	11	8	12	6
1	6	11	80	3	5	16	12	6	11	2
1	11	9	80	3	11	16	9	7	13	2
0	12	13	60	3	6	11	11	16	13	4
0	13	15	65	7	6	15	13	15	14	6
1	12	14	62	6	10	12	12	10	15	6
0	8	14	63	3	8	14	12	8	14	5
1	9	11	89	0	7	15	11	9	12	4
1	10	10	76	2	8	17	12	8	16	4
1	16	11	81	0	9	19	12	14	14	6
1	15	12	72	9	8	15	11	14	13	5
0	14	14	84	10	10	16	11	14	12	6
1	12	14	76	3	13	14	8	15	13	7
1	12	21	76	7	7	16	9	7	12	6
1	10	14	78	3	7	15	11	7	9	4
1	12	13	72	6	7	15	12	12	13	4
0	8	11	81	5	8	17	13	7	10	3
1	16	12	72	0	9	12	12	12	15	8
1	11	12	78	0	9	18	6	6	9	4
1	12	11	79	4	8	13	12	10	13	4
1	9	14	52	0	7	14	11	12	13	5
0	14	13	67	0	6	14	13	13	13	5
0	15	13	74	7	8	14	11	14	15	7
0	8	12	73	3	8	12	12	8	13	4
1	12	14	69	9	4	14	10	14	14	5
0	10	12	67	4	8	12	10	10	11	5
1	16	12	76	4	10	15	11	14	15	8
1	17	12	77	15	7	11	11	15	14	5
0	8	18	63	7	8	11	11	10	15	2
1	9	11	84	8	7	15	9	6	12	5
1	8	15	90	2	10	14	7	9	15	4
0	11	13	75	8	9	15	11	11	14	5
1	16	11	76	7	8	16	12	16	16	7
0	13	11	75	3	8	12	12	14	14	6
1	5	22	53	3	5	14	15	8	12	3
1	15	10	87	6	8	18	11	16	11	5
1	15	11	78	8	9	14	10	16	13	6
1	12	15	54	5	11	13	13	14	12	5
0	12	14	58	6	7	14	13	12	12	6
1	16	11	80	10	8	14	11	16	16	7
1	12	10	74	0	4	17	12	15	13	6
1	10	14	56	5	16	12	12	11	12	6
1	12	14	82	0	9	16	12	6	14	5
1	4	11	64	0	16	15	8	6	4	4
0	11	15	67	5	12	10	5	16	14	6
0	16	11	75	10	8	13	11	16	15	6
0	7	10	69	0	4	15	12	8	12	3
1	9	10	72	5	11	16	12	11	11	4
0	14	16	71	6	11	15	11	12	12	4
1	11	12	54	1	8	14	12	13	11	4
1	10	14	68	5	8	11	10	11	12	5
0	6	15	54	3	12	13	7	9	11	4
1	14	10	71	3	8	17	12	15	13	6
1	11	12	53	6	6	14	12	11	12	6
1	11	15	54	2	8	16	9	12	12	4
0	9	12	71	5	6	15	11	15	15	7
1	16	11	69	6	14	12	12	8	14	4
0	7	10	30	2	10	16	12	7	12	4
0	8	20	53	3	5	8	11	10	12	4
0	10	19	68	7	8	9	11	9	12	4
1	14	17	69	6	12	13	12	13	13	5
1	9	8	54	3	11	19	12	11	11	4
1	13	17	66	6	8	11	11	12	13	7
0	13	11	79	9	8	15	12	5	12	3
0	12	13	67	2	9	11	12	12	14	5
0	11	9	74	5	6	15	8	14	15	5
0	10	10	86	10	5	16	15	15	15	6
1	12	13	63	9	8	15	11	14	13	5
1	14	16	69	8	7	12	11	13	16	6
0	11	12	73	8	4	16	6	14	17	6
0	13	14	69	5	9	15	13	14	13	3
0	14	11	71	9	5	13	12	15	14	6
1	13	13	77	9	9	14	12	13	13	5
1	16	15	74	14	12	11	12	14	16	8
1	13	14	82	5	6	15	12	11	13	6
1	12	14	54	12	4	16	12	14	14	4
1	9	14	54	6	6	14	10	11	13	3
1	14	10	80	6	7	13	12	8	14	4

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 4 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107422&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107422&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107422&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 time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Confusion Matrix (predicted in columns / actuals in rows)
C1C2C3
C146141
C219365
C321913

\begin{tabular}{lllllllll}
\hline
Confusion Matrix (predicted in columns / actuals in rows) \tabularnewline
 & C1 & C2 & C3 \tabularnewline
C1 & 46 & 14 & 1 \tabularnewline
C2 & 19 & 36 & 5 \tabularnewline
C3 & 2 & 19 & 13 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107422&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]46[/C][C]14[/C][C]1[/C][/ROW]
[ROW][C]C2[/C][C]19[/C][C]36[/C][C]5[/C][/ROW]
[ROW][C]C3[/C][C]2[/C][C]19[/C][C]13[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107422&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107422&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)
C1C2C3
C146141
C219365
C321913


Figures (Output of Computation)

Input Parameters & R Code

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')
}