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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_regression_trees.wasp
Title produced by softwareRecursive Partitioning (Regression Trees)
Date of computationFri, 24 Dec 2010 15:56:33 +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/24/t1293206052uuzyfdfmvp871c9.htm/, Retrieved Tue, 30 Apr 2024 07:18:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=115159, Retrieved Tue, 30 Apr 2024 07:18:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact140

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
- RMP     [Recursive Partitioning (Regression Trees)] [Rec Part] [2010-12-24 15:56:33] [aedc5b8e4f26bdca34b1a0cf88d6dfa2] [Current]
- RMP       [Recursive Partitioning (Regression Trees)] [peer review] [2010-12-28 13:07:58] [8b7e5d4d87654725a776c7f35eb4752f]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	1	41	38	13	12	14
1	1	39	32	16	11	18
1	1	30	35	19	15	11
1	0	31	33	15	6	12
1	1	34	37	14	13	16
1	1	35	29	13	10	18
1	1	39	31	19	12	14
1	1	34	36	15	14	14
1	1	36	35	14	12	15
1	1	37	38	15	9	15
1	0	38	31	16	10	17
1	1	36	34	16	12	19
1	0	38	35	16	12	10
1	1	39	38	16	11	16
1	1	33	37	17	15	18
1	0	32	33	15	12	14
1	0	36	32	15	10	14
1	1	38	38	20	12	17
1	0	39	38	18	11	14
1	1	32	32	16	12	16
1	0	32	33	16	11	18
1	1	31	31	16	12	11
1	1	39	38	19	13	14
1	1	37	39	16	11	12
1	0	39	32	17	12	17
1	1	41	32	17	13	9
1	0	36	35	16	10	16
1	1	33	37	15	14	14
1	1	33	33	16	12	15
1	0	34	33	14	10	11
1	1	31	31	15	12	16
1	0	27	32	12	8	13
1	1	37	31	14	10	17
1	1	34	37	16	12	15
1	0	34	30	14	12	14
1	0	32	33	10	7	16
1	0	29	31	10	9	9
1	0	36	33	14	12	15
1	1	29	31	16	10	17
1	0	35	33	16	10	13
1	0	37	32	16	10	15
1	1	34	33	14	12	16
1	0	38	32	20	15	16
1	0	35	33	14	10	12
1	1	38	28	14	10	15
1	1	37	35	11	12	11
1	1	38	39	14	13	15
1	1	33	34	15	11	15
1	1	36	38	16	11	17
1	0	38	32	14	12	13
1	1	32	38	16	14	16
1	0	32	30	14	10	14
1	0	32	33	12	12	11
1	1	34	38	16	13	12
1	0	32	32	9	5	12
1	1	37	35	14	6	15
1	1	39	34	16	12	16
1	1	29	34	16	12	15
1	0	37	36	15	11	12
1	1	35	34	16	10	12
1	0	30	28	12	7	8
1	0	38	34	16	12	13
1	1	34	35	16	14	11
1	1	31	35	14	11	14
1	1	34	31	16	12	15
1	0	35	37	17	13	10
1	1	36	35	18	14	11
1	0	30	27	18	11	12
1	1	39	40	12	12	15
1	0	35	37	16	12	15
1	0	38	36	10	8	14
1	1	31	38	14	11	16
1	1	34	39	18	14	15
1	0	38	41	18	14	15
1	0	34	27	16	12	13
1	1	39	30	17	9	12
1	1	37	37	16	13	17
1	1	34	31	16	11	13
1	0	28	31	13	12	15
1	0	37	27	16	12	13
1	0	33	36	16	12	15
1	1	35	37	16	12	15
1	0	37	33	15	12	16
1	1	32	34	15	11	15
1	1	33	31	16	10	14
1	0	38	39	14	9	15
1	1	33	34	16	12	14
1	1	29	32	16	12	13
1	1	33	33	15	12	7
1	1	31	36	12	9	17
1	1	36	32	17	15	13
1	1	35	41	16	12	15
1	1	32	28	15	12	14
1	1	29	30	13	12	13
1	1	39	36	16	10	16
1	1	37	35	16	13	12
1	1	35	31	16	9	14
1	0	37	34	16	12	17
1	0	32	36	14	10	15
1	1	38	36	16	14	17
1	0	37	35	16	11	12
1	1	36	37	20	15	16
1	0	32	28	15	11	11
1	1	33	39	16	11	15
1	0	40	32	13	12	9
1	1	38	35	17	12	16
1	0	41	39	16	12	15
1	0	36	35	16	11	10
1	1	43	42	12	7	10
1	1	30	34	16	12	15
1	1	31	33	16	14	11
1	1	32	41	17	11	13
1	1	37	34	12	10	18
1	0	37	32	18	13	16
1	1	33	40	14	13	14
1	1	34	40	14	8	14
1	1	33	35	13	11	14
1	1	38	36	16	12	14
1	0	33	37	13	11	12
1	1	31	27	16	13	14
1	1	38	39	13	12	15
1	1	37	38	16	14	15
1	1	36	31	15	13	15
1	1	31	33	16	15	13
1	0	39	32	15	10	17
1	1	44	39	17	11	17
1	1	33	36	15	9	19
1	1	35	33	12	11	15
1	0	32	33	16	10	13
1	0	28	32	10	11	9
1	1	40	37	16	8	15
1	0	27	30	12	11	15
1	0	37	38	14	12	15
1	1	32	29	15	12	16
1	0	28	22	13	9	11
1	0	34	35	15	11	14
1	1	30	35	11	10	11
1	1	35	34	12	8	15
1	0	31	35	11	9	13
1	1	32	34	16	8	15
1	0	30	37	15	9	16
1	1	30	35	17	15	14
1	0	31	23	16	11	15
1	1	40	31	10	8	16
1	1	32	27	18	13	16
1	0	36	36	13	12	11
1	0	32	31	16	12	12
1	0	35	32	13	9	9
1	1	38	39	10	7	16
1	1	42	37	15	13	13
1	0	34	38	16	9	16
1	1	35	39	16	6	12
1	1	38	34	14	8	9
1	1	33	31	10	8	13
1	1	32	37	13	6	14
1	1	33	36	15	9	19
1	1	34	32	16	11	13
1	1	32	38	12	8	12
0	0	27	26	13	10	10
0	0	31	26	12	8	14
0	0	38	33	17	14	16
0	1	34	39	15	10	10
0	0	24	30	10	8	11
0	0	30	33	14	11	14
0	1	26	25	11	12	12
0	1	34	38	13	12	9
0	0	27	37	16	12	9
0	0	37	31	12	5	11
0	1	36	37	16	12	16
0	0	41	35	12	10	9
0	1	29	25	9	7	13
0	1	36	28	12	12	16
0	0	32	35	15	11	13
0	1	37	33	12	8	9
0	0	30	30	12	9	12
0	1	31	31	14	10	16
0	1	38	37	12	9	11
0	1	36	36	16	12	14
0	0	35	30	11	6	13
0	0	31	36	19	15	15
0	0	38	32	15	12	14
0	1	22	28	8	12	16
0	1	32	36	16	12	13
0	0	36	34	17	11	14
0	1	39	31	12	7	15
0	0	28	28	11	7	13
0	0	32	36	11	5	11
0	1	32	36	14	12	11
0	1	38	40	16	12	14
0	1	32	33	12	3	15
0	1	35	37	16	11	11
0	1	32	32	13	10	15
0	0	37	38	15	12	12
0	1	34	31	16	9	14
0	1	33	37	16	12	14
0	0	33	33	14	9	8
0	0	30	30	16	12	9
0	0	24	30	14	10	15
0	0	34	31	11	9	17
0	0	34	32	12	12	13
0	1	33	34	15	8	15
0	1	34	36	15	11	15
0	1	35	37	16	11	14
0	0	35	36	16	12	16
0	0	36	33	11	10	13
0	0	34	33	15	10	16
0	1	34	33	12	12	9
0	0	41	44	12	12	16
0	0	32	39	15	11	11
0	0	30	32	15	8	10
0	1	35	35	16	12	11
0	0	28	25	14	10	15
0	1	33	35	17	11	17
0	1	39	34	14	10	14
0	0	36	35	13	8	8
0	1	36	39	15	12	15
0	0	35	33	13	12	11
0	0	38	36	14	10	16
0	1	33	32	15	12	10
0	0	31	32	12	9	15
0	1	32	36	8	6	16
0	0	31	32	14	10	19
0	0	33	34	14	9	12
0	0	34	33	11	9	8
0	0	34	35	12	9	11
0	1	34	30	13	6	14
0	0	33	38	10	10	9
0	0	32	34	16	6	15
0	1	41	33	18	14	13
0	1	34	32	13	10	16
0	0	36	31	11	10	11
0	0	37	30	4	6	12
0	0	36	27	13	12	13
0	1	29	31	16	12	10
0	0	37	30	10	7	11
0	0	27	32	12	8	12
0	0	35	35	12	11	8
0	0	28	28	10	3	12
0	0	35	33	13	6	12
0	0	29	35	12	8	11
0	0	32	35	14	9	13
0	1	36	32	10	9	14
0	1	19	21	12	8	10
0	1	21	20	12	9	12
0	0	31	34	11	7	15
0	0	33	32	10	7	13
0	1	36	34	12	6	13
0	1	33	32	16	9	13
0	0	37	33	12	10	12
0	0	34	33	14	11	12
0	0	35	37	16	12	9
0	1	31	32	14	8	9
0	1	37	34	13	11	15
0	1	35	30	4	3	10
0	1	27	30	15	11	14
0	0	34	38	11	12	15
0	0	40	36	11	7	7
0	0	29	32	14	9	14
0	0	38	34	15	12	8
0	1	34	33	14	8	10
0	0	21	27	13	11	13
0	0	36	32	11	8	13
0	1	38	34	15	10	13
0	0	30	29	11	8	8
0	0	35	35	13	7	12
0	1	30	27	13	8	13
0	1	36	33	16	10	12
0	0	34	38	13	8	10
0	1	35	36	16	12	13
0	0	34	33	16	14	12
0	0	32	39	12	7	9
0	1	33	29	7	6	15
0	0	33	32	16	11	13
0	1	26	34	5	4	13
0	0	35	38	16	9	13
0	0	21	17	4	5	15
0	0	38	35	12	9	15
0	0	35	32	15	11	14
0	1	33	34	14	12	15
0	0	37	36	11	9	11
0	0	38	31	16	12	15
0	1	34	35	15	10	14
0	0	27	29	12	9	13
0	1	16	22	6	6	12
0	0	40	41	16	10	16
0	0	36	36	10	9	16
0	1	42	42	15	13	9
0	1	30	33	14	12	14

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115159&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115159&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115159&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 time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Model Performance
#Complexitysplitrelative errorCV errorCV S.D.
10.103011.010.012
20.03110.8970.9640.039
30.02320.8660.9730.046
40.01850.79610.052
50.0170.76110.058

\begin{tabular}{lllllllll}
\hline
Model Performance \tabularnewline
# & Complexity & split & relative error & CV error & CV S.D. \tabularnewline
1 & 0.103 & 0 & 1 & 1.01 & 0.012 \tabularnewline
2 & 0.031 & 1 & 0.897 & 0.964 & 0.039 \tabularnewline
3 & 0.023 & 2 & 0.866 & 0.973 & 0.046 \tabularnewline
4 & 0.018 & 5 & 0.796 & 1 & 0.052 \tabularnewline
5 & 0.01 & 7 & 0.761 & 1 & 0.058 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115159&T=1

[TABLE]
[ROW][C]Model Performance[/C][/ROW]
[ROW][C]#[/C][C]Complexity[/C][C]split[/C][C]relative error[/C][C]CV error[/C][C]CV S.D.[/C][/ROW]
[ROW][C]1[/C][C]0.103[/C][C]0[/C][C]1[/C][C]1.01[/C][C]0.012[/C][/ROW]
[ROW][C]2[/C][C]0.031[/C][C]1[/C][C]0.897[/C][C]0.964[/C][C]0.039[/C][/ROW]
[ROW][C]3[/C][C]0.023[/C][C]2[/C][C]0.866[/C][C]0.973[/C][C]0.046[/C][/ROW]
[ROW][C]4[/C][C]0.018[/C][C]5[/C][C]0.796[/C][C]1[/C][C]0.052[/C][/ROW]
[ROW][C]5[/C][C]0.01[/C][C]7[/C][C]0.761[/C][C]1[/C][C]0.058[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115159&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115159&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:

Model Performance
#Complexitysplitrelative errorCV errorCV S.D.
10.103011.010.012
20.03110.8970.9640.039
30.02320.8660.9730.046
40.01850.79610.052
50.0170.76110.058


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = No ;
Parameters (R input):
par1 = 1 ; par2 = No ;
R code (references can be found in the software module):
library(rpart)
library(partykit)
par1 <- as.numeric(par1)
autoprune <- function ( tree, method='Minimum CV'){
xerr <- tree$cptable[,'xerror']
cpmin.id <- which.min(xerr)
if (method == 'Minimum CV Error plus 1 SD'){
xstd <- tree$cptable[,'xstd']
errt <- xerr[cpmin.id] + xstd[cpmin.id]
cpSE1.min <- which.min( errt < xerr )
mycp <- (tree$cptable[,'CP'])[cpSE1.min]
}
if (method == 'Minimum CV') {
mycp <- (tree$cptable[,'CP'])[cpmin.id]
}
return (mycp)
}
conf.multi.mat <- function(true, new)
{
if ( all( is.na(match( levels(true),levels(new) ) )) )
stop ( 'conflict of vector levels')
multi.t <- list()
for (mylev in levels(true) ) {
true.tmp <- true
new.tmp <- new
left.lev <- levels (true.tmp)[- match(mylev,levels(true) ) ]
levels(true.tmp) <- list ( mylev = mylev, all = left.lev )
levels(new.tmp)  <- list ( mylev = mylev, all = left.lev )
curr.t <- conf.mat ( true.tmp , new.tmp )
multi.t[[mylev]] <- curr.t
multi.t[[mylev]]$precision <-
round( curr.t$conf[1,1] / sum( curr.t$conf[1,] ), 2 )
}
return (multi.t)
}
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
m <- rpart(as.data.frame(x1))
par2
if (par2 != 'No') {
mincp <- autoprune(m,method=par2)
print(mincp)
m <- prune(m,cp=mincp)
}
m$cptable
bitmap(file='test1.png')
plot(as.party(m),tp_args=list(id=FALSE))
dev.off()
bitmap(file='test2.png')
plotcp(m)
dev.off()
cbind(y=m$y,pred=predict(m),res=residuals(m))
myr <- residuals(m)
myp <- predict(m)
bitmap(file='test4.png')
op <- par(mfrow=c(2,2))
plot(myr,ylab='residuals')
plot(density(myr),main='Residual Kernel Density')
plot(myp,myr,xlab='predicted',ylab='residuals',main='Predicted vs Residuals')
plot(density(myp),main='Prediction Kernel Density')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Model Performance',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#',header=TRUE)
a<-table.element(a,'Complexity',header=TRUE)
a<-table.element(a,'split',header=TRUE)
a<-table.element(a,'relative error',header=TRUE)
a<-table.element(a,'CV error',header=TRUE)
a<-table.element(a,'CV S.D.',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$cptable[,1])) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,round(m$cptable[i,'CP'],3))
a<-table.element(a,m$cptable[i,'nsplit'])
a<-table.element(a,round(m$cptable[i,'rel error'],3))
a<-table.element(a,round(m$cptable[i,'xerror'],3))
a<-table.element(a,round(m$cptable[i,'xstd'],3))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')