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Author

*The author of this computation has been verified*

R Software Module

rwasp_pairs.wasp

Title produced by software

Kendall tau Correlation Matrix

Date of computation

Mon, 03 Nov 2008 17:33:15 -0700

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/2008/Nov/04/t1225758838faxzsk7t9tjhpiu.htm/, Retrieved Wed, 15 May 2024 23:20:37 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=21418, Retrieved Wed, 15 May 2024 23:20:37 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

opdracht 5 eda part 2 q2

Estimated Impact

206

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Kendall tau Correlation Matrix] [Kendall Tau Corre...] [2007-10-26 13:10:27] [aa4de776bce95665a02a61eda10fcf15]
F   PD    [Kendall tau Correlation Matrix] [Kendall Tau Corre...] [2008-11-04 00:33:15] [3efbb18563b4564408d69b3c9a8e9a6e] [Current]

Feedback Forum

2008-11-08 15:21:41 [Jeroen Aerts] [reply] 
Je veronderstellingen zijn correct, maar je kan zelfs nog verder categoriseren... 
 
de afkortingen staan namelijk voor: 
Cyl: Cylinderinhoud 
Mpg:miles per gallon 
Hp: horse power 
... 
 
Doordat je ziet dat sommige sterren dezelfde vorm aannemen kan je dus veronderstellen dat het om hetzelfde type auto gaat. Dus kan je nog categoriseren in terreinwagens, stadswagen ...
2008-11-09 16:13:05 [2df1bcd103d52957f4a39bd4617794c8] [reply] 
Student trekt een vrij correct en uiterst uitgebreid besluit. 
 
We merken op dat elke spaak van een ster overeenkomt met een van de zeven variabelen. Zo tracht de student gelijkaardige vormen correct te groeperen. Om vervolgens verschillende categorien te onderschijden. 
2008-11-11 15:11:07 [Bernard Femont] [reply] 
Door een starplot te maken van deze 32 verschillende modellen, kunnen we ze goed vergelijken en vooral de gelijkenissen opzoeken. Zo valt het op dat we verschillende groepen kunnen vormen, waarbij de verschillende ‘stars’ gelijkaardig zijn.

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Dataseries X:

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Histogram

Boxplots

4.2	4.8	20.8	0.9	39.6
2.6	-4.2	17.1	0.85	36.1
3	1.6	22.3	0.83	34.4
3.8	5.2	25.1	0.84	33.4
4	9.2	27.7	0.85	34.8
3.5	4.6	24.9	0.83	33.7
4.1	10.6	29.5	0.83	36.3

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=21418&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]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=21418&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21418&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	3 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

Kendall tau rank correlations for all pairs of data series
pair	tau	p-value
tau( X1 , X2 )	0.714285714285714	0.0301587301587301
tau( X1 , X3 )	0.523809523809524	0.136111111111111
tau( X1 , X4 )	0.264628062012482	0.427262856745706
tau( X1 , X5 )	0.333333333333333	0.381349206349206
tau( X2 , X3 )	0.80952380952381	0.0107142857142857
tau( X2 , X4 )	-0.0529256124024963	0.873844698517373
tau( X2 , X5 )	0.0476190476190476	1
tau( X3 , X4 )	-0.264628062012482	0.427262856745706
tau( X3 , X5 )	-0.142857142857143	0.772619047619048
tau( X4 , X5 )	0.370479286817474	0.266379923342483

\begin{tabular}{lllllllll}
\hline
Kendall tau rank correlations for all pairs of data series \tabularnewline
pair & tau & p-value \tabularnewline
tau( X1 , X2 ) & 0.714285714285714 & 0.0301587301587301 \tabularnewline
tau( X1 , X3 ) & 0.523809523809524 & 0.136111111111111 \tabularnewline
tau( X1 , X4 ) & 0.264628062012482 & 0.427262856745706 \tabularnewline
tau( X1 , X5 ) & 0.333333333333333 & 0.381349206349206 \tabularnewline
tau( X2 , X3 ) & 0.80952380952381 & 0.0107142857142857 \tabularnewline
tau( X2 , X4 ) & -0.0529256124024963 & 0.873844698517373 \tabularnewline
tau( X2 , X5 ) & 0.0476190476190476 & 1 \tabularnewline
tau( X3 , X4 ) & -0.264628062012482 & 0.427262856745706 \tabularnewline
tau( X3 , X5 ) & -0.142857142857143 & 0.772619047619048 \tabularnewline
tau( X4 , X5 ) & 0.370479286817474 & 0.266379923342483 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=21418&T=1

[TABLE]
[ROW][C]Kendall tau rank correlations for all pairs of data series[/C][/ROW]
[ROW][C]pair[/C][C]tau[/C][C]p-value[/C][/ROW]
[ROW][C]tau( X1 , X2 )[/C][C]0.714285714285714[/C][C]0.0301587301587301[/C][/ROW]
[ROW][C]tau( X1 , X3 )[/C][C]0.523809523809524[/C][C]0.136111111111111[/C][/ROW]
[ROW][C]tau( X1 , X4 )[/C][C]0.264628062012482[/C][C]0.427262856745706[/C][/ROW]
[ROW][C]tau( X1 , X5 )[/C][C]0.333333333333333[/C][C]0.381349206349206[/C][/ROW]
[ROW][C]tau( X2 , X3 )[/C][C]0.80952380952381[/C][C]0.0107142857142857[/C][/ROW]
[ROW][C]tau( X2 , X4 )[/C][C]-0.0529256124024963[/C][C]0.873844698517373[/C][/ROW]
[ROW][C]tau( X2 , X5 )[/C][C]0.0476190476190476[/C][C]1[/C][/ROW]
[ROW][C]tau( X3 , X4 )[/C][C]-0.264628062012482[/C][C]0.427262856745706[/C][/ROW]
[ROW][C]tau( X3 , X5 )[/C][C]-0.142857142857143[/C][C]0.772619047619048[/C][/ROW]
[ROW][C]tau( X4 , X5 )[/C][C]0.370479286817474[/C][C]0.266379923342483[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=21418&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21418&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Kendall tau rank correlations for all pairs of data series
pair	tau	p-value
tau( X1 , X2 )	0.714285714285714	0.0301587301587301
tau( X1 , X3 )	0.523809523809524	0.136111111111111
tau( X1 , X4 )	0.264628062012482	0.427262856745706
tau( X1 , X5 )	0.333333333333333	0.381349206349206
tau( X2 , X3 )	0.80952380952381	0.0107142857142857
tau( X2 , X4 )	-0.0529256124024963	0.873844698517373
tau( X2 , X5 )	0.0476190476190476	1
tau( X3 , X4 )	-0.264628062012482	0.427262856745706
tau( X3 , X5 )	-0.142857142857143	0.772619047619048
tau( X4 , X5 )	0.370479286817474	0.266379923342483

Figure 1

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 12 ;

Parameters (R input):

R code (references can be found in the software module):

panel.tau <- function(x, y, digits=2, prefix='', cex.cor)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
rr <- cor.test(x, y, method='kendall')
r <- round(rr$p.value,2)
txt <- format(c(r, 0.123456789), digits=digits)[1]
txt <- paste(prefix, txt, sep='')
if(missing(cex.cor)) cex <- 0.5/strwidth(txt)
text(0.5, 0.5, txt, cex = cex)
}
panel.hist <- function(x, ...)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Kendall tau rank correlations for all pairs of data series',3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'pair',1,TRUE)
a<-table.element(a,'tau',1,TRUE)
a<-table.element(a,'p-value',1,TRUE)
a<-table.row.end(a)
n <- length(y[,1])
n
cor.test(y[1,],y[2,],method='kendall')
for (i in 1:(n-1))
{
for (j in (i+1):n)
{
a<-table.row.start(a)
dum <- paste('tau(',dimnames(t(x))[[2]][i])
dum <- paste(dum,',')
dum <- paste(dum,dimnames(t(x))[[2]][j])
dum <- paste(dum,')')
a<-table.element(a,dum,header=TRUE)
r <- cor.test(y[i,],y[j,],method='kendall')
a<-table.element(a,r$estimate)
a<-table.element(a,r$p.value)
a<-table.row.end(a)
}
}
a<-table.end(a)
table.save(a,file='mytable.tab')

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Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code