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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationTue, 21 Dec 2010 14:03:59 +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/21/t1292940115moq5uejx9zgutp4.htm/, Retrieved Thu, 09 May 2024 19:33:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113585, Retrieved Thu, 09 May 2024 19:33:41 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact181
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 18:04:16] [b98453cac15ba1066b407e146608df68]
- RMPD  [Kendall tau Correlation Matrix] [WS 10 - Kendall T...] [2010-12-10 16:36:23] [033eb2749a430605d9b2be7c4aac4a0c]
-         [Kendall tau Correlation Matrix] [] [2010-12-13 18:16:57] [d7b28a0391ab3b2ddc9f9fba95a43f33]
-    D        [Kendall tau Correlation Matrix] [] [2010-12-21 14:03:59] [44163a3390d803b6e1dc8c2f0815c192] [Current]
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Dataseries X:
300	2,26	591.000
302	2,57	589.000
400	3,07	584.000
392	2,76	573.000
373	2,51	567.000
379	2,87	569.000
303	3,14	621.000
324	3,11	629.000
353	3,16	628.000
392	2,47	612.000
327	2,57	595.000
376	2,89	597.000
329	2,63	593.000
359	2,38	590.000
413	1,69	580.000
338	1,96	574.000
422	2,19	573.000
390	1,87	573.000
370	1,60	620.000
367	1,63	626.000
406	1,22	620.000
418	1,21	588.000
346	1,49	566.000
350	1,64	557.000
330	1,66	561.000
318	1,77	549.000
382	1,82	532.000
337	1,78	526.000
372	1,28	511.000
422	1,29	499.000
428	1,37	555.000
426	1,12	565.000
396	1,51	542.000
458	2,24	527.000
315	2,94	510.000
337	3,09	514.000
386	3,46	517.000
352	3,64	508.000
383	4,39	493.000
439	4,15	490.000
397	5,21	469.000
453	5,80	478.000
363	5,91	528.000
365	5,39	534.000
474	5,46	518.000
373	4,72	506.000
403	3,14	502.000
384	2,63	516.000
364	2,32	528.000
361	1,93	533.000
419	0,62	536.000
352	0,60	537.000
363	-0,37	524.000
410	-1,10	536.000
361	-1,68	587.000
383	-0,78	597.000
342	-1,19	581.000
369	-0,79	564.000
361	-0,12	558.000
317	0,26	575.000
386	0,62	580.000
318	0,70	575.000
407	1,66	563.000
393	1,80	552.000
404	2,27	537.000
498	2,46	545.000
438	2,57	601.000




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 1 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113585&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113585&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113585&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'George Udny Yule' @ 72.249.76.132







Correlations for all pairs of data series (method=kendall)
BouwvergunningenInflatieWerklozen
Bouwvergunningen10.016-0.181
Inflatie0.0161-0.163
Werklozen-0.181-0.1631

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & Bouwvergunningen & Inflatie & Werklozen \tabularnewline
Bouwvergunningen & 1 & 0.016 & -0.181 \tabularnewline
Inflatie & 0.016 & 1 & -0.163 \tabularnewline
Werklozen & -0.181 & -0.163 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113585&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]Bouwvergunningen[/C][C]Inflatie[/C][C]Werklozen[/C][/ROW]
[ROW][C]Bouwvergunningen[/C][C]1[/C][C]0.016[/C][C]-0.181[/C][/ROW]
[ROW][C]Inflatie[/C][C]0.016[/C][C]1[/C][C]-0.163[/C][/ROW]
[ROW][C]Werklozen[/C][C]-0.181[/C][C]-0.163[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113585&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113585&T=1

As an alternative you can also use a QR Code:  

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

Correlations for all pairs of data series (method=kendall)
BouwvergunningenInflatieWerklozen
Bouwvergunningen10.016-0.181
Inflatie0.0161-0.163
Werklozen-0.181-0.1631







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Bouwvergunningen;Inflatie0.14710.03510.0164
p-value(0.235)(0.7777)(0.8455)
Bouwvergunningen;Werklozen-0.2945-0.2596-0.1814
p-value(0.0156)(0.0339)(0.0308)
Inflatie;Werklozen-0.3533-0.2613-0.1635
p-value(0.0034)(0.0327)(0.0513)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
Bouwvergunningen;Inflatie & 0.1471 & 0.0351 & 0.0164 \tabularnewline
p-value & (0.235) & (0.7777) & (0.8455) \tabularnewline
Bouwvergunningen;Werklozen & -0.2945 & -0.2596 & -0.1814 \tabularnewline
p-value & (0.0156) & (0.0339) & (0.0308) \tabularnewline
Inflatie;Werklozen & -0.3533 & -0.2613 & -0.1635 \tabularnewline
p-value & (0.0034) & (0.0327) & (0.0513) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113585&T=2

[TABLE]
[ROW][C]Correlations for all pairs of data series with p-values[/C][/ROW]
[ROW][C]pair[/C][C]Pearson r[/C][C]Spearman rho[/C][C]Kendall tau[/C][/ROW]
[ROW][C]Bouwvergunningen;Inflatie[/C][C]0.1471[/C][C]0.0351[/C][C]0.0164[/C][/ROW]
[ROW][C]p-value[/C][C](0.235)[/C][C](0.7777)[/C][C](0.8455)[/C][/ROW]
[ROW][C]Bouwvergunningen;Werklozen[/C][C]-0.2945[/C][C]-0.2596[/C][C]-0.1814[/C][/ROW]
[ROW][C]p-value[/C][C](0.0156)[/C][C](0.0339)[/C][C](0.0308)[/C][/ROW]
[ROW][C]Inflatie;Werklozen[/C][C]-0.3533[/C][C]-0.2613[/C][C]-0.1635[/C][/ROW]
[ROW][C]p-value[/C][C](0.0034)[/C][C](0.0327)[/C][C](0.0513)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113585&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113585&T=2

As an alternative you can also use a QR Code:  

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

Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Bouwvergunningen;Inflatie0.14710.03510.0164
p-value(0.235)(0.7777)(0.8455)
Bouwvergunningen;Werklozen-0.2945-0.2596-0.1814
p-value(0.0156)(0.0339)(0.0308)
Inflatie;Werklozen-0.3533-0.2613-0.1635
p-value(0.0034)(0.0327)(0.0513)



Parameters (Session):
par1 = kendall ;
Parameters (R input):
par1 = kendall ;
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=par1)
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$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
n <- length(y[,1])
n
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ',header=TRUE)
for (i in 1:n) {
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
for (j in 1:n) {
r <- cor.test(y[i,],y[j,],method=par1)
a<-table.element(a,round(r$estimate,3))
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Correlations for all pairs of data series with p-values',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'pair',1,TRUE)
a<-table.element(a,'Pearson r',1,TRUE)
a<-table.element(a,'Spearman rho',1,TRUE)
a<-table.element(a,'Kendall tau',1,TRUE)
a<-table.row.end(a)
cor.test(y[1,],y[2,],method=par1)
for (i in 1:(n-1))
{
for (j in (i+1):n)
{
a<-table.row.start(a)
dum <- paste(dimnames(t(x))[[2]][i],';',dimnames(t(x))[[2]][j],sep='')
a<-table.element(a,dum,header=TRUE)
rp <- cor.test(y[i,],y[j,],method='pearson')
a<-table.element(a,round(rp$estimate,4))
rs <- cor.test(y[i,],y[j,],method='spearman')
a<-table.element(a,round(rs$estimate,4))
rk <- cor.test(y[i,],y[j,],method='kendall')
a<-table.element(a,round(rk$estimate,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=T)
a<-table.element(a,paste('(',round(rp$p.value,4),')',sep=''))
a<-table.element(a,paste('(',round(rs$p.value,4),')',sep=''))
a<-table.element(a,paste('(',round(rk$p.value,4),')',sep=''))
a<-table.row.end(a)
}
}
a<-table.end(a)
table.save(a,file='mytable1.tab')