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Author's title

Author*The author of this computation has been verified*
R Software ModulePatrick.Wessarwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationFri, 10 Dec 2010 09:35:29 +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/10/t12919737297ymbpbkcw5xxgj6.htm/, Retrieved Mon, 29 Apr 2024 09:45:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=107466, Retrieved Mon, 29 Apr 2024 09:45:24 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact234
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]
F   PD    [Kendall tau Correlation Matrix] [] [2010-12-10 09:35:29] [0bf4568947c4284a0258563e64d5d827] [Current]
-   PD      [Kendall tau Correlation Matrix] [] [2010-12-21 11:29:25] [504b6ff240ec7a3fcbc007044ae7a0bb]
Feedback Forum
2010-12-22 05:40:40 [f0479c8ad85b1406c7a3120008048c58] [reply
Opmerking: Het zijn niet exact dezelfde correlatiewaarden als die van bij Pearson. Inderdaad het is zo dat Kendall Tau minder gevoelig is voor outliers en een normaal verdeling is geen vereiste.

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Dataseries X:
101.76	101.82	107.34	93.63	99.85
102.37	101.68	107.34	93.63	99.91
102.38	101.68	107.34	93.63	99.87
102.86	102.45	107.34	96.13	99.86
102.87	102.45	107.34	96.13	100.10
102.92	102.45	107.34	96.13	100.10
102.95	102.45	107.34	96.13	100.12
103.02	102.45	107.34	96.13	99.95
104.08	102.45	112.60	96.13	99.94
104.16	102.52	112.60	96.13	100.18
104.24	102.52	112.60	96.13	100.31
104.33	102.85	112.60	96.13	100.65
104.73	102.85	112.61	96.13	100.65
104.86	102.85	112.61	96.13	100.69
105.03	103.25	112.61	96.13	101.26
105.62	103.25	112.61	98.73	101.26
105.63	103.25	112.61	98.73	101.38
105.63	103.25	112.61	98.73	101.38
105.94	104.45	112.61	98.73	101.38
106.61	104.45	112.61	98.73	101.44
107.69	104.45	118.65	98.73	101.40
107.78	104.80	118.65	98.73	101.40
107.93	104.80	118.65	98.73	100.58
108.48	105.29	118.65	98.73	100.58
108.14	105.29	114.29	98.73	100.58
108.48	105.29	114.29	98.73	100.59
108.48	105.29	114.29	98.73	100.81
108.89	106.04	114.29	101.67	100.75
108.93	105.94	114.29	101.67	100.75
109.21	105.94	114.29	101.67	100.96
109.47	105.94	114.29	101.67	101.31
109.80	106.28	114.29	101.67	101.64
111.73	106.48	123.33	101.67	101.46
111.85	107.19	123.33	101.67	101.73
112.12	108.14	123.33	101.67	101.73
112.15	108.22	123.33	101.67	101.64
112.17	108.22	123.33	101.67	101.77
112.67	108.61	123.33	101.67	101.74
112.80	108.61	123.33	101.67	101.89
113.44	108.61	123.33	107.94	101.89
113.53	108.61	123.33	107.94	101.93
114.53	109.06	123.33	107.94	101.93
114.51	109.06	123.33	107.94	102.32
115.05	112.93	123.33	107.94	102.41
116.67	115.84	129.03	107.94	103.58
117.07	118.57	128.76	107.94	104.12
116.92	118.57	128.76	107.94	104.10
117.00	118.86	128.76	107.94	104.15
117.02	118.98	128.76	107.94	104.15
117.35	119.27	128.76	107.94	104.16
117.36	119.39	128.76	107.94	102.94
117.82	119.49	128.76	110.30	103.07
117.88	119.59	128.76	110.30	103.04
118.24	120.12	128.76	110.30	103.06
118.50	120.14	128.76	110.30	103.05
118.80	120.14	128.76	110.30	102.95
119.76	120.14	132.63	110.30	102.95
120.09	120.14	132.63	110.30	103.05




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107466&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]2 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=107466&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107466&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Correlations for all pairs of data series (method=pearson)
cultuurbioscoopschouwburgeendagsactrhuur
cultuur10.9460.9740.9710.914
bioscoop0.94610.9150.9270.922
schouwburg0.9740.91510.920.907
eendagsactr0.9710.9270.9210.893
huur0.9140.9220.9070.8931

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & cultuur & bioscoop & schouwburg & eendagsactr & huur \tabularnewline
cultuur & 1 & 0.946 & 0.974 & 0.971 & 0.914 \tabularnewline
bioscoop & 0.946 & 1 & 0.915 & 0.927 & 0.922 \tabularnewline
schouwburg & 0.974 & 0.915 & 1 & 0.92 & 0.907 \tabularnewline
eendagsactr & 0.971 & 0.927 & 0.92 & 1 & 0.893 \tabularnewline
huur & 0.914 & 0.922 & 0.907 & 0.893 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107466&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]cultuur[/C][C]bioscoop[/C][C]schouwburg[/C][C]eendagsactr[/C][C]huur[/C][/ROW]
[ROW][C]cultuur[/C][C]1[/C][C]0.946[/C][C]0.974[/C][C]0.971[/C][C]0.914[/C][/ROW]
[ROW][C]bioscoop[/C][C]0.946[/C][C]1[/C][C]0.915[/C][C]0.927[/C][C]0.922[/C][/ROW]
[ROW][C]schouwburg[/C][C]0.974[/C][C]0.915[/C][C]1[/C][C]0.92[/C][C]0.907[/C][/ROW]
[ROW][C]eendagsactr[/C][C]0.971[/C][C]0.927[/C][C]0.92[/C][C]1[/C][C]0.893[/C][/ROW]
[ROW][C]huur[/C][C]0.914[/C][C]0.922[/C][C]0.907[/C][C]0.893[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107466&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107466&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=pearson)
cultuurbioscoopschouwburgeendagsactrhuur
cultuur10.9460.9740.9710.914
bioscoop0.94610.9150.9270.922
schouwburg0.9740.91510.920.907
eendagsactr0.9710.9270.9210.893
huur0.9140.9220.9070.8931







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
cultuur;bioscoop0.94630.99780.9761
p-value(0)(0)(0)
cultuur;schouwburg0.97440.97270.8796
p-value(0)(0)(0)
cultuur;eendagsactr0.97120.98140.9098
p-value(0)(0)(0)
cultuur;huur0.91390.92950.7929
p-value(0)(0)(0)
bioscoop;schouwburg0.91550.97070.884
p-value(0)(0)(0)
bioscoop;eendagsactr0.92650.98010.9192
p-value(0)(0)(0)
bioscoop;huur0.92230.92840.7923
p-value(0)(0)(0)
schouwburg;eendagsactr0.92030.93770.861
p-value(0)(0)(0)
schouwburg;huur0.90690.9270.7893
p-value(0)(0)(0)
eendagsactr;huur0.89350.91580.7879
p-value(0)(0)(0)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
cultuur;bioscoop & 0.9463 & 0.9978 & 0.9761 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
cultuur;schouwburg & 0.9744 & 0.9727 & 0.8796 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
cultuur;eendagsactr & 0.9712 & 0.9814 & 0.9098 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
cultuur;huur & 0.9139 & 0.9295 & 0.7929 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
bioscoop;schouwburg & 0.9155 & 0.9707 & 0.884 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
bioscoop;eendagsactr & 0.9265 & 0.9801 & 0.9192 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
bioscoop;huur & 0.9223 & 0.9284 & 0.7923 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
schouwburg;eendagsactr & 0.9203 & 0.9377 & 0.861 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
schouwburg;huur & 0.9069 & 0.927 & 0.7893 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
eendagsactr;huur & 0.8935 & 0.9158 & 0.7879 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107466&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]cultuur;bioscoop[/C][C]0.9463[/C][C]0.9978[/C][C]0.9761[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]cultuur;schouwburg[/C][C]0.9744[/C][C]0.9727[/C][C]0.8796[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]cultuur;eendagsactr[/C][C]0.9712[/C][C]0.9814[/C][C]0.9098[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]cultuur;huur[/C][C]0.9139[/C][C]0.9295[/C][C]0.7929[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]bioscoop;schouwburg[/C][C]0.9155[/C][C]0.9707[/C][C]0.884[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]bioscoop;eendagsactr[/C][C]0.9265[/C][C]0.9801[/C][C]0.9192[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]bioscoop;huur[/C][C]0.9223[/C][C]0.9284[/C][C]0.7923[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]schouwburg;eendagsactr[/C][C]0.9203[/C][C]0.9377[/C][C]0.861[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]schouwburg;huur[/C][C]0.9069[/C][C]0.927[/C][C]0.7893[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]eendagsactr;huur[/C][C]0.8935[/C][C]0.9158[/C][C]0.7879[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107466&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107466&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
cultuur;bioscoop0.94630.99780.9761
p-value(0)(0)(0)
cultuur;schouwburg0.97440.97270.8796
p-value(0)(0)(0)
cultuur;eendagsactr0.97120.98140.9098
p-value(0)(0)(0)
cultuur;huur0.91390.92950.7929
p-value(0)(0)(0)
bioscoop;schouwburg0.91550.97070.884
p-value(0)(0)(0)
bioscoop;eendagsactr0.92650.98010.9192
p-value(0)(0)(0)
bioscoop;huur0.92230.92840.7923
p-value(0)(0)(0)
schouwburg;eendagsactr0.92030.93770.861
p-value(0)(0)(0)
schouwburg;huur0.90690.9270.7893
p-value(0)(0)(0)
eendagsactr;huur0.89350.91580.7879
p-value(0)(0)(0)



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')