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Author*The author of this computation has been verified*
R Software Modulerwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationThu, 06 Dec 2012 13:19:41 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/06/t1354818016rnsznh75yu5j30t.htm/, Retrieved Fri, 19 Apr 2024 22:51:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197198, Retrieved Fri, 19 Apr 2024 22:51:37 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103
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] [WS10 - kendall tau] [2012-12-06 18:19:41] [f931cc80137eae2a7bb893d4ecca5b17] [Current]
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Dataseries X:
1	521	18308	185	4.041	7.2
1	367	1148	600	0.55	8.5
1	443	18068	372	3.665	5.7
1	365	7729	142	2.351	7.3
1	614	100484	432	29.76	7.5
1	385	16728	290	3.294	5
1	286	14630	346	3.287	6.7
1	397	4008	328	0.666	6.2
1	764	38927	354	12.938	7.3
1	427	22322	266	6.478	5
1	153	3711	320	1.108	2.8
1	231	3136	197	1.007	6.1
1	524	50508	266	11.431	7.1
1	328	28886	173	5.544	5.9
1	240	16996	190	2.777	4.6
1	286	13035	239	2.478	4.4
1	285	12973	190	3.685	7.4
1	569	16309	241	4.22	7.1
1	96	5227	189	1.228	7.5
1	498	19235	358	4.781	5.9
1	481	44487	315	6.016	9
1	468	44213	303	9.295	9.2
1	177	23619	228	4.375	5.1
1	198	9106	134	2.573	8.6
1	458	24917	189	5.117	6.6
1	108	3872	196	0.799	6.9
1	246	8945	183	1.578	2.7
1	291	2373	417	1.202	5.5
1	68	7128	233	1.109	7.2
1	311	23624	349	7.73	6.6
1	606	5242	284	1.515	6.9
1	512	92629	499	17.99	7.2
1	426	28795	231	6.629	5.8
1	47	4487	143	0.639	4.1
1	265	48799	249	10.847	6.4
1	370	14067	195	3.146	6.7
1	312	12693	288	2.842	6
1	222	62184	229	11.882	6.9
1	280	9153	287	1.003	8.5
1	759	14250	224	3.487	6.2
1	114	3680	161	0.696	3.4
1	419	18063	221	4.877	6.6
1	435	65112	237	16.987	6.6
1	186	11340	220	1.723	4.9
1	87	4553	185	0.563	6.4
1	188	28960	260	6.187	5.8
1	303	19201	261	4.867	6.3
1	102	7533	118	1.793	10.5
1	127	26343	268	4.892	5.4
1	251	1641	300	0.454	5.1
0	205	145360	237	10.379	6.8
0	453	9066420	240	82.422	5.6
0	320	1038933	185	16.491	3.8
0	405	2739420	201	60.876	8.2
0	89	61620	193	0.474	4.1
0	74	827530	254	7.523	2.8
0	101	534100	230	5.45	6.3
0	321	328755	197	10.605	11.4
0	315	1413895	248	40.397	19.4
0	229	2909136	258	60.607	5.8
0	302	3604246	206	58.133	6.9
0	216	917504	199	8.192	3.5




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197198&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197198&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197198&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'Herman Ole Andreas Wold' @ wold.wessa.net







Correlations for all pairs of data series (method=kendall)
PopAssaultsBachDgrPoliceExpTotlPopulUnempl
Pop10.141-0.5560.124-0.3810.059
Assaults0.14110.1690.2790.2890.22
BachDgr-0.5560.16910.0550.7920.06
PoliceExp0.1240.2790.05510.1170.04
TotlPopul-0.3810.2890.7920.11710.143
Unempl0.0590.220.060.040.1431

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & Pop & Assaults & BachDgr & PoliceExp & TotlPopul & Unempl \tabularnewline
Pop & 1 & 0.141 & -0.556 & 0.124 & -0.381 & 0.059 \tabularnewline
Assaults & 0.141 & 1 & 0.169 & 0.279 & 0.289 & 0.22 \tabularnewline
BachDgr & -0.556 & 0.169 & 1 & 0.055 & 0.792 & 0.06 \tabularnewline
PoliceExp & 0.124 & 0.279 & 0.055 & 1 & 0.117 & 0.04 \tabularnewline
TotlPopul & -0.381 & 0.289 & 0.792 & 0.117 & 1 & 0.143 \tabularnewline
Unempl & 0.059 & 0.22 & 0.06 & 0.04 & 0.143 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197198&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]Pop[/C][C]Assaults[/C][C]BachDgr[/C][C]PoliceExp[/C][C]TotlPopul[/C][C]Unempl[/C][/ROW]
[ROW][C]Pop[/C][C]1[/C][C]0.141[/C][C]-0.556[/C][C]0.124[/C][C]-0.381[/C][C]0.059[/C][/ROW]
[ROW][C]Assaults[/C][C]0.141[/C][C]1[/C][C]0.169[/C][C]0.279[/C][C]0.289[/C][C]0.22[/C][/ROW]
[ROW][C]BachDgr[/C][C]-0.556[/C][C]0.169[/C][C]1[/C][C]0.055[/C][C]0.792[/C][C]0.06[/C][/ROW]
[ROW][C]PoliceExp[/C][C]0.124[/C][C]0.279[/C][C]0.055[/C][C]1[/C][C]0.117[/C][C]0.04[/C][/ROW]
[ROW][C]TotlPopul[/C][C]-0.381[/C][C]0.289[/C][C]0.792[/C][C]0.117[/C][C]1[/C][C]0.143[/C][/ROW]
[ROW][C]Unempl[/C][C]0.059[/C][C]0.22[/C][C]0.06[/C][C]0.04[/C][C]0.143[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197198&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197198&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)
PopAssaultsBachDgrPoliceExpTotlPopulUnempl
Pop10.141-0.5560.124-0.3810.059
Assaults0.14110.1690.2790.2890.22
BachDgr-0.5560.16910.0550.7920.06
PoliceExp0.1240.2790.05510.1170.04
TotlPopul-0.3810.2890.7920.11710.143
Unempl0.0590.220.060.040.1431







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Pop;Assaults0.190.17110.1409
p-value(0.1391)(0.1836)(0.1814)
Pop;BachDgr-0.5862-0.6753-0.5558
p-value(0)(0)(0)
Pop;PoliceExp0.18930.15060.1242
p-value(0.1406)(0.2427)(0.2396)
Pop;TotlPopul-0.609-0.4631-0.3812
p-value(0)(2e-04)(3e-04)
Pop;Unempl-0.11420.07080.0587
p-value(0.3768)(0.5847)(0.5805)
Assaults;BachDgr0.06210.23810.1693
p-value(0.6314)(0.0624)(0.0519)
Assaults;PoliceExp0.38670.40110.2788
p-value(0.0019)(0.0012)(0.0014)
Assaults;TotlPopul0.18960.4130.2888
p-value(0.1399)(8e-04)(9e-04)
Assaults;Unempl0.20470.32520.2203
p-value(0.1105)(0.0099)(0.0121)
BachDgr;PoliceExp-0.07970.08150.0546
p-value(0.5383)(0.5289)(0.5315)
BachDgr;TotlPopul0.87790.90870.7916
p-value(0)(0)(0)
BachDgr;Unempl0.03750.07750.0597
p-value(0.7723)(0.5495)(0.496)
PoliceExp;TotlPopul0.01290.17480.1171
p-value(0.9205)(0.1741)(0.1794)
PoliceExp;Unempl0.06660.05740.0395
p-value(0.607)(0.6577)(0.6528)
TotlPopul;Unempl0.22890.20450.1429
p-value(0.0736)(0.1109)(0.1033)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
Pop;Assaults & 0.19 & 0.1711 & 0.1409 \tabularnewline
p-value & (0.1391) & (0.1836) & (0.1814) \tabularnewline
Pop;BachDgr & -0.5862 & -0.6753 & -0.5558 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Pop;PoliceExp & 0.1893 & 0.1506 & 0.1242 \tabularnewline
p-value & (0.1406) & (0.2427) & (0.2396) \tabularnewline
Pop;TotlPopul & -0.609 & -0.4631 & -0.3812 \tabularnewline
p-value & (0) & (2e-04) & (3e-04) \tabularnewline
Pop;Unempl & -0.1142 & 0.0708 & 0.0587 \tabularnewline
p-value & (0.3768) & (0.5847) & (0.5805) \tabularnewline
Assaults;BachDgr & 0.0621 & 0.2381 & 0.1693 \tabularnewline
p-value & (0.6314) & (0.0624) & (0.0519) \tabularnewline
Assaults;PoliceExp & 0.3867 & 0.4011 & 0.2788 \tabularnewline
p-value & (0.0019) & (0.0012) & (0.0014) \tabularnewline
Assaults;TotlPopul & 0.1896 & 0.413 & 0.2888 \tabularnewline
p-value & (0.1399) & (8e-04) & (9e-04) \tabularnewline
Assaults;Unempl & 0.2047 & 0.3252 & 0.2203 \tabularnewline
p-value & (0.1105) & (0.0099) & (0.0121) \tabularnewline
BachDgr;PoliceExp & -0.0797 & 0.0815 & 0.0546 \tabularnewline
p-value & (0.5383) & (0.5289) & (0.5315) \tabularnewline
BachDgr;TotlPopul & 0.8779 & 0.9087 & 0.7916 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
BachDgr;Unempl & 0.0375 & 0.0775 & 0.0597 \tabularnewline
p-value & (0.7723) & (0.5495) & (0.496) \tabularnewline
PoliceExp;TotlPopul & 0.0129 & 0.1748 & 0.1171 \tabularnewline
p-value & (0.9205) & (0.1741) & (0.1794) \tabularnewline
PoliceExp;Unempl & 0.0666 & 0.0574 & 0.0395 \tabularnewline
p-value & (0.607) & (0.6577) & (0.6528) \tabularnewline
TotlPopul;Unempl & 0.2289 & 0.2045 & 0.1429 \tabularnewline
p-value & (0.0736) & (0.1109) & (0.1033) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197198&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]Pop;Assaults[/C][C]0.19[/C][C]0.1711[/C][C]0.1409[/C][/ROW]
[ROW][C]p-value[/C][C](0.1391)[/C][C](0.1836)[/C][C](0.1814)[/C][/ROW]
[ROW][C]Pop;BachDgr[/C][C]-0.5862[/C][C]-0.6753[/C][C]-0.5558[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Pop;PoliceExp[/C][C]0.1893[/C][C]0.1506[/C][C]0.1242[/C][/ROW]
[ROW][C]p-value[/C][C](0.1406)[/C][C](0.2427)[/C][C](0.2396)[/C][/ROW]
[ROW][C]Pop;TotlPopul[/C][C]-0.609[/C][C]-0.4631[/C][C]-0.3812[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](2e-04)[/C][C](3e-04)[/C][/ROW]
[ROW][C]Pop;Unempl[/C][C]-0.1142[/C][C]0.0708[/C][C]0.0587[/C][/ROW]
[ROW][C]p-value[/C][C](0.3768)[/C][C](0.5847)[/C][C](0.5805)[/C][/ROW]
[ROW][C]Assaults;BachDgr[/C][C]0.0621[/C][C]0.2381[/C][C]0.1693[/C][/ROW]
[ROW][C]p-value[/C][C](0.6314)[/C][C](0.0624)[/C][C](0.0519)[/C][/ROW]
[ROW][C]Assaults;PoliceExp[/C][C]0.3867[/C][C]0.4011[/C][C]0.2788[/C][/ROW]
[ROW][C]p-value[/C][C](0.0019)[/C][C](0.0012)[/C][C](0.0014)[/C][/ROW]
[ROW][C]Assaults;TotlPopul[/C][C]0.1896[/C][C]0.413[/C][C]0.2888[/C][/ROW]
[ROW][C]p-value[/C][C](0.1399)[/C][C](8e-04)[/C][C](9e-04)[/C][/ROW]
[ROW][C]Assaults;Unempl[/C][C]0.2047[/C][C]0.3252[/C][C]0.2203[/C][/ROW]
[ROW][C]p-value[/C][C](0.1105)[/C][C](0.0099)[/C][C](0.0121)[/C][/ROW]
[ROW][C]BachDgr;PoliceExp[/C][C]-0.0797[/C][C]0.0815[/C][C]0.0546[/C][/ROW]
[ROW][C]p-value[/C][C](0.5383)[/C][C](0.5289)[/C][C](0.5315)[/C][/ROW]
[ROW][C]BachDgr;TotlPopul[/C][C]0.8779[/C][C]0.9087[/C][C]0.7916[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]BachDgr;Unempl[/C][C]0.0375[/C][C]0.0775[/C][C]0.0597[/C][/ROW]
[ROW][C]p-value[/C][C](0.7723)[/C][C](0.5495)[/C][C](0.496)[/C][/ROW]
[ROW][C]PoliceExp;TotlPopul[/C][C]0.0129[/C][C]0.1748[/C][C]0.1171[/C][/ROW]
[ROW][C]p-value[/C][C](0.9205)[/C][C](0.1741)[/C][C](0.1794)[/C][/ROW]
[ROW][C]PoliceExp;Unempl[/C][C]0.0666[/C][C]0.0574[/C][C]0.0395[/C][/ROW]
[ROW][C]p-value[/C][C](0.607)[/C][C](0.6577)[/C][C](0.6528)[/C][/ROW]
[ROW][C]TotlPopul;Unempl[/C][C]0.2289[/C][C]0.2045[/C][C]0.1429[/C][/ROW]
[ROW][C]p-value[/C][C](0.0736)[/C][C](0.1109)[/C][C](0.1033)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197198&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197198&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
Pop;Assaults0.190.17110.1409
p-value(0.1391)(0.1836)(0.1814)
Pop;BachDgr-0.5862-0.6753-0.5558
p-value(0)(0)(0)
Pop;PoliceExp0.18930.15060.1242
p-value(0.1406)(0.2427)(0.2396)
Pop;TotlPopul-0.609-0.4631-0.3812
p-value(0)(2e-04)(3e-04)
Pop;Unempl-0.11420.07080.0587
p-value(0.3768)(0.5847)(0.5805)
Assaults;BachDgr0.06210.23810.1693
p-value(0.6314)(0.0624)(0.0519)
Assaults;PoliceExp0.38670.40110.2788
p-value(0.0019)(0.0012)(0.0014)
Assaults;TotlPopul0.18960.4130.2888
p-value(0.1399)(8e-04)(9e-04)
Assaults;Unempl0.20470.32520.2203
p-value(0.1105)(0.0099)(0.0121)
BachDgr;PoliceExp-0.07970.08150.0546
p-value(0.5383)(0.5289)(0.5315)
BachDgr;TotlPopul0.87790.90870.7916
p-value(0)(0)(0)
BachDgr;Unempl0.03750.07750.0597
p-value(0.7723)(0.5495)(0.496)
PoliceExp;TotlPopul0.01290.17480.1171
p-value(0.9205)(0.1741)(0.1794)
PoliceExp;Unempl0.06660.05740.0395
p-value(0.607)(0.6577)(0.6528)
TotlPopul;Unempl0.22890.20450.1429
p-value(0.0736)(0.1109)(0.1033)



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')