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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 11:21:30 +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/t1291980063vlnarpb5yqojvn0.htm/, Retrieved Mon, 29 Apr 2024 12:56:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=107556, Retrieved Mon, 29 Apr 2024 12:56:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact222
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 R PD    [Kendall tau Correlation Matrix] [W10 Kendall Tau] [2010-12-10 11:21:30] [59f7d3e7fcb6374015f4e6b9053b0f01] [Current]
Feedback Forum
2010-12-20 05:37:03 [411b43619fc9db329bbcdbf7261c55fb] [reply
Je maakt een correcte berekening. Daarnaast merk je correct op dat er geen enkel koppel van variabelen is dat een hoge correlatie heeft. Maar vergeet niet dat je hier de Kendall Correlation Matrix (methode: Kendall) hebt gebruikt, zoals je reeds aangaf is deze robuuster (minder gevoelig voor outliers). Bijgevolg moet je bij uw conclusie dus GEEN rekening houden met de normaalverdeling.

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Dataseries X:
24	14	11	12	24	26
25	11	7	8	25	23
17	6	17	8	30	25
18	12	10	8	19	23
18	8	12	9	22	19
16	10	12	7	22	29
20	10	11	4	25	25
16	11	11	11	23	21
18	16	12	7	17	22
17	11	13	7	21	25
23	13	14	12	19	24
30	12	16	10	19	18
23	8	11	10	15	22
18	12	10	8	16	15
15	11	11	8	23	22
12	4	15	4	27	28
21	9	9	9	22	20
15	8	11	8	14	12
20	8	17	7	22	24
31	14	17	11	23	20
27	15	11	9	23	21
34	16	18	11	21	20
21	9	14	13	19	21
31	14	10	8	18	23
19	11	11	8	20	28
16	8	15	9	23	24
20	9	15	6	25	24
21	9	13	9	19	24
22	9	16	9	24	23
17	9	13	6	22	23
24	10	9	6	25	29
25	16	18	16	26	24
26	11	18	5	29	18
25	8	12	7	32	25
17	9	17	9	25	21
32	16	9	6	29	26
33	11	9	6	28	22
13	16	12	5	17	22
32	12	18	12	28	22
25	12	12	7	29	23
29	14	18	10	26	30
22	9	14	9	25	23
18	10	15	8	14	17
17	9	16	5	25	23
20	10	10	8	26	23
15	12	11	8	20	25
20	14	14	10	18	24
33	14	9	6	32	24
29	10	12	8	25	23
23	14	17	7	25	21
26	16	5	4	23	24
18	9	12	8	21	24
20	10	12	8	20	28
11	6	6	4	15	16
28	8	24	20	30	20
26	13	12	8	24	29
22	10	12	8	26	27
17	8	14	6	24	22
12	7	7	4	22	28
14	15	13	8	14	16
17	9	12	9	24	25
21	10	13	6	24	24
19	12	14	7	24	28
18	13	8	9	24	24
10	10	11	5	19	23
29	11	9	5	31	30
31	8	11	8	22	24
19	9	13	8	27	21
9	13	10	6	19	25
20	11	11	8	25	25
28	8	12	7	20	22
19	9	9	7	21	23
30	9	15	9	27	26
29	15	18	11	23	23
26	9	15	6	25	25
23	10	12	8	20	21
13	14	13	6	21	25
21	12	14	9	22	24
19	12	10	8	23	29
28	11	13	6	25	22
23	14	13	10	25	27
18	6	11	8	17	26
21	12	13	8	19	22
20	8	16	10	25	24
23	14	8	5	19	27
21	11	16	7	20	24
21	10	11	5	26	24
15	14	9	8	23	29
28	12	16	14	27	22
19	10	12	7	17	21
26	14	14	8	17	24
10	5	8	6	19	24
16	11	9	5	17	23
22	10	15	6	22	20
19	9	11	10	21	27
31	10	21	12	32	26
31	16	14	9	21	25
29	13	18	12	21	21
19	9	12	7	18	21
22	10	13	8	18	19
23	10	15	10	23	21
15	7	12	6	19	21
20	9	19	10	20	16
18	8	15	10	21	22
23	14	11	10	20	29
25	14	11	5	17	15
21	8	10	7	18	17
24	9	13	10	19	15
25	14	15	11	22	21
17	14	12	6	15	21
13	8	12	7	14	19
28	8	16	12	18	24
21	8	9	11	24	20
25	7	18	11	35	17
9	6	8	11	29	23
16	8	13	5	21	24
19	6	17	8	25	14
17	11	9	6	20	19
25	14	15	9	22	24
20	11	8	4	13	13
29	11	7	4	26	22
14	11	12	7	17	16
22	14	14	11	25	19
15	8	6	6	20	25
19	20	8	7	19	25
20	11	17	8	21	23
15	8	10	4	22	24
20	11	11	8	24	26
18	10	14	9	21	26
33	14	11	8	26	25
22	11	13	11	24	18
16	9	12	8	16	21
17	9	11	5	23	26
16	8	9	4	18	23
21	10	12	8	16	23
26	13	20	10	26	22
18	13	12	6	19	20
18	12	13	9	21	13
17	8	12	9	21	24
22	13	12	13	22	15
30	14	9	9	23	14
30	12	15	10	29	22
24	14	24	20	21	10
21	15	7	5	21	24
21	13	17	11	23	22
29	16	11	6	27	24
31	9	17	9	25	19
20	9	11	7	21	20
16	9	12	9	10	13
22	8	14	10	20	20
20	7	11	9	26	22
28	16	16	8	24	24
38	11	21	7	29	29
22	9	14	6	19	12
20	11	20	13	24	20
17	9	13	6	19	21
28	14	11	8	24	24
22	13	15	10	22	22
31	16	19	16	17	20




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107556&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]4 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=107556&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107556&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Correlations for all pairs of data series (method=kendall)
CDDAPEPCPSO
CD10.3020.2140.2530.3010.002
DA0.30210.0120.0880.0020.059
PE0.2140.01210.3770.151-0.135
PC0.2530.0880.37710.085-0.138
PS0.3010.0020.1510.08510.224
O0.0020.059-0.135-0.1380.2241

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & CD & DA & PE & PC & PS & O \tabularnewline
CD & 1 & 0.302 & 0.214 & 0.253 & 0.301 & 0.002 \tabularnewline
DA & 0.302 & 1 & 0.012 & 0.088 & 0.002 & 0.059 \tabularnewline
PE & 0.214 & 0.012 & 1 & 0.377 & 0.151 & -0.135 \tabularnewline
PC & 0.253 & 0.088 & 0.377 & 1 & 0.085 & -0.138 \tabularnewline
PS & 0.301 & 0.002 & 0.151 & 0.085 & 1 & 0.224 \tabularnewline
O & 0.002 & 0.059 & -0.135 & -0.138 & 0.224 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107556&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]CD[/C][C]DA[/C][C]PE[/C][C]PC[/C][C]PS[/C][C]O[/C][/ROW]
[ROW][C]CD[/C][C]1[/C][C]0.302[/C][C]0.214[/C][C]0.253[/C][C]0.301[/C][C]0.002[/C][/ROW]
[ROW][C]DA[/C][C]0.302[/C][C]1[/C][C]0.012[/C][C]0.088[/C][C]0.002[/C][C]0.059[/C][/ROW]
[ROW][C]PE[/C][C]0.214[/C][C]0.012[/C][C]1[/C][C]0.377[/C][C]0.151[/C][C]-0.135[/C][/ROW]
[ROW][C]PC[/C][C]0.253[/C][C]0.088[/C][C]0.377[/C][C]1[/C][C]0.085[/C][C]-0.138[/C][/ROW]
[ROW][C]PS[/C][C]0.301[/C][C]0.002[/C][C]0.151[/C][C]0.085[/C][C]1[/C][C]0.224[/C][/ROW]
[ROW][C]O[/C][C]0.002[/C][C]0.059[/C][C]-0.135[/C][C]-0.138[/C][C]0.224[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107556&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107556&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)
CDDAPEPCPSO
CD10.3020.2140.2530.3010.002
DA0.30210.0120.0880.0020.059
PE0.2140.01210.3770.151-0.135
PC0.2530.0880.37710.085-0.138
PS0.3010.0020.1510.08510.224
O0.0020.059-0.135-0.1380.2241







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
CD;DA0.39530.39490.3021
p-value(0)(0)(0)
CD;PE0.32880.28420.2144
p-value(0)(3e-04)(2e-04)
CD;PC0.31090.33090.2526
p-value(1e-04)(0)(0)
CD;PS0.4250.40770.3013
p-value(0)(0)(0)
CD;O0.05410.00580.0018
p-value(0.4983)(0.9427)(0.9749)
DA;PE0.02720.02130.0124
p-value(0.7332)(0.7897)(0.8314)
DA;PC0.15430.12050.0884
p-value(0.0521)(0.1304)(0.1357)
DA;PS-0.03240.00270.002
p-value(0.6847)(0.9734)(0.9724)
DA;O0.06980.08150.0587
p-value(0.3817)(0.3068)(0.314)
PE;PC0.59320.48210.3775
p-value(0)(0)(0)
PE;PS0.24360.19870.1512
p-value(0.002)(0.0121)(0.0084)
PE;O-0.1549-0.1805-0.1349
p-value(0.0512)(0.0228)(0.0196)
PC;PS0.13130.11590.085
p-value(0.099)(0.1459)(0.1439)
PC;O-0.203-0.189-0.1383
p-value(0.0103)(0.0171)(0.0182)
PS;O0.35220.3090.2242
p-value(0)(1e-04)(1e-04)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
CD;DA & 0.3953 & 0.3949 & 0.3021 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
CD;PE & 0.3288 & 0.2842 & 0.2144 \tabularnewline
p-value & (0) & (3e-04) & (2e-04) \tabularnewline
CD;PC & 0.3109 & 0.3309 & 0.2526 \tabularnewline
p-value & (1e-04) & (0) & (0) \tabularnewline
CD;PS & 0.425 & 0.4077 & 0.3013 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
CD;O & 0.0541 & 0.0058 & 0.0018 \tabularnewline
p-value & (0.4983) & (0.9427) & (0.9749) \tabularnewline
DA;PE & 0.0272 & 0.0213 & 0.0124 \tabularnewline
p-value & (0.7332) & (0.7897) & (0.8314) \tabularnewline
DA;PC & 0.1543 & 0.1205 & 0.0884 \tabularnewline
p-value & (0.0521) & (0.1304) & (0.1357) \tabularnewline
DA;PS & -0.0324 & 0.0027 & 0.002 \tabularnewline
p-value & (0.6847) & (0.9734) & (0.9724) \tabularnewline
DA;O & 0.0698 & 0.0815 & 0.0587 \tabularnewline
p-value & (0.3817) & (0.3068) & (0.314) \tabularnewline
PE;PC & 0.5932 & 0.4821 & 0.3775 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
PE;PS & 0.2436 & 0.1987 & 0.1512 \tabularnewline
p-value & (0.002) & (0.0121) & (0.0084) \tabularnewline
PE;O & -0.1549 & -0.1805 & -0.1349 \tabularnewline
p-value & (0.0512) & (0.0228) & (0.0196) \tabularnewline
PC;PS & 0.1313 & 0.1159 & 0.085 \tabularnewline
p-value & (0.099) & (0.1459) & (0.1439) \tabularnewline
PC;O & -0.203 & -0.189 & -0.1383 \tabularnewline
p-value & (0.0103) & (0.0171) & (0.0182) \tabularnewline
PS;O & 0.3522 & 0.309 & 0.2242 \tabularnewline
p-value & (0) & (1e-04) & (1e-04) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107556&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]CD;DA[/C][C]0.3953[/C][C]0.3949[/C][C]0.3021[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]CD;PE[/C][C]0.3288[/C][C]0.2842[/C][C]0.2144[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](3e-04)[/C][C](2e-04)[/C][/ROW]
[ROW][C]CD;PC[/C][C]0.3109[/C][C]0.3309[/C][C]0.2526[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]CD;PS[/C][C]0.425[/C][C]0.4077[/C][C]0.3013[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]CD;O[/C][C]0.0541[/C][C]0.0058[/C][C]0.0018[/C][/ROW]
[ROW][C]p-value[/C][C](0.4983)[/C][C](0.9427)[/C][C](0.9749)[/C][/ROW]
[ROW][C]DA;PE[/C][C]0.0272[/C][C]0.0213[/C][C]0.0124[/C][/ROW]
[ROW][C]p-value[/C][C](0.7332)[/C][C](0.7897)[/C][C](0.8314)[/C][/ROW]
[ROW][C]DA;PC[/C][C]0.1543[/C][C]0.1205[/C][C]0.0884[/C][/ROW]
[ROW][C]p-value[/C][C](0.0521)[/C][C](0.1304)[/C][C](0.1357)[/C][/ROW]
[ROW][C]DA;PS[/C][C]-0.0324[/C][C]0.0027[/C][C]0.002[/C][/ROW]
[ROW][C]p-value[/C][C](0.6847)[/C][C](0.9734)[/C][C](0.9724)[/C][/ROW]
[ROW][C]DA;O[/C][C]0.0698[/C][C]0.0815[/C][C]0.0587[/C][/ROW]
[ROW][C]p-value[/C][C](0.3817)[/C][C](0.3068)[/C][C](0.314)[/C][/ROW]
[ROW][C]PE;PC[/C][C]0.5932[/C][C]0.4821[/C][C]0.3775[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]PE;PS[/C][C]0.2436[/C][C]0.1987[/C][C]0.1512[/C][/ROW]
[ROW][C]p-value[/C][C](0.002)[/C][C](0.0121)[/C][C](0.0084)[/C][/ROW]
[ROW][C]PE;O[/C][C]-0.1549[/C][C]-0.1805[/C][C]-0.1349[/C][/ROW]
[ROW][C]p-value[/C][C](0.0512)[/C][C](0.0228)[/C][C](0.0196)[/C][/ROW]
[ROW][C]PC;PS[/C][C]0.1313[/C][C]0.1159[/C][C]0.085[/C][/ROW]
[ROW][C]p-value[/C][C](0.099)[/C][C](0.1459)[/C][C](0.1439)[/C][/ROW]
[ROW][C]PC;O[/C][C]-0.203[/C][C]-0.189[/C][C]-0.1383[/C][/ROW]
[ROW][C]p-value[/C][C](0.0103)[/C][C](0.0171)[/C][C](0.0182)[/C][/ROW]
[ROW][C]PS;O[/C][C]0.3522[/C][C]0.309[/C][C]0.2242[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](1e-04)[/C][C](1e-04)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107556&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107556&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
CD;DA0.39530.39490.3021
p-value(0)(0)(0)
CD;PE0.32880.28420.2144
p-value(0)(3e-04)(2e-04)
CD;PC0.31090.33090.2526
p-value(1e-04)(0)(0)
CD;PS0.4250.40770.3013
p-value(0)(0)(0)
CD;O0.05410.00580.0018
p-value(0.4983)(0.9427)(0.9749)
DA;PE0.02720.02130.0124
p-value(0.7332)(0.7897)(0.8314)
DA;PC0.15430.12050.0884
p-value(0.0521)(0.1304)(0.1357)
DA;PS-0.03240.00270.002
p-value(0.6847)(0.9734)(0.9724)
DA;O0.06980.08150.0587
p-value(0.3817)(0.3068)(0.314)
PE;PC0.59320.48210.3775
p-value(0)(0)(0)
PE;PS0.24360.19870.1512
p-value(0.002)(0.0121)(0.0084)
PE;O-0.1549-0.1805-0.1349
p-value(0.0512)(0.0228)(0.0196)
PC;PS0.13130.11590.085
p-value(0.099)(0.1459)(0.1439)
PC;O-0.203-0.189-0.1383
p-value(0.0103)(0.0171)(0.0182)
PS;O0.35220.3090.2242
p-value(0)(1e-04)(1e-04)



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')