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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 computationMon, 13 Dec 2010 11:17:02 +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/13/t1292238906baee0vi68ifc4lf.htm/, Retrieved Mon, 06 May 2024 17:17:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108850, Retrieved Mon, 06 May 2024 17:17:20 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact135
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Kendall tau Correlation Matrix] [Pearson ws10] [2010-12-13 11:17:02] [039869833c16fe697975601e6b065e0f] [Current]
Feedback Forum
2010-12-17 09:17:46 [Pascal Wijnen] [reply
De student verwerkt de gegevens correct en geeft een goede interpretatie. Er wordt echter niets gezegd over het feit dat de gegevens eigenlijk niet bruikbaar zijn door het feit dat niet alle variabelen volledig normaal-verdeeld zijn! Ook is deze methode zeer gevoelig voor Outliers.

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




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108850&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108850&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108850&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Correlations for all pairs of data series (method=pearson)
CMDPEPCPS
CM10.3950.3290.3110.425
D0.39510.0270.154-0.032
PE0.3290.02710.5930.244
PC0.3110.1540.59310.131
PS0.425-0.0320.2440.1311

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & CM & D & PE & PC & PS \tabularnewline
CM & 1 & 0.395 & 0.329 & 0.311 & 0.425 \tabularnewline
D & 0.395 & 1 & 0.027 & 0.154 & -0.032 \tabularnewline
PE & 0.329 & 0.027 & 1 & 0.593 & 0.244 \tabularnewline
PC & 0.311 & 0.154 & 0.593 & 1 & 0.131 \tabularnewline
PS & 0.425 & -0.032 & 0.244 & 0.131 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108850&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]CM[/C][C]D[/C][C]PE[/C][C]PC[/C][C]PS[/C][/ROW]
[ROW][C]CM[/C][C]1[/C][C]0.395[/C][C]0.329[/C][C]0.311[/C][C]0.425[/C][/ROW]
[ROW][C]D[/C][C]0.395[/C][C]1[/C][C]0.027[/C][C]0.154[/C][C]-0.032[/C][/ROW]
[ROW][C]PE[/C][C]0.329[/C][C]0.027[/C][C]1[/C][C]0.593[/C][C]0.244[/C][/ROW]
[ROW][C]PC[/C][C]0.311[/C][C]0.154[/C][C]0.593[/C][C]1[/C][C]0.131[/C][/ROW]
[ROW][C]PS[/C][C]0.425[/C][C]-0.032[/C][C]0.244[/C][C]0.131[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108850&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108850&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)
CMDPEPCPS
CM10.3950.3290.3110.425
D0.39510.0270.154-0.032
PE0.3290.02710.5930.244
PC0.3110.1540.59310.131
PS0.425-0.0320.2440.1311







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
CM;D0.39530.39490.3021
p-value(0)(0)(0)
CM;PE0.32880.28420.2144
p-value(0)(3e-04)(2e-04)
CM;PC0.31090.33090.2526
p-value(1e-04)(0)(0)
CM;PS0.4250.40770.3013
p-value(0)(0)(0)
D;PE0.02720.02130.0124
p-value(0.7332)(0.7897)(0.8314)
D;PC0.15430.12050.0884
p-value(0.0521)(0.1304)(0.1357)
D;PS-0.03240.00270.002
p-value(0.6847)(0.9734)(0.9724)
PE;PC0.59320.48210.3775
p-value(0)(0)(0)
PE;PS0.24360.19870.1512
p-value(0.002)(0.0121)(0.0084)
PC;PS0.13130.11590.085
p-value(0.099)(0.1459)(0.1439)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
CM;D & 0.3953 & 0.3949 & 0.3021 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
CM;PE & 0.3288 & 0.2842 & 0.2144 \tabularnewline
p-value & (0) & (3e-04) & (2e-04) \tabularnewline
CM;PC & 0.3109 & 0.3309 & 0.2526 \tabularnewline
p-value & (1e-04) & (0) & (0) \tabularnewline
CM;PS & 0.425 & 0.4077 & 0.3013 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
D;PE & 0.0272 & 0.0213 & 0.0124 \tabularnewline
p-value & (0.7332) & (0.7897) & (0.8314) \tabularnewline
D;PC & 0.1543 & 0.1205 & 0.0884 \tabularnewline
p-value & (0.0521) & (0.1304) & (0.1357) \tabularnewline
D;PS & -0.0324 & 0.0027 & 0.002 \tabularnewline
p-value & (0.6847) & (0.9734) & (0.9724) \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
PC;PS & 0.1313 & 0.1159 & 0.085 \tabularnewline
p-value & (0.099) & (0.1459) & (0.1439) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108850&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]CM;D[/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]CM;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]CM;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]CM;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]D;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]D;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]D;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]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]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]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108850&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108850&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
CM;D0.39530.39490.3021
p-value(0)(0)(0)
CM;PE0.32880.28420.2144
p-value(0)(3e-04)(2e-04)
CM;PC0.31090.33090.2526
p-value(1e-04)(0)(0)
CM;PS0.4250.40770.3013
p-value(0)(0)(0)
D;PE0.02720.02130.0124
p-value(0.7332)(0.7897)(0.8314)
D;PC0.15430.12050.0884
p-value(0.0521)(0.1304)(0.1357)
D;PS-0.03240.00270.002
p-value(0.6847)(0.9734)(0.9724)
PE;PC0.59320.48210.3775
p-value(0)(0)(0)
PE;PS0.24360.19870.1512
p-value(0.002)(0.0121)(0.0084)
PC;PS0.13130.11590.085
p-value(0.099)(0.1459)(0.1439)



Parameters (Session):
par1 = pearson ;
Parameters (R input):
par1 = pearson ;
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')