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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 10:41:34 -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/t1354809411w9e2hpar40b6jrd.htm/, Retrieved Fri, 01 Nov 2024 00:35:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197152, Retrieved Fri, 01 Nov 2024 00:35:33 +0000
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Estimated Impact137
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-       [Kendall tau Correlation Matrix] [WS 10 Correlation...] [2012-12-06 15:41:34] [b126d3b292555ea554033ae826bcef2a] [Current]
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Dataseries X:
1	1	4	0	2
1	1	0	0	2
0	1	4	1	1.5
0	0	0	0	0
1	1	0	1	1
1	1	0	1	2
1	1	0	1	2
0	1	0	1	1
0	1	4	1	2
1	1	1	0	2
0	0	4	0	2
0	1	0	1	0
0	1	2	1	0
0	1	0	0	2
0	0	0	NA	NA
1	1	0	1	2
1	1	1	0	2
1	1	0	1	0.5
0	1	0	1	2
0	0	2	1	0
1	1	2	1	2
1	1	1	0	0
0	0	2	NA	NA
1	0	0	NA	NA
1	1	3	1	2
1	0	0	1	0
1	1	0	NA	NA
0	0	0	NA	NA
0	0	1	0	2
1	1	0	1	1
1	0	0	0	0.5
1	1	4	0	2
0	0	0	1	0.5
0	0	1	NA	NA
0	0	0	1	0.5
1	1	0	NA	NA
1	1	4	0	2
0	1	1	1	0
0	1	0	1	1
1	1	4	1	2
1	1	0	1	1
1	1	4	1	2
1	1	0	0	0
1	1	0	1	0.5
0	0	0	1	0
0	1	4	1	2
0	1	0	0	0
1	1	0	0	1
1	1	4	1	2
0	0	4	0	0.5
0	1	0	1	2
1	1	1	1	2
0	1	0	1	2
0	0	4	NA	NA
0	1	0	0	0
0	1	2	1	0
0	1	0	1	0.5
0	1	4	NA	NA
0	0	4	0	2
0	0	0	NA	NA
0	1	0	1	0
1	1	4	1	2
1	1	0	1	1
1	0	0	1	0
0	0	2	1	2
0	1	0	0	1
0	1	0	1	2
0	0	0	0	0
1	1	4	1	1
1	1	4	1	2
0	1	2	0	0
0	1	0	0	0
0	1	0	0	0
0	1	4	0	0
1	1	0	1	2
1	0	0	1	2
0	0	1	1	2
1	1	2	1	2
1	0	0	1	2
1	1	2	1	2
0	0	0	1	2
0	0	4	1	2
0	0	4	1	2
1	0	0	1	2
0	0	0	NA	NA
0	0	4	1	2
1	0	0	NA	NA
1	1	4	1	2
0	0	2	1	2
0	0	2	NA	NA
1	1	0	0	0
1	1	0	1	2
1	1	4	NA	NA
0	1	0	1	2
1	1	0	1	2
1	1	0	1	2
1	1	4	1	2
1	1	4	1	2
0	0	0	NA	NA
0	0	0	0	0
1	1	2	0	0
0	0	1	1	2
0	0	0	0	0
0	0	2	1	2
0	1	1	0	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197152&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197152&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197152&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' @ jenkins.wessa.net







Correlations for all pairs of data series (method=pearson)
prepost1post2post3post4
pre10.3870.0250.1040.272
post10.38710.0760.0160.044
post20.0250.07610.0330.351
post30.1040.0160.03310.354
post40.2720.0440.3510.3541

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & pre & post1 & post2 & post3 & post4 \tabularnewline
pre & 1 & 0.387 & 0.025 & 0.104 & 0.272 \tabularnewline
post1 & 0.387 & 1 & 0.076 & 0.016 & 0.044 \tabularnewline
post2 & 0.025 & 0.076 & 1 & 0.033 & 0.351 \tabularnewline
post3 & 0.104 & 0.016 & 0.033 & 1 & 0.354 \tabularnewline
post4 & 0.272 & 0.044 & 0.351 & 0.354 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197152&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]pre[/C][C]post1[/C][C]post2[/C][C]post3[/C][C]post4[/C][/ROW]
[ROW][C]pre[/C][C]1[/C][C]0.387[/C][C]0.025[/C][C]0.104[/C][C]0.272[/C][/ROW]
[ROW][C]post1[/C][C]0.387[/C][C]1[/C][C]0.076[/C][C]0.016[/C][C]0.044[/C][/ROW]
[ROW][C]post2[/C][C]0.025[/C][C]0.076[/C][C]1[/C][C]0.033[/C][C]0.351[/C][/ROW]
[ROW][C]post3[/C][C]0.104[/C][C]0.016[/C][C]0.033[/C][C]1[/C][C]0.354[/C][/ROW]
[ROW][C]post4[/C][C]0.272[/C][C]0.044[/C][C]0.351[/C][C]0.354[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197152&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197152&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)
prepost1post2post3post4
pre10.3870.0250.1040.272
post10.38710.0760.0160.044
post20.0250.07610.0330.351
post30.1040.0160.03310.354
post40.2720.0440.3510.3541







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
pre;post10.38670.38670.3867
p-value(0)(0)(1e-04)
pre;post20.0246-3e-04-3e-04
p-value(0.803)(0.9972)(0.9972)
pre;post30.10360.10360.1036
p-value(0.3313)(0.3313)(0.3285)
pre;post40.27150.27150.2554
p-value(0.0096)(0.0097)(0.0104)
post1;post20.07560.05770.054
p-value(0.4435)(0.5588)(0.5562)
post1;post30.01560.01560.0156
p-value(0.8842)(0.8842)(0.8833)
post1;post40.04410.02730.0257
p-value(0.6798)(0.7981)(0.7965)
post2;post30.03310.0190.0177
p-value(0.7571)(0.8591)(0.858)
post2;post40.3510.33510.2849
p-value(7e-04)(0.0012)(0.0022)
post3;post40.35380.3480.3274
p-value(6e-04)(8e-04)(0.001)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
pre;post1 & 0.3867 & 0.3867 & 0.3867 \tabularnewline
p-value & (0) & (0) & (1e-04) \tabularnewline
pre;post2 & 0.0246 & -3e-04 & -3e-04 \tabularnewline
p-value & (0.803) & (0.9972) & (0.9972) \tabularnewline
pre;post3 & 0.1036 & 0.1036 & 0.1036 \tabularnewline
p-value & (0.3313) & (0.3313) & (0.3285) \tabularnewline
pre;post4 & 0.2715 & 0.2715 & 0.2554 \tabularnewline
p-value & (0.0096) & (0.0097) & (0.0104) \tabularnewline
post1;post2 & 0.0756 & 0.0577 & 0.054 \tabularnewline
p-value & (0.4435) & (0.5588) & (0.5562) \tabularnewline
post1;post3 & 0.0156 & 0.0156 & 0.0156 \tabularnewline
p-value & (0.8842) & (0.8842) & (0.8833) \tabularnewline
post1;post4 & 0.0441 & 0.0273 & 0.0257 \tabularnewline
p-value & (0.6798) & (0.7981) & (0.7965) \tabularnewline
post2;post3 & 0.0331 & 0.019 & 0.0177 \tabularnewline
p-value & (0.7571) & (0.8591) & (0.858) \tabularnewline
post2;post4 & 0.351 & 0.3351 & 0.2849 \tabularnewline
p-value & (7e-04) & (0.0012) & (0.0022) \tabularnewline
post3;post4 & 0.3538 & 0.348 & 0.3274 \tabularnewline
p-value & (6e-04) & (8e-04) & (0.001) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197152&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]pre;post1[/C][C]0.3867[/C][C]0.3867[/C][C]0.3867[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](1e-04)[/C][/ROW]
[ROW][C]pre;post2[/C][C]0.0246[/C][C]-3e-04[/C][C]-3e-04[/C][/ROW]
[ROW][C]p-value[/C][C](0.803)[/C][C](0.9972)[/C][C](0.9972)[/C][/ROW]
[ROW][C]pre;post3[/C][C]0.1036[/C][C]0.1036[/C][C]0.1036[/C][/ROW]
[ROW][C]p-value[/C][C](0.3313)[/C][C](0.3313)[/C][C](0.3285)[/C][/ROW]
[ROW][C]pre;post4[/C][C]0.2715[/C][C]0.2715[/C][C]0.2554[/C][/ROW]
[ROW][C]p-value[/C][C](0.0096)[/C][C](0.0097)[/C][C](0.0104)[/C][/ROW]
[ROW][C]post1;post2[/C][C]0.0756[/C][C]0.0577[/C][C]0.054[/C][/ROW]
[ROW][C]p-value[/C][C](0.4435)[/C][C](0.5588)[/C][C](0.5562)[/C][/ROW]
[ROW][C]post1;post3[/C][C]0.0156[/C][C]0.0156[/C][C]0.0156[/C][/ROW]
[ROW][C]p-value[/C][C](0.8842)[/C][C](0.8842)[/C][C](0.8833)[/C][/ROW]
[ROW][C]post1;post4[/C][C]0.0441[/C][C]0.0273[/C][C]0.0257[/C][/ROW]
[ROW][C]p-value[/C][C](0.6798)[/C][C](0.7981)[/C][C](0.7965)[/C][/ROW]
[ROW][C]post2;post3[/C][C]0.0331[/C][C]0.019[/C][C]0.0177[/C][/ROW]
[ROW][C]p-value[/C][C](0.7571)[/C][C](0.8591)[/C][C](0.858)[/C][/ROW]
[ROW][C]post2;post4[/C][C]0.351[/C][C]0.3351[/C][C]0.2849[/C][/ROW]
[ROW][C]p-value[/C][C](7e-04)[/C][C](0.0012)[/C][C](0.0022)[/C][/ROW]
[ROW][C]post3;post4[/C][C]0.3538[/C][C]0.348[/C][C]0.3274[/C][/ROW]
[ROW][C]p-value[/C][C](6e-04)[/C][C](8e-04)[/C][C](0.001)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197152&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197152&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
pre;post10.38670.38670.3867
p-value(0)(0)(1e-04)
pre;post20.0246-3e-04-3e-04
p-value(0.803)(0.9972)(0.9972)
pre;post30.10360.10360.1036
p-value(0.3313)(0.3313)(0.3285)
pre;post40.27150.27150.2554
p-value(0.0096)(0.0097)(0.0104)
post1;post20.07560.05770.054
p-value(0.4435)(0.5588)(0.5562)
post1;post30.01560.01560.0156
p-value(0.8842)(0.8842)(0.8833)
post1;post40.04410.02730.0257
p-value(0.6798)(0.7981)(0.7965)
post2;post30.03310.0190.0177
p-value(0.7571)(0.8591)(0.858)
post2;post40.3510.33510.2849
p-value(7e-04)(0.0012)(0.0022)
post3;post40.35380.3480.3274
p-value(6e-04)(8e-04)(0.001)



Parameters (Session):
par1 = pearson ;
Parameters (R input):
par1 = pearson ;
R code (references can be found in the software module):
par1 <- 'pearson'
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')