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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationFri, 24 Dec 2010 17:20:52 +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/24/t12932111148ejspx88g0sfplg.htm/, Retrieved Tue, 30 Apr 2024 07:46:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=115226, Retrieved Tue, 30 Apr 2024 07:46:55 +0000
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IsPrivate?No (this computation is public)
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Estimated Impact116

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-     [Kendall tau Correlation Matrix] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
- RMPD    [Kendall tau Correlation Matrix] [workshop 10: Kendall] [2010-12-24 17:20:52] [35c3410767ea63f72c8afa35bf7b6164] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0	0	1	0	1
0	1	2	1	0
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Tables (Output of Computation)





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=115226&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=115226&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115226&T=0

As an alternative you can also use a QR Code:  
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=kendall)
PrepostTreatmentdiffgender
Pre10.166-0.002-0.190.157
post0.1661-0.2820.931-0.037
Treatment-0.002-0.2821-0.2890.069
diff-0.190.931-0.2891-0.093
gender0.157-0.0370.069-0.0931

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & Pre & post & Treatment & diff & gender \tabularnewline
Pre & 1 & 0.166 & -0.002 & -0.19 & 0.157 \tabularnewline
post & 0.166 & 1 & -0.282 & 0.931 & -0.037 \tabularnewline
Treatment & -0.002 & -0.282 & 1 & -0.289 & 0.069 \tabularnewline
diff & -0.19 & 0.931 & -0.289 & 1 & -0.093 \tabularnewline
gender & 0.157 & -0.037 & 0.069 & -0.093 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115226&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]Pre[/C][C]post[/C][C]Treatment[/C][C]diff[/C][C]gender[/C][/ROW]
[ROW][C]Pre[/C][C]1[/C][C]0.166[/C][C]-0.002[/C][C]-0.19[/C][C]0.157[/C][/ROW]
[ROW][C]post[/C][C]0.166[/C][C]1[/C][C]-0.282[/C][C]0.931[/C][C]-0.037[/C][/ROW]
[ROW][C]Treatment[/C][C]-0.002[/C][C]-0.282[/C][C]1[/C][C]-0.289[/C][C]0.069[/C][/ROW]
[ROW][C]diff[/C][C]-0.19[/C][C]0.931[/C][C]-0.289[/C][C]1[/C][C]-0.093[/C][/ROW]
[ROW][C]gender[/C][C]0.157[/C][C]-0.037[/C][C]0.069[/C][C]-0.093[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115226&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115226&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Correlations for all pairs of data series (method=kendall)
PrepostTreatmentdiffgender
Pre10.166-0.002-0.190.157
post0.1661-0.2820.931-0.037
Treatment-0.002-0.2821-0.2890.069
diff-0.190.931-0.2891-0.093
gender0.157-0.0370.069-0.0931






Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Pre;post0.16630.16630.1663
p-value(0.0731)(0.0731)(0.0733)
Pre;Treatment-0.002-0.0022-0.0021
p-value(0.9831)(0.9811)(0.981)
Pre;diff-0.217-0.1907-0.1899
p-value(0.0188)(0.0394)(0.04)
Pre;gender0.15690.15690.1569
p-value(0.0911)(0.0911)(0.091)
post;Treatment-0.298-0.2992-0.2821
p-value(0.0011)(0.001)(0.0013)
post;diff0.92650.9350.9311
p-value(0)(0)(0)
post;gender-0.0369-0.0369-0.0369
p-value(0.6932)(0.6932)(0.6914)
Treatment;diff-0.2943-0.3061-0.2892
p-value(0.0013)(8e-04)(9e-04)
Treatment;gender0.0730.07310.0689
p-value(0.4338)(0.4333)(0.4309)
diff;gender-0.0964-0.0931-0.0927
p-value(0.3013)(0.3182)(0.3161)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
Pre;post & 0.1663 & 0.1663 & 0.1663 \tabularnewline
p-value & (0.0731) & (0.0731) & (0.0733) \tabularnewline
Pre;Treatment & -0.002 & -0.0022 & -0.0021 \tabularnewline
p-value & (0.9831) & (0.9811) & (0.981) \tabularnewline
Pre;diff & -0.217 & -0.1907 & -0.1899 \tabularnewline
p-value & (0.0188) & (0.0394) & (0.04) \tabularnewline
Pre;gender & 0.1569 & 0.1569 & 0.1569 \tabularnewline
p-value & (0.0911) & (0.0911) & (0.091) \tabularnewline
post;Treatment & -0.298 & -0.2992 & -0.2821 \tabularnewline
p-value & (0.0011) & (0.001) & (0.0013) \tabularnewline
post;diff & 0.9265 & 0.935 & 0.9311 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
post;gender & -0.0369 & -0.0369 & -0.0369 \tabularnewline
p-value & (0.6932) & (0.6932) & (0.6914) \tabularnewline
Treatment;diff & -0.2943 & -0.3061 & -0.2892 \tabularnewline
p-value & (0.0013) & (8e-04) & (9e-04) \tabularnewline
Treatment;gender & 0.073 & 0.0731 & 0.0689 \tabularnewline
p-value & (0.4338) & (0.4333) & (0.4309) \tabularnewline
diff;gender & -0.0964 & -0.0931 & -0.0927 \tabularnewline
p-value & (0.3013) & (0.3182) & (0.3161) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115226&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;post[/C][C]0.1663[/C][C]0.1663[/C][C]0.1663[/C][/ROW]
[ROW][C]p-value[/C][C](0.0731)[/C][C](0.0731)[/C][C](0.0733)[/C][/ROW]
[ROW][C]Pre;Treatment[/C][C]-0.002[/C][C]-0.0022[/C][C]-0.0021[/C][/ROW]
[ROW][C]p-value[/C][C](0.9831)[/C][C](0.9811)[/C][C](0.981)[/C][/ROW]
[ROW][C]Pre;diff[/C][C]-0.217[/C][C]-0.1907[/C][C]-0.1899[/C][/ROW]
[ROW][C]p-value[/C][C](0.0188)[/C][C](0.0394)[/C][C](0.04)[/C][/ROW]
[ROW][C]Pre;gender[/C][C]0.1569[/C][C]0.1569[/C][C]0.1569[/C][/ROW]
[ROW][C]p-value[/C][C](0.0911)[/C][C](0.0911)[/C][C](0.091)[/C][/ROW]
[ROW][C]post;Treatment[/C][C]-0.298[/C][C]-0.2992[/C][C]-0.2821[/C][/ROW]
[ROW][C]p-value[/C][C](0.0011)[/C][C](0.001)[/C][C](0.0013)[/C][/ROW]
[ROW][C]post;diff[/C][C]0.9265[/C][C]0.935[/C][C]0.9311[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]post;gender[/C][C]-0.0369[/C][C]-0.0369[/C][C]-0.0369[/C][/ROW]
[ROW][C]p-value[/C][C](0.6932)[/C][C](0.6932)[/C][C](0.6914)[/C][/ROW]
[ROW][C]Treatment;diff[/C][C]-0.2943[/C][C]-0.3061[/C][C]-0.2892[/C][/ROW]
[ROW][C]p-value[/C][C](0.0013)[/C][C](8e-04)[/C][C](9e-04)[/C][/ROW]
[ROW][C]Treatment;gender[/C][C]0.073[/C][C]0.0731[/C][C]0.0689[/C][/ROW]
[ROW][C]p-value[/C][C](0.4338)[/C][C](0.4333)[/C][C](0.4309)[/C][/ROW]
[ROW][C]diff;gender[/C][C]-0.0964[/C][C]-0.0931[/C][C]-0.0927[/C][/ROW]
[ROW][C]p-value[/C][C](0.3013)[/C][C](0.3182)[/C][C](0.3161)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115226&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115226&T=2

As an alternative you can also use a QR Code:  
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;post0.16630.16630.1663
p-value(0.0731)(0.0731)(0.0733)
Pre;Treatment-0.002-0.0022-0.0021
p-value(0.9831)(0.9811)(0.981)
Pre;diff-0.217-0.1907-0.1899
p-value(0.0188)(0.0394)(0.04)
Pre;gender0.15690.15690.1569
p-value(0.0911)(0.0911)(0.091)
post;Treatment-0.298-0.2992-0.2821
p-value(0.0011)(0.001)(0.0013)
post;diff0.92650.9350.9311
p-value(0)(0)(0)
post;gender-0.0369-0.0369-0.0369
p-value(0.6932)(0.6932)(0.6914)
Treatment;diff-0.2943-0.3061-0.2892
p-value(0.0013)(8e-04)(9e-04)
Treatment;gender0.0730.07310.0689
p-value(0.4338)(0.4333)(0.4309)
diff;gender-0.0964-0.0931-0.0927
p-value(0.3013)(0.3182)(0.3161)


Figures (Output of Computation)

Input Parameters & R Code

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')