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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, 03 Sep 2015 08:51:15 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Sep/03/t1441266707lr04dt765muflr2.htm/, Retrieved Thu, 31 Oct 2024 23:25:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=280563, Retrieved Thu, 31 Oct 2024 23:25:44 +0000
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Estimated Impact84
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Kendall tau Correlation Matrix] [] [2015-09-03 07:51:15] [e9774d91d06602b4e3bbce6871390c37] [Current]
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Dataseries X:
99.2	96.7	101.0
99.0	98.1	100.1
100.0	100.0	100.0
111.6	104.9	90.6
122.2	104.9	86.5
117.6	109.5	89.7
121.1	110.8	90.6
136.0	112.3	82.8
154.2	109.3	70.1
153.6	105.3	65.4
158.5	101.7	61.3
140.6	95.4	62.5
136.2	96.4	63.6
168.0	97.6	52.6
154.3	102.4	59.7
149.0	101.6	59.5
165.5	103.8	61.3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 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 & 0 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280563&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]0 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=280563&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280563&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 time0 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Correlations for all pairs of data series (method=pearson)
V1V2V3
V110.062-0.947
V20.06210.179
V3-0.9470.1791

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & V1 & V2 & V3 \tabularnewline
V1 & 1 & 0.062 & -0.947 \tabularnewline
V2 & 0.062 & 1 & 0.179 \tabularnewline
V3 & -0.947 & 0.179 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280563&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]V1[/C][C]V2[/C][C]V3[/C][/ROW]
[ROW][C]V1[/C][C]1[/C][C]0.062[/C][C]-0.947[/C][/ROW]
[ROW][C]V2[/C][C]0.062[/C][C]1[/C][C]0.179[/C][/ROW]
[ROW][C]V3[/C][C]-0.947[/C][C]0.179[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280563&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280563&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)
V1V2V3
V110.062-0.947
V20.06210.179
V3-0.9470.1791







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
V1;V20.06180.00610.0221
p-value(0.8138)(0.9814)(0.9016)
V1;V3-0.9466-0.9166-0.7852
p-value(0)(0)(0)
V2;V30.17880.16640.1413
p-value(0.4922)(0.5234)(0.4327)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
V1;V2 & 0.0618 & 0.0061 & 0.0221 \tabularnewline
p-value & (0.8138) & (0.9814) & (0.9016) \tabularnewline
V1;V3 & -0.9466 & -0.9166 & -0.7852 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
V2;V3 & 0.1788 & 0.1664 & 0.1413 \tabularnewline
p-value & (0.4922) & (0.5234) & (0.4327) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280563&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]V1;V2[/C][C]0.0618[/C][C]0.0061[/C][C]0.0221[/C][/ROW]
[ROW][C]p-value[/C][C](0.8138)[/C][C](0.9814)[/C][C](0.9016)[/C][/ROW]
[ROW][C]V1;V3[/C][C]-0.9466[/C][C]-0.9166[/C][C]-0.7852[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]V2;V3[/C][C]0.1788[/C][C]0.1664[/C][C]0.1413[/C][/ROW]
[ROW][C]p-value[/C][C](0.4922)[/C][C](0.5234)[/C][C](0.4327)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280563&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280563&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
V1;V20.06180.00610.0221
p-value(0.8138)(0.9814)(0.9016)
V1;V3-0.9466-0.9166-0.7852
p-value(0)(0)(0)
V2;V30.17880.16640.1413
p-value(0.4922)(0.5234)(0.4327)







Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.010.330.330.33
0.020.330.330.33
0.030.330.330.33
0.040.330.330.33
0.050.330.330.33
0.060.330.330.33
0.070.330.330.33
0.080.330.330.33
0.090.330.330.33
0.10.330.330.33

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Correlation Tests \tabularnewline
Number of significant by total number of Correlations \tabularnewline
Type I error & Pearson r & Spearman rho & Kendall tau \tabularnewline
0.01 & 0.33 & 0.33 & 0.33 \tabularnewline
0.02 & 0.33 & 0.33 & 0.33 \tabularnewline
0.03 & 0.33 & 0.33 & 0.33 \tabularnewline
0.04 & 0.33 & 0.33 & 0.33 \tabularnewline
0.05 & 0.33 & 0.33 & 0.33 \tabularnewline
0.06 & 0.33 & 0.33 & 0.33 \tabularnewline
0.07 & 0.33 & 0.33 & 0.33 \tabularnewline
0.08 & 0.33 & 0.33 & 0.33 \tabularnewline
0.09 & 0.33 & 0.33 & 0.33 \tabularnewline
0.1 & 0.33 & 0.33 & 0.33 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280563&T=3

[TABLE]
[ROW][C]Meta Analysis of Correlation Tests[/C][/ROW]
[ROW][C]Number of significant by total number of Correlations[/C][/ROW]
[ROW][C]Type I error[/C][C]Pearson r[/C][C]Spearman rho[/C][C]Kendall tau[/C][/ROW]
[ROW][C]0.01[/C][C]0.33[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.02[/C][C]0.33[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.03[/C][C]0.33[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.04[/C][C]0.33[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.05[/C][C]0.33[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.06[/C][C]0.33[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.07[/C][C]0.33[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.08[/C][C]0.33[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.09[/C][C]0.33[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.1[/C][C]0.33[/C][C]0.33[/C][C]0.33[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280563&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280563&T=3

As an alternative you can also use a QR Code:  

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

Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.010.330.330.33
0.020.330.330.33
0.030.330.330.33
0.040.330.330.33
0.050.330.330.33
0.060.330.330.33
0.070.330.330.33
0.080.330.330.33
0.090.330.330.33
0.10.330.330.33



Parameters (Session):
par1 = 9 ; par2 = 0.36 ; par3 = 5.35 ; par4 = 5.2 ; par5 = 0.1 ;
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')
ncorrs <- (n*n -n)/2
mycorrs <- array(0, dim=c(10,3))
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)
for (iii in 1:10) {
iiid100 <- iii / 100
if (rp$p.value < iiid100) mycorrs[iii, 1] = mycorrs[iii, 1] + 1
if (rs$p.value < iiid100) mycorrs[iii, 2] = mycorrs[iii, 2] + 1
if (rk$p.value < iiid100) mycorrs[iii, 3] = mycorrs[iii, 3] + 1
}
}
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Correlation Tests',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Number of significant by total number of Correlations',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Type I error',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)
for (iii in 1:10) {
iiid100 <- iii / 100
a<-table.row.start(a)
a<-table.element(a,round(iiid100,2),header=T)
a<-table.element(a,round(mycorrs[iii,1]/ncorrs,2))
a<-table.element(a,round(mycorrs[iii,2]/ncorrs,2))
a<-table.element(a,round(mycorrs[iii,3]/ncorrs,2))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')