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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, 07 Dec 2015 11:30:36 +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/2015/Dec/07/t1449487921duftcyhumkix3tc.htm/, Retrieved Thu, 31 Oct 2024 23:25:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=285347, Retrieved Thu, 31 Oct 2024 23:25:09 +0000
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Original text written by user:
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Estimated Impact153
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Kendall tau Correlation Matrix] [] [2015-12-07 11:30:36] [a5a43fd78e41efc7c76f5ec05dea2bfd] [Current]
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Dataseries X:
5907 54694 0
5041 49298 0
5003 44659 0
5331 43657 0
5446 47002 0
83421 47042 1
9621 48959 0
9638 49750 0
8366 54048 0
8797 60067 0
8657 68929 0
8457 74617 0
46368 75940 1
16376 72762 0
25787 75621 0
26129 73008 0
24581 74196 0
31687 78878 0
34034 83812 0
24196 91624 0
61980 89388 1
62982 110410 1
46417 113857 0
33709 112060 0
34754 117236 0
31512 132810 0
31860 137699 0
36063 146409 0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285347&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]1 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=285347&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285347&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 time1 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Correlations for all pairs of data series (method=pearson)
NATWIJZIMMIGRWETG
NATWIJZ10.4530.767
IMMIGR0.45310.015
WETG0.7670.0151

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & NATWIJZ & IMMIGR & WETG \tabularnewline
NATWIJZ & 1 & 0.453 & 0.767 \tabularnewline
IMMIGR & 0.453 & 1 & 0.015 \tabularnewline
WETG & 0.767 & 0.015 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285347&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]NATWIJZ[/C][C]IMMIGR[/C][C]WETG[/C][/ROW]
[ROW][C]NATWIJZ[/C][C]1[/C][C]0.453[/C][C]0.767[/C][/ROW]
[ROW][C]IMMIGR[/C][C]0.453[/C][C]1[/C][C]0.015[/C][/ROW]
[ROW][C]WETG[/C][C]0.767[/C][C]0.015[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285347&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285347&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)
NATWIJZIMMIGRWETG
NATWIJZ10.4530.767
IMMIGR0.45310.015
WETG0.7670.0151







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
NATWIJZ;IMMIGR0.45290.69620.5503
p-value(0.0155)(1e-04)(0)
NATWIJZ;WETG0.76720.59380.4935
p-value(0)(9e-04)(0.002)
IMMIGR;WETG0.01520.06320.0525
p-value(0.9389)(0.7494)(0.7427)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
NATWIJZ;IMMIGR & 0.4529 & 0.6962 & 0.5503 \tabularnewline
p-value & (0.0155) & (1e-04) & (0) \tabularnewline
NATWIJZ;WETG & 0.7672 & 0.5938 & 0.4935 \tabularnewline
p-value & (0) & (9e-04) & (0.002) \tabularnewline
IMMIGR;WETG & 0.0152 & 0.0632 & 0.0525 \tabularnewline
p-value & (0.9389) & (0.7494) & (0.7427) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285347&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]NATWIJZ;IMMIGR[/C][C]0.4529[/C][C]0.6962[/C][C]0.5503[/C][/ROW]
[ROW][C]p-value[/C][C](0.0155)[/C][C](1e-04)[/C][C](0)[/C][/ROW]
[ROW][C]NATWIJZ;WETG[/C][C]0.7672[/C][C]0.5938[/C][C]0.4935[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](9e-04)[/C][C](0.002)[/C][/ROW]
[ROW][C]IMMIGR;WETG[/C][C]0.0152[/C][C]0.0632[/C][C]0.0525[/C][/ROW]
[ROW][C]p-value[/C][C](0.9389)[/C][C](0.7494)[/C][C](0.7427)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285347&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285347&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
NATWIJZ;IMMIGR0.45290.69620.5503
p-value(0.0155)(1e-04)(0)
NATWIJZ;WETG0.76720.59380.4935
p-value(0)(9e-04)(0.002)
IMMIGR;WETG0.01520.06320.0525
p-value(0.9389)(0.7494)(0.7427)







Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.010.330.670.67
0.020.670.670.67
0.030.670.670.67
0.040.670.670.67
0.050.670.670.67
0.060.670.670.67
0.070.670.670.67
0.080.670.670.67
0.090.670.670.67
0.10.670.670.67

\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 & 0.67 & 0.67 \tabularnewline
0.02 & 0.67 & 0.67 & 0.67 \tabularnewline
0.03 & 0.67 & 0.67 & 0.67 \tabularnewline
0.04 & 0.67 & 0.67 & 0.67 \tabularnewline
0.05 & 0.67 & 0.67 & 0.67 \tabularnewline
0.06 & 0.67 & 0.67 & 0.67 \tabularnewline
0.07 & 0.67 & 0.67 & 0.67 \tabularnewline
0.08 & 0.67 & 0.67 & 0.67 \tabularnewline
0.09 & 0.67 & 0.67 & 0.67 \tabularnewline
0.1 & 0.67 & 0.67 & 0.67 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285347&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]0.67[/C][C]0.67[/C][/ROW]
[ROW][C]0.02[/C][C]0.67[/C][C]0.67[/C][C]0.67[/C][/ROW]
[ROW][C]0.03[/C][C]0.67[/C][C]0.67[/C][C]0.67[/C][/ROW]
[ROW][C]0.04[/C][C]0.67[/C][C]0.67[/C][C]0.67[/C][/ROW]
[ROW][C]0.05[/C][C]0.67[/C][C]0.67[/C][C]0.67[/C][/ROW]
[ROW][C]0.06[/C][C]0.67[/C][C]0.67[/C][C]0.67[/C][/ROW]
[ROW][C]0.07[/C][C]0.67[/C][C]0.67[/C][C]0.67[/C][/ROW]
[ROW][C]0.08[/C][C]0.67[/C][C]0.67[/C][C]0.67[/C][/ROW]
[ROW][C]0.09[/C][C]0.67[/C][C]0.67[/C][C]0.67[/C][/ROW]
[ROW][C]0.1[/C][C]0.67[/C][C]0.67[/C][C]0.67[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285347&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285347&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
0.010.330.670.67
0.020.670.670.67
0.030.670.670.67
0.040.670.670.67
0.050.670.670.67
0.060.670.670.67
0.070.670.670.67
0.080.670.670.67
0.090.670.670.67
0.10.670.670.67



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$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
x <- na.omit(x)
y <- t(na.omit(t(y)))
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
n <- length(y[,1])
n
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ',header=TRUE)
for (i in 1:n) {
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
for (j in 1:n) {
r <- cor.test(y[i,],y[j,],method=par1)
a<-table.element(a,round(r$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')