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R Software Module

rwasp_pairs.wasp

Title produced by software

Kendall tau Correlation Matrix

Date of computation

Sat, 17 Dec 2016 15:55:10 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/17/t1481986541o2vij46oz4prvq9.htm/, Retrieved Thu, 02 May 2024 11:32:33 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=300836, Retrieved Thu, 02 May 2024 11:32:33 +0000

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-       [Kendall tau Correlation Matrix] [GW onderling] [2016-12-17 14:55:10] [8263efc94e08b372ab727a2b95bd56b1] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300836&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

1 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=300836&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300836&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	Big Analytics Cloud Computing Center

Correlations for all pairs of data series (method=pearson)
	GW1	GW2	GW3	GW4
GW1	1	-0.023	-0.297	0.348
GW2	-0.023	1	0.368	0.278
GW3	-0.297	0.368	1	0.299
GW4	0.348	0.278	0.299	1

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & GW1 & GW2 & GW3 & GW4 \tabularnewline
GW1 & 1 & -0.023 & -0.297 & 0.348 \tabularnewline
GW2 & -0.023 & 1 & 0.368 & 0.278 \tabularnewline
GW3 & -0.297 & 0.368 & 1 & 0.299 \tabularnewline
GW4 & 0.348 & 0.278 & 0.299 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300836&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]GW1[/C][C]GW2[/C][C]GW3[/C][C]GW4[/C][/ROW]
[ROW][C]GW1[/C][C]1[/C][C]-0.023[/C][C]-0.297[/C][C]0.348[/C][/ROW]
[ROW][C]GW2[/C][C]-0.023[/C][C]1[/C][C]0.368[/C][C]0.278[/C][/ROW]
[ROW][C]GW3[/C][C]-0.297[/C][C]0.368[/C][C]1[/C][C]0.299[/C][/ROW]
[ROW][C]GW4[/C][C]0.348[/C][C]0.278[/C][C]0.299[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300836&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300836&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)
	GW1	GW2	GW3	GW4
GW1	1	-0.023	-0.297	0.348
GW2	-0.023	1	0.368	0.278
GW3	-0.297	0.368	1	0.299
GW4	0.348	0.278	0.299	1

Correlations for all pairs of data series with p-values
pair	Pearson r	Spearman rho	Kendall tau
GW1;GW2	-0.0225	0.0031	0.0044
p-value	(0.7731)	(0.9679)	(0.9505)
GW1;GW3	-0.2968	-0.2275	-0.2186
p-value	(1e-04)	(0.0032)	(0.0031)
GW1;GW4	0.3477	0.4782	0.454
p-value	(0)	(0)	(0)
GW2;GW3	0.3678	0.3821	0.3618
p-value	(0)	(0)	(0)
GW2;GW4	0.2783	0.242	0.2238
p-value	(3e-04)	(0.0017)	(0.0015)
GW3;GW4	0.2986	0.1843	0.1752
p-value	(1e-04)	(0.0174)	(0.0167)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
GW1;GW2 & -0.0225 & 0.0031 & 0.0044 \tabularnewline
p-value & (0.7731) & (0.9679) & (0.9505) \tabularnewline
GW1;GW3 & -0.2968 & -0.2275 & -0.2186 \tabularnewline
p-value & (1e-04) & (0.0032) & (0.0031) \tabularnewline
GW1;GW4 & 0.3477 & 0.4782 & 0.454 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
GW2;GW3 & 0.3678 & 0.3821 & 0.3618 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
GW2;GW4 & 0.2783 & 0.242 & 0.2238 \tabularnewline
p-value & (3e-04) & (0.0017) & (0.0015) \tabularnewline
GW3;GW4 & 0.2986 & 0.1843 & 0.1752 \tabularnewline
p-value & (1e-04) & (0.0174) & (0.0167) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300836&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]GW1;GW2[/C][C]-0.0225[/C][C]0.0031[/C][C]0.0044[/C][/ROW]
[ROW][C]p-value[/C][C](0.7731)[/C][C](0.9679)[/C][C](0.9505)[/C][/ROW]
[ROW][C]GW1;GW3[/C][C]-0.2968[/C][C]-0.2275[/C][C]-0.2186[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](0.0032)[/C][C](0.0031)[/C][/ROW]
[ROW][C]GW1;GW4[/C][C]0.3477[/C][C]0.4782[/C][C]0.454[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]GW2;GW3[/C][C]0.3678[/C][C]0.3821[/C][C]0.3618[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]GW2;GW4[/C][C]0.2783[/C][C]0.242[/C][C]0.2238[/C][/ROW]
[ROW][C]p-value[/C][C](3e-04)[/C][C](0.0017)[/C][C](0.0015)[/C][/ROW]
[ROW][C]GW3;GW4[/C][C]0.2986[/C][C]0.1843[/C][C]0.1752[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](0.0174)[/C][C](0.0167)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300836&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300836&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
pair	Pearson r	Spearman rho	Kendall tau
GW1;GW2	-0.0225	0.0031	0.0044
p-value	(0.7731)	(0.9679)	(0.9505)
GW1;GW3	-0.2968	-0.2275	-0.2186
p-value	(1e-04)	(0.0032)	(0.0031)
GW1;GW4	0.3477	0.4782	0.454
p-value	(0)	(0)	(0)
GW2;GW3	0.3678	0.3821	0.3618
p-value	(0)	(0)	(0)
GW2;GW4	0.2783	0.242	0.2238
p-value	(3e-04)	(0.0017)	(0.0015)
GW3;GW4	0.2986	0.1843	0.1752
p-value	(1e-04)	(0.0174)	(0.0167)

Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I error	Pearson r	Spearman rho	Kendall tau
0.01	0.83	0.67	0.67
0.02	0.83	0.83	0.83
0.03	0.83	0.83	0.83
0.04	0.83	0.83	0.83
0.05	0.83	0.83	0.83
0.06	0.83	0.83	0.83
0.07	0.83	0.83	0.83
0.08	0.83	0.83	0.83
0.09	0.83	0.83	0.83
0.1	0.83	0.83	0.83

\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 & 0.83 & 0.67 & 0.67 \tabularnewline
0.02 & 0.83 & 0.83 & 0.83 \tabularnewline
0.03 & 0.83 & 0.83 & 0.83 \tabularnewline
0.04 & 0.83 & 0.83 & 0.83 \tabularnewline
0.05 & 0.83 & 0.83 & 0.83 \tabularnewline
0.06 & 0.83 & 0.83 & 0.83 \tabularnewline
0.07 & 0.83 & 0.83 & 0.83 \tabularnewline
0.08 & 0.83 & 0.83 & 0.83 \tabularnewline
0.09 & 0.83 & 0.83 & 0.83 \tabularnewline
0.1 & 0.83 & 0.83 & 0.83 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300836&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]0.83[/C][C]0.67[/C][C]0.67[/C][/ROW]
[ROW][C]0.02[/C][C]0.83[/C][C]0.83[/C][C]0.83[/C][/ROW]
[ROW][C]0.03[/C][C]0.83[/C][C]0.83[/C][C]0.83[/C][/ROW]
[ROW][C]0.04[/C][C]0.83[/C][C]0.83[/C][C]0.83[/C][/ROW]
[ROW][C]0.05[/C][C]0.83[/C][C]0.83[/C][C]0.83[/C][/ROW]
[ROW][C]0.06[/C][C]0.83[/C][C]0.83[/C][C]0.83[/C][/ROW]
[ROW][C]0.07[/C][C]0.83[/C][C]0.83[/C][C]0.83[/C][/ROW]
[ROW][C]0.08[/C][C]0.83[/C][C]0.83[/C][C]0.83[/C][/ROW]
[ROW][C]0.09[/C][C]0.83[/C][C]0.83[/C][C]0.83[/C][/ROW]
[ROW][C]0.1[/C][C]0.83[/C][C]0.83[/C][C]0.83[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300836&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300836&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 error	Pearson r	Spearman rho	Kendall tau
0.01	0.83	0.67	0.67
0.02	0.83	0.83	0.83
0.03	0.83	0.83	0.83
0.04	0.83	0.83	0.83
0.05	0.83	0.83	0.83
0.06	0.83	0.83	0.83
0.07	0.83	0.83	0.83
0.08	0.83	0.83	0.83
0.09	0.83	0.83	0.83
0.1	0.83	0.83	0.83

Figure 1

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = 2 ; par3 = Pearson Chi-Squared ;

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

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