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R Software Module

rwasp_pairs.wasp

Title produced by software

Kendall tau Correlation Matrix

Date of computation

Wed, 23 Oct 2013 07:31:07 -0400

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Oct/23/t1382527878sjbo0oxmwgwdhr7.htm/, Retrieved Sun, 28 Apr 2024 13:32:26 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=218805, Retrieved Sun, 28 Apr 2024 13:32:26 +0000

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-       [Kendall tau Correlation Matrix] [] [2013-10-23 11:31:07] [a5501fab9c787febb2a952032c60b28f] [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	3 seconds
R Server	'Gertrude Mary Cox' @ cox.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 & 3 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=218805&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=218805&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=218805&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	3 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Correlations for all pairs of data series (method=pearson)
	V1	V2	V3	V4
V1	1	0.416	0.44	0.35
V2	0.416	1	0.439	0.391
V3	0.44	0.439	1	0.456
V4	0.35	0.391	0.456	1

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & V1 & V2 & V3 & V4 \tabularnewline
V1 & 1 & 0.416 & 0.44 & 0.35 \tabularnewline
V2 & 0.416 & 1 & 0.439 & 0.391 \tabularnewline
V3 & 0.44 & 0.439 & 1 & 0.456 \tabularnewline
V4 & 0.35 & 0.391 & 0.456 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=218805&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][C]V4[/C][/ROW]
[ROW][C]V1[/C][C]1[/C][C]0.416[/C][C]0.44[/C][C]0.35[/C][/ROW]
[ROW][C]V2[/C][C]0.416[/C][C]1[/C][C]0.439[/C][C]0.391[/C][/ROW]
[ROW][C]V3[/C][C]0.44[/C][C]0.439[/C][C]1[/C][C]0.456[/C][/ROW]
[ROW][C]V4[/C][C]0.35[/C][C]0.391[/C][C]0.456[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=218805&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=218805&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)
	V1	V2	V3	V4
V1	1	0.416	0.44	0.35
V2	0.416	1	0.439	0.391
V3	0.44	0.439	1	0.456
V4	0.35	0.391	0.456	1

Correlations for all pairs of data series with p-values
pair	Pearson r	Spearman rho	Kendall tau
V1;V2	0.4164	0.4201	0.373
p-value	(0)	(0)	(0)
V1;V3	0.4398	0.476	0.412
p-value	(0)	(0)	(0)
V1;V4	0.3495	0.3518	0.309
p-value	(0)	(0)	(0)
V2;V3	0.4395	0.4572	0.3974
p-value	(0)	(0)	(0)
V2;V4	0.3907	0.3322	0.2958
p-value	(0)	(0)	(0)
V3;V4	0.4556	0.4657	0.411
p-value	(0)	(0)	(0)

\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 & 0.4164 & 0.4201 & 0.373 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
V1;V3 & 0.4398 & 0.476 & 0.412 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
V1;V4 & 0.3495 & 0.3518 & 0.309 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
V2;V3 & 0.4395 & 0.4572 & 0.3974 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
V2;V4 & 0.3907 & 0.3322 & 0.2958 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
V3;V4 & 0.4556 & 0.4657 & 0.411 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=218805&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]0.4164[/C][C]0.4201[/C][C]0.373[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]V1;V3[/C][C]0.4398[/C][C]0.476[/C][C]0.412[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]V1;V4[/C][C]0.3495[/C][C]0.3518[/C][C]0.309[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]V2;V3[/C][C]0.4395[/C][C]0.4572[/C][C]0.3974[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]V2;V4[/C][C]0.3907[/C][C]0.3322[/C][C]0.2958[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]V3;V4[/C][C]0.4556[/C][C]0.4657[/C][C]0.411[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=218805&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=218805&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
V1;V2	0.4164	0.4201	0.373
p-value	(0)	(0)	(0)
V1;V3	0.4398	0.476	0.412
p-value	(0)	(0)	(0)
V1;V4	0.3495	0.3518	0.309
p-value	(0)	(0)	(0)
V2;V3	0.4395	0.4572	0.3974
p-value	(0)	(0)	(0)
V2;V4	0.3907	0.3322	0.2958
p-value	(0)	(0)	(0)
V3;V4	0.4556	0.4657	0.411
p-value	(0)	(0)	(0)

Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I error	Pearson r	Spearman rho	Kendall tau
0.01	1	1	1
0.02	1	1	1
0.03	1	1	1
0.04	1	1	1
0.05	1	1	1
0.06	1	1	1
0.07	1	1	1
0.08	1	1	1
0.09	1	1	1
0.1	1	1	1

\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 & 1 & 1 & 1 \tabularnewline
0.02 & 1 & 1 & 1 \tabularnewline
0.03 & 1 & 1 & 1 \tabularnewline
0.04 & 1 & 1 & 1 \tabularnewline
0.05 & 1 & 1 & 1 \tabularnewline
0.06 & 1 & 1 & 1 \tabularnewline
0.07 & 1 & 1 & 1 \tabularnewline
0.08 & 1 & 1 & 1 \tabularnewline
0.09 & 1 & 1 & 1 \tabularnewline
0.1 & 1 & 1 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=218805&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]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.02[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.03[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.04[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.05[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.06[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.07[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.08[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.09[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.1[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=218805&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=218805&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	1	1	1
0.02	1	1	1
0.03	1	1	1
0.04	1	1	1
0.05	1	1	1
0.06	1	1	1
0.07	1	1	1
0.08	1	1	1
0.09	1	1	1
0.1	1	1	1

Figure 1

PNG link

Postscript link

PDF link

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

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Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code