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*The author of this computation has been verified*

R Software Module

rwasp_pairs.wasp

Title produced by software

Kendall tau Correlation Matrix

Date of computation

Thu, 21 Jan 2016 15:08:29 +0000

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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/Jan/21/t1453390330yqxm4len2fhldbf.htm/, Retrieved Sun, 28 Apr 2024 19:09:16 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=289905, Retrieved Sun, 28 Apr 2024 19:09:16 +0000

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-       [Kendall tau Correlation Matrix] [oefening 10 jenkins] [2016-01-21 15:08:29] [3e2780e2cb0e47accd70a310e72f0fc9] [Current]

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Histogram

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46.4 392 0.4 68.5
45.7 118 0.61 87.8
45.3 44 0.53 115.8
38.6 158 0.53 106.8
37.2 81 0.53 71.6
35 374 0.37 60.2
34 187 0.3 118.7
28.3 993 0.19 33.7
24.7 1723 0.12 27.2
24.7 287 0.2 62
24.4 970 0.19 24.9
22.7 885 0.12 22.9
22.3 200 0.53 65.7
21.7 575 0.14 21.6
21.6 688 0.34 32.4
21.3 48 0.69 108.7
21.2 572 0.49 38.6
20.8 239 0.42 46.7
20.3 244 0.48 56.5
18.9 472 0.25 44.4
18.8 134 0.52 47.4
18.6 633 0.19 21.7
18 295 0.44 55.7
17.6 906 0.24 27.1
17 1045 0.16 28.5
16.7 775 0.1 41.6
15.9 619 0.15 44.6
15.3 901 0.05 26.1
15 910 0.24 18.7
14.8 556 0.22 49.1

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	'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=289905&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=289905&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=289905&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	'Gwilym Jenkins' @ jenkins.wessa.net

Correlations for all pairs of data series (method=pearson)
	HIV_Risk	Per_Capita_Income	Prop_Population_on_Farms	Homicides
HIV_Risk	1	-0.419	0.48	0.664
Per_Capita_Income	-0.419	1	-0.775	-0.745
Prop_Population_on_Farms	0.48	-0.775	1	0.696
Homicides	0.664	-0.745	0.696	1

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & HIV_Risk & Per_Capita_Income & Prop_Population_on_Farms & Homicides \tabularnewline
HIV_Risk & 1 & -0.419 & 0.48 & 0.664 \tabularnewline
Per_Capita_Income & -0.419 & 1 & -0.775 & -0.745 \tabularnewline
Prop_Population_on_Farms & 0.48 & -0.775 & 1 & 0.696 \tabularnewline
Homicides & 0.664 & -0.745 & 0.696 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=289905&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]HIV_Risk[/C][C]Per_Capita_Income[/C][C]Prop_Population_on_Farms[/C][C]Homicides[/C][/ROW]
[ROW][C]HIV_Risk[/C][C]1[/C][C]-0.419[/C][C]0.48[/C][C]0.664[/C][/ROW]
[ROW][C]Per_Capita_Income[/C][C]-0.419[/C][C]1[/C][C]-0.775[/C][C]-0.745[/C][/ROW]
[ROW][C]Prop_Population_on_Farms[/C][C]0.48[/C][C]-0.775[/C][C]1[/C][C]0.696[/C][/ROW]
[ROW][C]Homicides[/C][C]0.664[/C][C]-0.745[/C][C]0.696[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=289905&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=289905&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)
	HIV_Risk	Per_Capita_Income	Prop_Population_on_Farms	Homicides
HIV_Risk	1	-0.419	0.48	0.664
Per_Capita_Income	-0.419	1	-0.775	-0.745
Prop_Population_on_Farms	0.48	-0.775	1	0.696
Homicides	0.664	-0.745	0.696	1

Correlations for all pairs of data series with p-values
pair	Pearson r	Spearman rho	Kendall tau
HIV_Risk;Per_Capita_Income	-0.4191	-0.4232	-0.29
p-value	(0.0212)	(0.0198)	(0.0246)
HIV_Risk;Prop_Population_on_Farms	0.4804	0.4216	0.3287
p-value	(0.0072)	(0.0203)	(0.0117)
HIV_Risk;Homicides	0.6643	0.5293	0.359
p-value	(1e-04)	(0.0026)	(0.0054)
Per_Capita_Income;Prop_Population_on_Farms	-0.7755	-0.8293	-0.652
p-value	(0)	(0)	(0)
Per_Capita_Income;Homicides	-0.7455	-0.8541	-0.646
p-value	(0)	(0)	(0)
Prop_Population_on_Farms;Homicides	0.6964	0.737	0.5402
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
HIV_Risk;Per_Capita_Income & -0.4191 & -0.4232 & -0.29 \tabularnewline
p-value & (0.0212) & (0.0198) & (0.0246) \tabularnewline
HIV_Risk;Prop_Population_on_Farms & 0.4804 & 0.4216 & 0.3287 \tabularnewline
p-value & (0.0072) & (0.0203) & (0.0117) \tabularnewline
HIV_Risk;Homicides & 0.6643 & 0.5293 & 0.359 \tabularnewline
p-value & (1e-04) & (0.0026) & (0.0054) \tabularnewline
Per_Capita_Income;Prop_Population_on_Farms & -0.7755 & -0.8293 & -0.652 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Per_Capita_Income;Homicides & -0.7455 & -0.8541 & -0.646 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Prop_Population_on_Farms;Homicides & 0.6964 & 0.737 & 0.5402 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=289905&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]HIV_Risk;Per_Capita_Income[/C][C]-0.4191[/C][C]-0.4232[/C][C]-0.29[/C][/ROW]
[ROW][C]p-value[/C][C](0.0212)[/C][C](0.0198)[/C][C](0.0246)[/C][/ROW]
[ROW][C]HIV_Risk;Prop_Population_on_Farms[/C][C]0.4804[/C][C]0.4216[/C][C]0.3287[/C][/ROW]
[ROW][C]p-value[/C][C](0.0072)[/C][C](0.0203)[/C][C](0.0117)[/C][/ROW]
[ROW][C]HIV_Risk;Homicides[/C][C]0.6643[/C][C]0.5293[/C][C]0.359[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](0.0026)[/C][C](0.0054)[/C][/ROW]
[ROW][C]Per_Capita_Income;Prop_Population_on_Farms[/C][C]-0.7755[/C][C]-0.8293[/C][C]-0.652[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Per_Capita_Income;Homicides[/C][C]-0.7455[/C][C]-0.8541[/C][C]-0.646[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Prop_Population_on_Farms;Homicides[/C][C]0.6964[/C][C]0.737[/C][C]0.5402[/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=289905&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=289905&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
HIV_Risk;Per_Capita_Income	-0.4191	-0.4232	-0.29
p-value	(0.0212)	(0.0198)	(0.0246)
HIV_Risk;Prop_Population_on_Farms	0.4804	0.4216	0.3287
p-value	(0.0072)	(0.0203)	(0.0117)
HIV_Risk;Homicides	0.6643	0.5293	0.359
p-value	(1e-04)	(0.0026)	(0.0054)
Per_Capita_Income;Prop_Population_on_Farms	-0.7755	-0.8293	-0.652
p-value	(0)	(0)	(0)
Per_Capita_Income;Homicides	-0.7455	-0.8541	-0.646
p-value	(0)	(0)	(0)
Prop_Population_on_Farms;Homicides	0.6964	0.737	0.5402
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	0.83	0.67	0.67
0.02	0.83	0.83	0.83
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 & 0.83 & 0.67 & 0.67 \tabularnewline
0.02 & 0.83 & 0.83 & 0.83 \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=289905&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]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=289905&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=289905&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	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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code