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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationFri, 12 Dec 2014 20:50:43 +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/2014/Dec/12/t141841747658zzl1b5kbh3yma.htm/, Retrieved Thu, 16 May 2024 16:56:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266885, Retrieved Thu, 16 May 2024 16:56:49 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact84

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Kendall tau Correlation Matrix] [] [2014-12-12 20:50:43] [8145b3fe416df466b077d26de89041cd] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4 13
9 NA
4 8
5 14
4 16
4 14
9 13
8 15
11 13
4 20
4 17
6 15
4 16
8 12
4 17
4 11
11 16
4 16
4 15
6 13
6 14
4 19
8 16
5 17
4 10
9 15
4 14
7 14
10 16
4 15
4 17
7 14
12 16
4 NA
7 15
5 16
8 16
5 10
4 8
9 17
7 14
4 10
4 14
4 12
4 16
4 16
7 16
4 8
7 16
4 15
4 8
4 13
4 14
8 13
4 16
4 19
4 19
4 14
7 15
12 13
4 10
4 16
4 15
5 11
15 9
5 16
10 12
9 12
8 14
4 14
5 13
4 15
9 17
4 14
10 11
4 9
7 NA
4 7
6 13
7 15
5 12
4 15
4 14
4 16
4 14
4 13
4 16
6 13
10 16
7 16
4 16
4 10
7 12
4 12
8 12
11 12
6 19
14 14
5 13
4 16
8 15
9 12
4 8
4 10
5 16
4 16
5 10
4 18
4 12
7 16
10 10
4 14
5 12
4 11
4 15
4 7
6 16
4 16
8 16
5 16
4 12
17 15
4 14
4 15
8 16
4 13
7 10
4 17
4 15
5 18
7 16
4 20
4 16
7 17
11 16
7 15
4 13
4 16
4 16
4 16
4 17
4 20
6 14
8 17
23 6
4 16
8 15
6 16
4 NA
4 16
7 14
4 16
4 NA
4 16
4 16
10 14
6 14
5 16
5 16
4 15
4 16
5 16
5 18
5 15
5 16
4 16
6 16
4 17
4 14
4 18
9 9
18 15
6 14
5 15
4 13
11 16
4 20
10 14
6 12
8 15
8 15
6 15
8 16
4 11
4 16
9 7
9 11
5 9
4 15
4 16
15 14
10 15
9 13
7 13
9 12
6 16
4 14
7 16
4 14
7 15
4 10
15 16
4 14
9 16
4 12
4 16
28 16
4 15
4 14
4 16
5 11
4 15
4 18
12 13
5 NA
4 7
6 7
6 17
5 18
4 15
4 8
4 13
10 13
7 15
4 NA
4 18
7 16
4 14
4 15
12 19
5 16
8 12
6 16
17 11
4 16
5 15
4 19
5 15
5 14
6 14
4 17
4 16
4 20
6 16
8 9
10 13
4 15
5 19
4 16
4 17
4 16
16 9
4 NA
7 11
4 14
4 19
14 13
5 14
5 15
5 15
5 14
7 16
19 17
16 12
4 15
4 17
7 15
9 10
5 16
14 15
4 11
16 16
10 16
5 16
6 14
4 14
4 16
4 16
5 18
4 14
4 20
5 15
4 16
4 16
5 16
8 12
15 8

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'George Udny Yule' @ yule.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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266885&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266885&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266885&T=0

As an alternative you can also use a QR Code:  
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'George Udny Yule' @ yule.wessa.net






Correlations for all pairs of data series (method=pearson)
AMS.ACONFSTATTOT
AMS.A1-0.155
CONFSTATTOT-0.1551

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & AMS.A & CONFSTATTOT \tabularnewline
AMS.A & 1 & -0.155 \tabularnewline
CONFSTATTOT & -0.155 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266885&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]AMS.A[/C][C]CONFSTATTOT[/C][/ROW]
[ROW][C]AMS.A[/C][C]1[/C][C]-0.155[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]-0.155[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266885&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266885&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Correlations for all pairs of data series (method=pearson)
AMS.ACONFSTATTOT
AMS.A1-0.155
CONFSTATTOT-0.1551






Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
AMS.A;CONFSTATTOT-0.1551-0.1449-0.1158
p-value(0.0095)(0.0154)(0.0131)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
AMS.A;CONFSTATTOT & -0.1551 & -0.1449 & -0.1158 \tabularnewline
p-value & (0.0095) & (0.0154) & (0.0131) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266885&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]AMS.A;CONFSTATTOT[/C][C]-0.1551[/C][C]-0.1449[/C][C]-0.1158[/C][/ROW]
[ROW][C]p-value[/C][C](0.0095)[/C][C](0.0154)[/C][C](0.0131)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266885&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266885&T=2

As an alternative you can also use a QR Code:  
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
AMS.A;CONFSTATTOT-0.1551-0.1449-0.1158
p-value(0.0095)(0.0154)(0.0131)






Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.01100
0.02111
0.03111
0.04111
0.05111
0.06111
0.07111
0.08111
0.09111
0.1111

\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 & 0 & 0 \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=266885&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]0[/C][C]0[/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=266885&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266885&T=3

As an alternative you can also use a QR Code:  
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
0.01100
0.02111
0.03111
0.04111
0.05111
0.06111
0.07111
0.08111
0.09111
0.1111


Figures (Output of Computation)

Input Parameters & R Code

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