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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 computationThu, 31 Jan 2019 08:47:36 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2019/Jan/31/t1548921016uh44abeptdpyg6f.htm/, Retrieved Sun, 05 May 2024 19:26:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=317127, Retrieved Sun, 05 May 2024 19:26:57 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact101

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-       [Kendall tau Correlation Matrix] [] [2019-01-31 07:47:36] [9e2b27cf675a1d7b8e7da04c0e26bb6a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
14 13
19 16
17 17
17 NA
15 NA
20 16
15 NA
19 NA
15 NA
15 17
19 17
NA 15
20 16
18 14
15 16
14 17
20 NA
NA NA
16 NA
16 NA
16 16
10 NA
19 16
19 NA
16 NA
15 NA
18 16
17 15
19 16
17 16
NA 13
19 15
20 17
5 NA
19 13
16 17
15 NA
16 14
18 14
16 18
15 NA
17 17
NA 13
20 16
19 15
7 15
13 NA
16 15
16 13
NA NA
18 17
18 NA
16 NA
17 11
19 14
16 13
19 NA
13 17
16 16
13 NA
12 17
17 16
17 16
17 16
16 15
16 12
14 17
16 14
13 14
16 16
14 NA
20 NA
12 NA
13 NA
18 NA
14 15
19 16
18 14
14 15
18 17
19 NA
15 10
14 NA
17 17
19 NA
13 20
19 17
18 18
20 NA
15 17
15 14
15 NA
20 17
15 NA
19 17
18 NA
18 16
15 18
20 18
17 16
12 NA
18 NA
19 15
20 13
NA NA
17 NA
15 NA
16 NA
18 NA
18 16
14 NA
15 NA
12 NA
17 12
14 NA
18 16
17 16
17 NA
20 16
16 14
14 15
15 14
18 NA
20 15
17 NA
17 15
17 16
17 NA
15 NA
17 NA
18 11
17 NA
20 18
15 NA
16 11
15 NA
18 18
11 NA
15 15
18 19
20 17
19 NA
14 14
16 NA
15 13
17 17
18 14
20 19
17 14
18 NA
15 NA
16 16
11 16
15 15
18 12
17 NA
16 17
12 NA
19 NA
18 18
15 15
17 18
19 15
18 NA
19 NA
16 NA
16 16
16 NA
14 16

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig 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=317127&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=317127&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317127&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center






Correlations for all pairs of data series (method=pearson)
ITHSUMTVDC
ITHSUM10.108
TVDC0.1081

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & ITHSUM & TVDC \tabularnewline
ITHSUM & 1 & 0.108 \tabularnewline
TVDC & 0.108 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317127&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]ITHSUM[/C][C]TVDC[/C][/ROW]
[ROW][C]ITHSUM[/C][C]1[/C][C]0.108[/C][/ROW]
[ROW][C]TVDC[/C][C]0.108[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=317127&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317127&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)
ITHSUMTVDC
ITHSUM10.108
TVDC0.1081






Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
ITHSUM;TVDC0.10810.15920.1228
p-value(0.2845)(0.1137)(0.1102)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
ITHSUM;TVDC & 0.1081 & 0.1592 & 0.1228 \tabularnewline
p-value & (0.2845) & (0.1137) & (0.1102) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317127&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]ITHSUM;TVDC[/C][C]0.1081[/C][C]0.1592[/C][C]0.1228[/C][/ROW]
[ROW][C]p-value[/C][C](0.2845)[/C][C](0.1137)[/C][C](0.1102)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=317127&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317127&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
ITHSUM;TVDC0.10810.15920.1228
p-value(0.2845)(0.1137)(0.1102)






Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.01000
0.02000
0.03000
0.04000
0.05000
0.06000
0.07000
0.08000
0.09000
0.1000

\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 & 0 & 0 \tabularnewline
0.02 & 0 & 0 & 0 \tabularnewline
0.03 & 0 & 0 & 0 \tabularnewline
0.04 & 0 & 0 & 0 \tabularnewline
0.05 & 0 & 0 & 0 \tabularnewline
0.06 & 0 & 0 & 0 \tabularnewline
0.07 & 0 & 0 & 0 \tabularnewline
0.08 & 0 & 0 & 0 \tabularnewline
0.09 & 0 & 0 & 0 \tabularnewline
0.1 & 0 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=317127&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[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]0.02[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]0.03[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]0.04[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]0.05[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]0.06[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]0.07[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]0.08[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]0.09[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]0.1[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=317127&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=317127&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
0.01000
0.02000
0.03000
0.04000
0.05000
0.06000
0.07000
0.08000
0.09000
0.1000


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = two.sided ; par2 = 0.95 ; par3 = 0 ;
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')