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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, 07 Dec 2017 12:41:22 +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/2017/Dec/07/t1512647095othc7w4kaiv60le.htm/, Retrieved Wed, 15 May 2024 20:15:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=308697, Retrieved Wed, 15 May 2024 20:15:59 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact122

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] [Correlation Matri...] [2017-12-07 11:41:22] [0553ded3e3c4af9d05e2b3f12829db4c] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
7	8
10	8
7	10
8	8
7	9
9	8
8	10
7	8
7	6
6	9
7	8
9	10
8	7
8	9
8	8
3	10
8	10
10	4
8	6
8	7
7	7
7	8
10	6
9	4
8	6
6	9
9	10
5	9
4	7
8	10
6	7
7	10
9	8
8	7
9	8
9	8
6	9
7	9
5	6
6	8
8	10
10	7
5	5
8	9
7	10
9	6
8	9
9	3
8	7
5	9
3	8
5	8
5	7
9	9
8	9
5	8
6	10
8	8
7	10
5	9
8	6
6	5
7	3
8	6
9	6
4	10
9	9
4	9
7	6
6	7
8	8
8	9
6	10
5	7
7	6
8	8
6	7
8	5
7	10
7	10
8	6
9	4
8	5
4	7
7	10
5	8
9	2
8	7
8	9
7	8
5	10
6	10
5	8
8	10
9	6
4	6
6	9
8	8
8	4
8	9
8	5
8	8
8	8
10	9
8	9
4	7
4	8
9	8
3	9
9	9
4	2
10	8
5	8
8	8
7	7
10	10
10	8
4	10
8	5
5	10
8	8
9	7
8	2
8	9
8	8
8	5
8	8
6	7
6	10
8	6
8	7
7	8
7	4
9	10
9	6
8	7
6	7
8	8
2	10
6	3
7	2
8	10
6	6
10	10
10	10
10	9
8	6
5	5
2	4
6	6
8	6
8	8
5	8
5	5
8	7
7	6
9	8
7	8
4	9
10	10
8	8
4	9
5	9
8	4
8	8
6	9
7	10
3	10
8	7
7	8
7	8
8	6
7	8
9	5
9	9
6	9
6	8
8	9
3	10
8	7
10	10
2	8
6	7
5	10
4	7
7	9
6	8
6	8
7	8
6	7
4	10
9	3
7	2
7	3
7	4
7	10
8	9
6	8
6	9
NA	8
NA	7
NA	9
NA	4
NA	7
NA	8
NA	7
NA	6
NA	6
NA	9
NA	10
NA	8
NA	8
NA	9
NA	4
NA	10
NA	10
NA	9
NA	8
NA	8
NA	7
NA	9
NA	4
NA	6
NA	8
NA	6
NA	9
NA	6
NA	5
NA	8
NA	9
NA	4
NA	2
NA	8
NA	9
NA	5
NA	7
NA	8
NA	9
NA	9
NA	5
NA	9
NA	8
NA	6
NA	9
NA	8
NA	7
NA	10

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 time2 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308697&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]2 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=308697&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308697&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 time2 seconds
R ServerBig Analytics Cloud Computing Center






Correlations for all pairs of data series (method=pearson)
FM
F1-0.093
M-0.0931

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & F & M \tabularnewline
F & 1 & -0.093 \tabularnewline
M & -0.093 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308697&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]F[/C][C]M[/C][/ROW]
[ROW][C]F[/C][C]1[/C][C]-0.093[/C][/ROW]
[ROW][C]M[/C][C]-0.093[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308697&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308697&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)
FM
F1-0.093
M-0.0931






Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
F;M-0.0934-0.094-0.0744
p-value(0.1893)(0.1866)(0.1765)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
F;M & -0.0934 & -0.094 & -0.0744 \tabularnewline
p-value & (0.1893) & (0.1866) & (0.1765) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308697&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]F;M[/C][C]-0.0934[/C][C]-0.094[/C][C]-0.0744[/C][/ROW]
[ROW][C]p-value[/C][C](0.1893)[/C][C](0.1866)[/C][C](0.1765)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308697&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308697&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
F;M-0.0934-0.094-0.0744
p-value(0.1893)(0.1866)(0.1765)






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=308697&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=308697&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308697&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 = pearson ;
Parameters (R input):
par1 = pearson ;
R code (references can be found in the software module):
par1 <- 'pearson'
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')