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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, 22 Dec 2016 20:53:06 +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/2016/Dec/22/t1482436797zqj7u4kd9pumrx2.htm/, Retrieved Mon, 29 Apr 2024 04:44:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=302668, Retrieved Mon, 29 Apr 2024 04:44:12 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact42

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 matrix ] [2016-12-22 19:53:06] [0c0812954c36a879765e84a6a68f060c] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3	5
5	4
4	4
3	5
2	5
4	5
5	5
3	5
2	4
2	5
4	5
4	5
4	4
5	5
2	3
4	5
5	3
3	4
4	4
3	5
3	4
5	5
4	4
4	5
3	4
3	4
3	5
3	5
3	5
5	5
3	2
3	4
4	4
3	5
1	5
4	5
4	4
3	4
4	5
5	5
4	4
4	4
3	5
2	5
4	5
3	4
3	5
4	4
3	5
4	5
5	4
3	5
3	3
3	3
4	5
3	4
4	4
3	5
1	5
3	5
2	4
3	4
3	4
4	5
5	4
3	4
4	4
2	4
2	4
3	4
4	4
3	5
4	5
4	4
3	4
4	3
3	4
5	2
4	4
4	5
3	3
4	4
1	5
4	2
4	5
4	5
4	5
4	4
3	4
5	5
5	4
4	4
4	5
4	5
3	4
4	5
3	5
3	4
3	5
2	5
4	4
3	4
2	4
4	5
4	5
4	4
5	5
4	5
4	4
4	4
4	4
5	5
4	4
5	4
4	3
5	4
2	5
4	4
5	4
3	5
4	3
4	4
3	1
5	5
5	4
3	5
2	3
4	4
3	4
3	4
4	5
2	5
3	5
4	5
1	5
4	4
3	3
3	4
2	4
3	5
3	4
5	4

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time0 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 time0 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302668&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]0 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=302668&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302668&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 time0 seconds
R ServerBig Analytics Cloud Computing Center






Correlations for all pairs of data series (method=pearson)
V1V2
V11-0.041
V2-0.0411

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & V1 & V2 \tabularnewline
V1 & 1 & -0.041 \tabularnewline
V2 & -0.041 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302668&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][/ROW]
[ROW][C]V1[/C][C]1[/C][C]-0.041[/C][/ROW]
[ROW][C]V2[/C][C]-0.041[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302668&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302668&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)
V1V2
V11-0.041
V2-0.0411






Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
V1;V2-0.0407-0.0251-0.0218
p-value(0.6302)(0.767)(0.771)

\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 & -0.0407 & -0.0251 & -0.0218 \tabularnewline
p-value & (0.6302) & (0.767) & (0.771) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302668&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]-0.0407[/C][C]-0.0251[/C][C]-0.0218[/C][/ROW]
[ROW][C]p-value[/C][C](0.6302)[/C][C](0.767)[/C][C](0.771)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302668&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302668&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
V1;V2-0.0407-0.0251-0.0218
p-value(0.6302)(0.767)(0.771)






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=302668&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=302668&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302668&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 = 36 ; par2 = 1 ; par3 = 0 ; par4 = 2 ; par5 = 6 ; par6 = White Noise ; par7 = 0.95 ;
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')