FreeStatistics.org

Free Statistics

of Irreproducible Research!

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, 22 Jan 2016 08:54:14 +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/2016/Jan/22/t1453452866qsrojgtpsod713u.htm/, Retrieved Sun, 10 Nov 2024 19:41:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=290528, Retrieved Sun, 10 Nov 2024 19:41:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact85

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] [] [2016-01-22 08:54:14] [968c2d3e3424d11fe6e5c9ca66798528] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
6 3.2 10.24
7 3.3 10.89
2 3 9
11 3.5 12.25
13 3.7 13.69
3 2.7 7.29
17 3.6 12.96
10 3.5 12.25
4 3.8 14.44
12 3.4 11.56
7 3.7 13.69
11 3.5 12.25
3 2.8 7.84
5 3.8 14.44
1 4.3 18.49
12 3.3 10.89
18 3.6 12.96
8 3.6 12.96
6 3.3 10.89
1 2.8 7.84

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 0 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=290528&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]0 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=290528&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=290528&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 time0 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Correlations for all pairs of data series (method=pearson)
NumeracyGebgewichtGebgew2
Numeracy10.2730.221
Gebgewicht0.27310.997
Gebgew20.2210.9971

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & Numeracy & Gebgewicht & Gebgew2 \tabularnewline
Numeracy & 1 & 0.273 & 0.221 \tabularnewline
Gebgewicht & 0.273 & 1 & 0.997 \tabularnewline
Gebgew2 & 0.221 & 0.997 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=290528&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]Numeracy[/C][C]Gebgewicht[/C][C]Gebgew2[/C][/ROW]
[ROW][C]Numeracy[/C][C]1[/C][C]0.273[/C][C]0.221[/C][/ROW]
[ROW][C]Gebgewicht[/C][C]0.273[/C][C]1[/C][C]0.997[/C][/ROW]
[ROW][C]Gebgew2[/C][C]0.221[/C][C]0.997[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=290528&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=290528&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)
NumeracyGebgewichtGebgew2
Numeracy10.2730.221
Gebgewicht0.27310.997
Gebgew20.2210.9971






Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Numeracy;Gebgewicht0.27310.2770.2044
p-value(0.244)(0.2371)(0.2251)
Numeracy;Gebgew20.22090.2770.2044
p-value(0.3494)(0.2371)(0.2251)
Gebgewicht;Gebgew20.996911
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
Numeracy;Gebgewicht & 0.2731 & 0.277 & 0.2044 \tabularnewline
p-value & (0.244) & (0.2371) & (0.2251) \tabularnewline
Numeracy;Gebgew2 & 0.2209 & 0.277 & 0.2044 \tabularnewline
p-value & (0.3494) & (0.2371) & (0.2251) \tabularnewline
Gebgewicht;Gebgew2 & 0.9969 & 1 & 1 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=290528&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]Numeracy;Gebgewicht[/C][C]0.2731[/C][C]0.277[/C][C]0.2044[/C][/ROW]
[ROW][C]p-value[/C][C](0.244)[/C][C](0.2371)[/C][C](0.2251)[/C][/ROW]
[ROW][C]Numeracy;Gebgew2[/C][C]0.2209[/C][C]0.277[/C][C]0.2044[/C][/ROW]
[ROW][C]p-value[/C][C](0.3494)[/C][C](0.2371)[/C][C](0.2251)[/C][/ROW]
[ROW][C]Gebgewicht;Gebgew2[/C][C]0.9969[/C][C]1[/C][C]1[/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=290528&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=290528&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
Numeracy;Gebgewicht0.27310.2770.2044
p-value(0.244)(0.2371)(0.2251)
Numeracy;Gebgew20.22090.2770.2044
p-value(0.3494)(0.2371)(0.2251)
Gebgewicht;Gebgew20.996911
p-value(0)(0)(0)






Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.010.330.330.33
0.020.330.330.33
0.030.330.330.33
0.040.330.330.33
0.050.330.330.33
0.060.330.330.33
0.070.330.330.33
0.080.330.330.33
0.090.330.330.33
0.10.330.330.33

\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 & 0.33 & 0.33 & 0.33 \tabularnewline
0.02 & 0.33 & 0.33 & 0.33 \tabularnewline
0.03 & 0.33 & 0.33 & 0.33 \tabularnewline
0.04 & 0.33 & 0.33 & 0.33 \tabularnewline
0.05 & 0.33 & 0.33 & 0.33 \tabularnewline
0.06 & 0.33 & 0.33 & 0.33 \tabularnewline
0.07 & 0.33 & 0.33 & 0.33 \tabularnewline
0.08 & 0.33 & 0.33 & 0.33 \tabularnewline
0.09 & 0.33 & 0.33 & 0.33 \tabularnewline
0.1 & 0.33 & 0.33 & 0.33 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=290528&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]0.33[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.02[/C][C]0.33[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.03[/C][C]0.33[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.04[/C][C]0.33[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.05[/C][C]0.33[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.06[/C][C]0.33[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.07[/C][C]0.33[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.08[/C][C]0.33[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.09[/C][C]0.33[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.1[/C][C]0.33[/C][C]0.33[/C][C]0.33[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=290528&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=290528&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
0.010.330.330.33
0.020.330.330.33
0.030.330.330.33
0.040.330.330.33
0.050.330.330.33
0.060.330.330.33
0.070.330.330.33
0.080.330.330.33
0.090.330.330.33
0.10.330.330.33


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
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')