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 computationWed, 17 Dec 2014 12:28:28 +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/17/t1418819346qefex8hv4nw0e7g.htm/, Retrieved Thu, 16 May 2024 16:39:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270124, Retrieved Thu, 16 May 2024 16:39:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact70

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] [TOT vs Numeracy] [2014-12-17 12:28:28] [8a5be748fffbe1272db475ee7e612f22] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12.9	21
7.4	26
12.2	22
12.8	22
7.4	18
6.7	23
12.6	12
14.8	20
13.3	22
11.1	21
8.2	19
11.4	22
6.4	15
10.6	20
12.0	19
6.3	18
11.3	15
11.9	20
9.3	21
9.6	21
10.0	15
6.4	16
13.8	23
10.8	21
13.8	18
11.7	25
10.9	9
16.1	30
13.4	20
9.9	23
11.5	16
8.3	16
11.7	19
6.1	25
9.0	25
9.7	18
10.8	23
10.3	21
10.4	10
12.7	14
9.3	22
11.8	26
5.9	23
11.4	23
13.0	24
10.8	24
12.3	18
11.3	23
11.8	15
7.9	19
12.7	16
12.3	25
11.6	23
6.7	17
10.9	19
12.1	21
13.3	18
10.1	27
5.7	21
14.3	13
8.0	8
13.3	29
9.3	28
12.5	23
7.6	21
15.9	19
9.2	19
9.1	20
11.1	18
13.0	19
14.5	17
12.2	19
12.3	25
11.4	19
8.8	22
14.6	23
7.3	26
12.6	14
NA	28
13.0	16
12.6	24
13.2	20
9.9	12
7.7	24
10.5	22
13.4	12
10.9	22
4.3	20
10.3	10
11.8	23
11.2	17
11.4	22
8.6	24
13.2	18
12.6	21
5.6	20
9.9	20
8.8	22
7.7	19
9.0	20
7.3	26
11.4	23
13.6	24
7.9	21
10.7	21
10.3	19
8.3	8
9.6	17
14.2	20
8.5	11
13.5	8
4.9	15
6.4	18
9.6	18
11.6	19
11.1	19
4.4	23
12.7	22
18.1	21
17.9	25
16.6	30
12.6	17
17.1	27
19.1	23
16.1	23
13.4	18
18.4	18
14.7	23
10.6	19
12.6	15
16.2	20
13.6	16
18.9	24
14.1	25
14.5	25
16.2	19
14.8	19
14.8	16
12.5	19
12.7	19
17.4	23
8.6	21
18.4	22
16.1	19
11.6	20
17.8	20
15.3	3
17.7	23
15.6	14
16.4	23
17.7	20
13.6	15
11.7	13
14.4	16
14.8	7
18.3	24
9.9	17
16.0	24
18.3	24
16.9	19
14.6	25
13.9	20
19.0	28
15.6	23
14.9	27
11.8	18
18.5	28
15.9	21
17.1	19
16.1	23
19.9	27
11.0	22
18.5	28
15.1	25
15.0	21
11.4	22
16.0	28
18.1	20
14.6	29
15.4	25
15.4	25
17.6	20
13.4	20
19.1	16
15.4	20
7.6	20
13.4	23
13.9	18
19.1	25
15.3	18
12.9	19
16.1	25
17.4	25
13.2	25
12.2	24
12.6	19
10.4	26
15.4	10
9.6	17
18.2	13
13.6	17
14.9	30
14.8	25
14.1	4
14.9	16
16.3	21
19.3	23
13.6	22
13.6	17
15.7	20
12.8	20
14.6	22
9.9	16
12.7	23
11.9	16
19.2	0
16.6	18
11.2	25
15.3	23
11.9	12
13.2	18
16.4	24
12.4	11
15.9	18
14.4	14
18.2	23
11.2	24
15.7	29
17.8	18
7.7	15
12.4	29
15.6	16
19.3	19
15.2	22
17.1	16
15.6	23
18.4	23
19.1	19
18.6	4
19.1	20
13.1	24
12.9	20
9.5	4
4.5	24
11.9	22
13.6	16
11.7	3
12.4	15
13.4	24
11.4	17
14.9	20
19.9	27
17.8	23
11.2	26
14.6	23
17.6	17
14.1	20
16.1	22
13.4	19
11.9	24
12.0	19
14.8	23
15.2	15
13.2	27
16.9	26
7.9	22
7.7	22
12.6	18
7.9	15
11.0	22
12.4	27
10.0	10
14.9	20
16.7	17
13.4	23
14.0	19
15.7	13
16.9	27
11.0	23
15.4	16
12.2	25
15.1	2
17.8	26
15.2	20
14.6	23
16.7	22
8.1	24

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'Sir Ronald Aylmer Fisher' @ fisher.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 & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270124&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270124&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270124&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'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Correlations for all pairs of data series (method=pearson)
TOTNumeracyTOT
TOT10.099
NumeracyTOT0.0991

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & TOT & NumeracyTOT \tabularnewline
TOT & 1 & 0.099 \tabularnewline
NumeracyTOT & 0.099 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270124&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]TOT[/C][C]NumeracyTOT[/C][/ROW]
[ROW][C]TOT[/C][C]1[/C][C]0.099[/C][/ROW]
[ROW][C]NumeracyTOT[/C][C]0.099[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270124&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270124&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)
TOTNumeracyTOT
TOT10.099
NumeracyTOT0.0991






Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
TOT;NumeracyTOT0.0990.1350.0936
p-value(0.0948)(0.0224)(0.0226)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
TOT;NumeracyTOT & 0.099 & 0.135 & 0.0936 \tabularnewline
p-value & (0.0948) & (0.0224) & (0.0226) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270124&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]TOT;NumeracyTOT[/C][C]0.099[/C][C]0.135[/C][C]0.0936[/C][/ROW]
[ROW][C]p-value[/C][C](0.0948)[/C][C](0.0224)[/C][C](0.0226)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270124&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270124&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
TOT;NumeracyTOT0.0990.1350.0936
p-value(0.0948)(0.0224)(0.0226)






Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.01000
0.02000
0.03011
0.04011
0.05011
0.06011
0.07011
0.08011
0.09011
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 & 0 & 0 & 0 \tabularnewline
0.02 & 0 & 0 & 0 \tabularnewline
0.03 & 0 & 1 & 1 \tabularnewline
0.04 & 0 & 1 & 1 \tabularnewline
0.05 & 0 & 1 & 1 \tabularnewline
0.06 & 0 & 1 & 1 \tabularnewline
0.07 & 0 & 1 & 1 \tabularnewline
0.08 & 0 & 1 & 1 \tabularnewline
0.09 & 0 & 1 & 1 \tabularnewline
0.1 & 1 & 1 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270124&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]1[/C][C]1[/C][/ROW]
[ROW][C]0.04[/C][C]0[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.05[/C][C]0[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.06[/C][C]0[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.07[/C][C]0[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.08[/C][C]0[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.09[/C][C]0[/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=270124&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270124&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
0.03011
0.04011
0.05011
0.06011
0.07011
0.08011
0.09011
0.1111


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