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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 computationMon, 15 Dec 2014 21:34:54 +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/15/t14186793019joujkx0fvr6xpj.htm/, Retrieved Thu, 16 May 2024 18:35:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269050, Retrieved Thu, 16 May 2024 18:35:24 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact94

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] [] [2014-12-14 18:20:59] [c3af58d916586065e82a9492c7f087b1]
- R P     [Kendall tau Correlation Matrix] [] [2014-12-15 21:34:54] [8145b3fe416df466b077d26de89041cd] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
26 50 21
51 68 26
57 62 22
37 54 22
67 71 18
43 54 23
52 65 12
52 73 20
43 52 22
84 84 21
67 42 19
49 66 22
70 65 15
52 78 20
58 73 19
68 75 18
62 72 15
43 66 20
56 70 21
56 61 21
74 81 15
65 71 16
63 69 23
58 71 21
57 72 18
63 68 25
53 70 9
57 68 30
51 61 20
64 67 23
53 76 16
29 70 16
54 60 19
51 77 25
58 72 25
43 69 18
51 71 23
53 62 21
54 70 10
56 64 14
61 58 22
47 76 26
39 52 23
48 59 23
50 68 24
35 76 24
30 65 18
68 67 23
49 59 15
61 69 19
67 76 16
47 63 25
56 75 23
50 63 17
43 60 19
67 73 21
62 63 18
57 70 27
41 75 21
54 66 13
45 63 8
48 63 29
61 64 28
56 70 23
41 75 21
43 61 19
53 60 19
44 62 20
66 73 18
58 61 19
46 66 17
37 64 19
51 59 25
51 64 19
56 60 22
66 56 23
45 66 26
37 78 14
59 53 28
42 67 16
38 59 24
66 66 20
34 68 12
53 71 24
49 66 22
55 73 12
49 72 22
59 71 20
40 59 10
58 64 23
60 66 17
63 78 22
56 68 24
54 73 18
52 62 21
34 65 20
69 68 20
32 65 22
48 60 19
67 71 20
58 65 26
57 68 23
42 64 24
64 74 21
58 69 21
66 76 19
26 68 8
61 72 17
52 67 20
51 63 11
55 59 8
50 73 15
60 66 18
56 62 18
63 69 19
61 66 19
52 51 23
16 56 22
46 67 21
56 69 25
52 57 30
55 56 17
50 55 27
59 63 23
60 67 23
52 65 18
44 47 18
67 76 23
52 64 19
55 68 15
37 64 20
54 65 16
72 71 24
51 63 25
48 60 25
60 68 19
50 72 19
63 70 16
33 61 19
67 61 19
46 62 23
54 71 21
59 71 22
61 51 19
33 56 20
47 70 20
69 73 3
52 76 23
55 59 14
55 68 23
41 48 20
73 52 15
51 59 13
52 60 16
50 59 7
51 57 24
60 79 17
56 60 24
56 60 24
29 59 19
66 62 25
66 59 20
73 61 28
55 71 23
64 57 27
40 66 18
46 63 28
58 69 21
43 58 19
61 59 23
51 48 27
50 66 22
52 73 28
54 67 25
66 61 21
61 68 22
80 75 28
51 62 20
56 69 29
56 58 25
56 60 25
53 74 20
47 55 20
25 62 16
47 63 20
46 69 20
50 58 23
39 58 18
51 68 25
58 72 18
35 62 19
58 62 25
60 65 25
62 69 25
63 66 24
53 72 19
46 62 26
67 75 10
59 58 17
64 66 13
38 55 17
50 47 30
48 72 25
48 62 4
47 64 16
66 64 21
47 19 23
63 50 22
58 68 17
44 70 20
51 79 20
43 69 22
55 71 16
38 48 23
56 66 16
45 73 0
50 74 18
54 66 25
57 71 23
60 74 12
55 78 18
56 75 24
49 53 11
37 60 18
43 50 14
59 70 23
46 69 24
51 65 29
58 78 18
64 78 15
53 59 29
48 72 16
51 70 19
47 63 22
59 63 16
62 71 23
62 74 23
51 67 19
64 66 4
52 62 20
67 80 24
50 73 20
54 67 4
58 61 24
56 73 22
63 74 16
31 32 3
65 69 15
71 69 24
50 84 17
57 64 20
47 58 27
54 60 23
47 59 26
57 78 23
43 57 17
41 60 20
63 68 22
63 68 19
56 73 24
51 69 19
50 67 23
22 60 15
41 65 27
59 66 26
56 74 22
66 81 22
53 72 18
42 55 15
52 49 22
54 74 27
44 53 10
62 64 20
53 65 17
50 57 23
36 51 19
76 80 13
66 67 27
62 70 23
59 74 16
47 75 25
55 70 2
58 69 26
60 65 20
44 55 23
57 71 22
45 65 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'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269050&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269050&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269050&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'Herman Ole Andreas Wold' @ wold.wessa.net






Correlations for all pairs of data series (method=kendall)
AMS.IAMS.ENUMERACYTOT
AMS.I10.2430.035
AMS.E0.2431-0.046
NUMERACYTOT0.035-0.0461

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & AMS.I & AMS.E & NUMERACYTOT \tabularnewline
AMS.I & 1 & 0.243 & 0.035 \tabularnewline
AMS.E & 0.243 & 1 & -0.046 \tabularnewline
NUMERACYTOT & 0.035 & -0.046 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269050&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]AMS.I[/C][C]AMS.E[/C][C]NUMERACYTOT[/C][/ROW]
[ROW][C]AMS.I[/C][C]1[/C][C]0.243[/C][C]0.035[/C][/ROW]
[ROW][C]AMS.E[/C][C]0.243[/C][C]1[/C][C]-0.046[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]0.035[/C][C]-0.046[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269050&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269050&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=kendall)
AMS.IAMS.ENUMERACYTOT
AMS.I10.2430.035
AMS.E0.2431-0.046
NUMERACYTOT0.035-0.0461






Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
AMS.I;AMS.E0.34410.34380.2425
p-value(0)(0)(0)
AMS.I;NUMERACYTOT0.08680.0540.0355
p-value(0.1425)(0.3617)(0.3915)
AMS.E;NUMERACYTOT-0.0345-0.0653-0.0463
p-value(0.5608)(0.2703)(0.265)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
AMS.I;AMS.E & 0.3441 & 0.3438 & 0.2425 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
AMS.I;NUMERACYTOT & 0.0868 & 0.054 & 0.0355 \tabularnewline
p-value & (0.1425) & (0.3617) & (0.3915) \tabularnewline
AMS.E;NUMERACYTOT & -0.0345 & -0.0653 & -0.0463 \tabularnewline
p-value & (0.5608) & (0.2703) & (0.265) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269050&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]AMS.I;AMS.E[/C][C]0.3441[/C][C]0.3438[/C][C]0.2425[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]AMS.I;NUMERACYTOT[/C][C]0.0868[/C][C]0.054[/C][C]0.0355[/C][/ROW]
[ROW][C]p-value[/C][C](0.1425)[/C][C](0.3617)[/C][C](0.3915)[/C][/ROW]
[ROW][C]AMS.E;NUMERACYTOT[/C][C]-0.0345[/C][C]-0.0653[/C][C]-0.0463[/C][/ROW]
[ROW][C]p-value[/C][C](0.5608)[/C][C](0.2703)[/C][C](0.265)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269050&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269050&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
AMS.I;AMS.E0.34410.34380.2425
p-value(0)(0)(0)
AMS.I;NUMERACYTOT0.08680.0540.0355
p-value(0.1425)(0.3617)(0.3915)
AMS.E;NUMERACYTOT-0.0345-0.0653-0.0463
p-value(0.5608)(0.2703)(0.265)






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=269050&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=269050&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269050&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):
par1 = Default ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
Parameters (R input):
par1 = kendall ;
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$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
n <- length(y[,1])
n
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ',header=TRUE)
for (i in 1:n) {
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
for (j in 1:n) {
r <- cor.test(y[i,],y[j,],method=par1)
a<-table.element(a,round(r$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')