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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 21:24:57 +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/t1418678730m333v1wyperc0et.htm/, Retrieved Sun, 19 May 2024 16:13:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269046, Retrieved Sun, 19 May 2024 16:13:03 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact56

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [(Partial) Autocorrelation Function] [] [2014-12-15 19:51:27] [c3af58d916586065e82a9492c7f087b1]
- RMPD    [Kendall tau Correlation Matrix] [] [2014-12-15 21:24:57] [8145b3fe416df466b077d26de89041cd] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
45 73 7
31 32 15
54 67 20
50 59 16
45 63 10
53 70 14
54 70 8
34 68 14
55 73 13
60 74 15
51 59 NA
76 80 16
37 78 7
55 59 NA
43 50 NA
70 65 16
50 73 14
73 52 16
65 71 19
53 76 17
67 76 8
54 65 20
63 70 16
52 60 16
25 62 11
47 64 12
55 71 18
59 63 16
59 74 14
60 66 16
61 72 18
59 58 14
38 55 10
58 68 14
50 84 17
43 57 19
67 71 16
68 75 11
57 72 10
62 63 19
54 73 12
56 62 11
52 65 13
40 66 16
58 72 16
55 78 8
58 78 15
67 42 17
58 73 17
61 69 15
43 60 16
58 61 14
37 64 15
51 64 14
66 76 16
63 69 15
61 66 7
52 64 15
50 72 13
33 61 16
67 61 16
29 59 15
43 58 14
36 51 14
43 66 16
66 66 15
67 71 16
52 67 12
47 70 16
47 63 16
44 70 16
50 73 16
57 64 16
60 65 16
26 50 13
84 84 20
56 70 15
67 73 19
64 74 10
54 71 20
58 69 17
66 61 13
66 64 16
57 62 8
49 66 14
49 72 16
63 78 10
16 56 16
63 50 15
43 69 15
56 74 17
43 54 14
64 67 15
39 52 14
48 59 12
68 67 8
56 75 14
66 56 9
59 63 15
67 76 17
46 62 17
55 68 16
61 59 18
59 70 18
62 74 19
54 60 NA
57 78 14
50 68 16
35 76 16
53 71 16
42 64 8
72 71 16
56 75 13
67 80 17
71 69 16
51 77 NA
47 63 13
66 62 16
51 68 15
48 72 14
47 75 20
47 76 10
46 62 14
59 66 15
58 69 16
57 70 14
50 55 14
54 74 11
66 67 16
61 64 15
80 75 20
48 63 16
51 65 14
52 57 12
55 70 15
64 66 14
26 68 10
55 68 18
65 69 19
42 55 16
56 66 NA
46 66 13
53 65 16
43 69 16
60 66 12
39 58 9
43 61 16
48 60 13
51 67 15
63 68 15
51 69 14
66 59 16
51 79 11
58 71 17
53 62 10
58 69 16
37 54 14
63 68 14
56 70 11
55 71 15
57 71 18
62 71 15
62 70 18
44 55 16
38 59 12
56 60 16
56 60 16
56 73 15
56 69 16
54 67 15
64 57 16
73 61 18
53 59 16
74 81 14
60 79 14
50 74 7
53 72 16
51 70 16
59 71 13
69 68 19
53 74 15
52 62 14
56 61 13
49 66 15
59 71 14
52 51 16
52 76 16
50 57 14
58 61 16
54 66 17
59 53 13
46 63 16
52 73 14
56 69 12
67 75 16
51 63 16
64 66 15
49 59 16
29 70 14
42 67 15
30 65 16
44 47 10
37 60 15
60 68 15
37 64 16
41 48 14
41 75 15
61 58 14
66 81 15
58 64 16
50 67 16
56 68 12
46 69 16
58 72 15
51 63 17
62 69 13
45 66 NA
47 59 11
57 68 14
69 73 15
48 72 12
50 63 13
66 73 14
61 51 17
52 73 15
52 78 12
47 55 16
52 62 12
46 67 16
56 73 9
57 71 12
63 69 16
51 71 16
60 67 16
56 58 15
56 60 15
58 65 15
48 62 16
52 65 13
56 64 17
53 72 10
44 62 12
46 69 7
57 68 12
50 58 11
63 66 12
63 68 15
51 59 17
60 65 13
51 68 NA
51 48 9
55 59 10
40 59 16
49 53 13
63 74 13
53 60 12
51 61 16
51 62 14
62 64 16
56 60 11
51 57 14
58 62 15
62 72 16
34 65 12
43 52 13
61 68 16
48 60 16
54 66 13
64 78 19
54 60 16
38 48 13
41 60 13
32 65 14
52 49 15
35 62 14
41 75 9
45 65 8
50 47 16
44 53 16
47 58 9
41 65 12
55 56 15
47 63 11
50 66 15
22 60 17
33 56 6
47 19 16

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 Maurice George Kendall' @ kendall.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 Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269046&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 Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269046&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269046&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 Maurice George Kendall' @ kendall.wessa.net






Correlations for all pairs of data series (method=pearson)
AMS.IAMS.ECONFSTATTOT
AMS.I10.3440.228
AMS.E0.34410.033
CONFSTATTOT0.2280.0331

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & AMS.I & AMS.E & CONFSTATTOT \tabularnewline
AMS.I & 1 & 0.344 & 0.228 \tabularnewline
AMS.E & 0.344 & 1 & 0.033 \tabularnewline
CONFSTATTOT & 0.228 & 0.033 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269046&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]AMS.I[/C][C]AMS.E[/C][C]CONFSTATTOT[/C][/ROW]
[ROW][C]AMS.I[/C][C]1[/C][C]0.344[/C][C]0.228[/C][/ROW]
[ROW][C]AMS.E[/C][C]0.344[/C][C]1[/C][C]0.033[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]0.228[/C][C]0.033[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269046&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269046&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)
AMS.IAMS.ECONFSTATTOT
AMS.I10.3440.228
AMS.E0.34410.033
CONFSTATTOT0.2280.0331






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;CONFSTATTOT0.22850.22690.1674
p-value(1e-04)(1e-04)(1e-04)
AMS.E;CONFSTATTOT0.03280.05660.0419
p-value(0.5854)(0.3462)(0.3345)

\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;CONFSTATTOT & 0.2285 & 0.2269 & 0.1674 \tabularnewline
p-value & (1e-04) & (1e-04) & (1e-04) \tabularnewline
AMS.E;CONFSTATTOT & 0.0328 & 0.0566 & 0.0419 \tabularnewline
p-value & (0.5854) & (0.3462) & (0.3345) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269046&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;CONFSTATTOT[/C][C]0.2285[/C][C]0.2269[/C][C]0.1674[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](1e-04)[/C][C](1e-04)[/C][/ROW]
[ROW][C]AMS.E;CONFSTATTOT[/C][C]0.0328[/C][C]0.0566[/C][C]0.0419[/C][/ROW]
[ROW][C]p-value[/C][C](0.5854)[/C][C](0.3462)[/C][C](0.3345)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269046&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269046&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;CONFSTATTOT0.22850.22690.1674
p-value(1e-04)(1e-04)(1e-04)
AMS.E;CONFSTATTOT0.03280.05660.0419
p-value(0.5854)(0.3462)(0.3345)






Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.010.670.670.67
0.020.670.670.67
0.030.670.670.67
0.040.670.670.67
0.050.670.670.67
0.060.670.670.67
0.070.670.670.67
0.080.670.670.67
0.090.670.670.67
0.10.670.670.67

\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 & 0.67 & 0.67 & 0.67 \tabularnewline
0.02 & 0.67 & 0.67 & 0.67 \tabularnewline
0.03 & 0.67 & 0.67 & 0.67 \tabularnewline
0.04 & 0.67 & 0.67 & 0.67 \tabularnewline
0.05 & 0.67 & 0.67 & 0.67 \tabularnewline
0.06 & 0.67 & 0.67 & 0.67 \tabularnewline
0.07 & 0.67 & 0.67 & 0.67 \tabularnewline
0.08 & 0.67 & 0.67 & 0.67 \tabularnewline
0.09 & 0.67 & 0.67 & 0.67 \tabularnewline
0.1 & 0.67 & 0.67 & 0.67 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269046&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]0.67[/C][C]0.67[/C][C]0.67[/C][/ROW]
[ROW][C]0.02[/C][C]0.67[/C][C]0.67[/C][C]0.67[/C][/ROW]
[ROW][C]0.03[/C][C]0.67[/C][C]0.67[/C][C]0.67[/C][/ROW]
[ROW][C]0.04[/C][C]0.67[/C][C]0.67[/C][C]0.67[/C][/ROW]
[ROW][C]0.05[/C][C]0.67[/C][C]0.67[/C][C]0.67[/C][/ROW]
[ROW][C]0.06[/C][C]0.67[/C][C]0.67[/C][C]0.67[/C][/ROW]
[ROW][C]0.07[/C][C]0.67[/C][C]0.67[/C][C]0.67[/C][/ROW]
[ROW][C]0.08[/C][C]0.67[/C][C]0.67[/C][C]0.67[/C][/ROW]
[ROW][C]0.09[/C][C]0.67[/C][C]0.67[/C][C]0.67[/C][/ROW]
[ROW][C]0.1[/C][C]0.67[/C][C]0.67[/C][C]0.67[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269046&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269046&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
0.010.670.670.67
0.020.670.670.67
0.030.670.670.67
0.040.670.670.67
0.050.670.670.67
0.060.670.670.67
0.070.670.670.67
0.080.670.670.67
0.090.670.670.67
0.10.670.670.67


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