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Author*The author of this computation has been verified*
R Software Modulerwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationFri, 16 Dec 2016 19:07:00 +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/16/t14819122899zs7mjistpikfrz.htm/, Retrieved Fri, 01 Nov 2024 03:37:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=300469, Retrieved Fri, 01 Nov 2024 03:37:22 +0000
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Dataseries X:
5	3	4	5	13
2	2	5	2	16
3	3	4	2	17
3	3	4	2	NA
3	2	4	4	NA
4	4	5	4	16
4	2	5	4	17
2	2	5	2	17
4	4	4	4	15
3	5	4	3	16
3	5	5	3	14
4	2	5	4	16
2	2	4	3	17
1	1	4	2	NA
2	2	4	2	NA
3	4	5	2	NA
5	4	5	2	16
4	4	4	3	NA
5	4	4	2	16
3	3	4	2	NA
5	5	5	3	NA
2	2	4	2	NA
4	5	5	3	16
4	2	4	2	15
3	3	5	2	16
2	1	4	2	16
1	1	4	5	13
2	2	3	3	15
5	1	5	4	17
4	4	4	3	NA
3	3	4	3	13
2	3	5	3	17
1	2	4	2	NA
3	2	5	4	14
3	3	5	3	14
3	1	5	2	18
5	3	4	3	NA
2	2	4	4	17
2	2	4	3	13
1	2	5	4	16
4	4	4	3	15
4	1	4	4	15
2	2	4	3	NA
1	5	2	2	15
5	4	4	3	13
4	4	4	1	NA
4	4	5	2	17
4	2	5	3	NA
2	2	5	3	NA
2	2	4	2	11
3	2	4	3	14
2	1	4	2	13
3	5	5	2	NA
4	5	5	2	17
3	3	4	2	16
2	2	5	2	NA
2	2	5	2	17
1	2	4	2	16
3	2	5	3	16
4	5	5	3	16
4	5	5	4	15
4	3	5	3	12
3	3	3	3	17
5	4	5	4	14
4	1	4	2	14
1	1	3	1	16
1	1	5	3	NA
5	5	5	4	NA
5	4	3	4	NA
3	1	4	4	NA
2	2	4	2	NA
4	3	5	2	15
4	2	5	1	16
4	2	5	2	14
4	5	5	2	15
5	5	5	3	17
4	2	5	2	NA
4	4	4	3	10
4	4	4	4	NA
2	1	4	2	17
1	1	5	2	NA
1	2	4	1	20
5	4	5	4	17
5	5	5	3	18
3	2	5	4	NA
2	2	2	2	17
4	3	4	3	14
2	1	5	5	NA
3	4	4	3	17
1	1	4	1	NA
5	5	5	3	17
4	4	5	3	NA
2	1	4	2	16
2	3	5	1	18
1	1	5	3	18
4	2	5	2	16
2	1	5	2	NA
3	1	5	3	NA
1	3	4	3	15
2	2	5	3	13
3	2	4	3	NA
1	2	5	2	NA
4	3	4	1	NA
1	2	5	4	NA
4	4	5	3	16
1	3	5	2	NA
4	2	3	3	NA
2	2	5	3	NA
3	4	3	3	12
3	1	4	2	NA
3	4	4	3	16
3	3	5	2	16
3	5	4	3	NA
2	4	5	2	16
2	3	5	3	14
4	4	5	4	15
2	3	4	3	14
5	5	4	3	NA
1	1	5	2	15
3	2	4	3	NA
3	4	5	2	15
3	4	5	2	16
4	5	3	2	NA
3	2	5	2	NA
2	4	4	3	11
4	5	4	2	NA
5	5	3	3	18
4	2	5	2	NA
4	4	4	2	11
4	4	4	2	NA
3	5	4	5	18
4	2	4	3	NA
3	4	5	3	15
1	2	5	3	17
2	2	5	2	NA
1	1	4	3	14
4	4	4	3	NA
5	3	5	3	13
4	4	5	3	17
3	1	4	2	14
2	4	5	4	19
1	2	5	2	14
3	3	5	1	NA
4	3	5	2	NA
4	5	5	4	16
1	5	5	4	16
5	5	5	4	15
3	4	3	3	12
4	2	5	4	17
1	1	3	2	NA
3	2	4	5	NA
4	2	5	3	15
4	3	2	2	18
5	5	5	3	15
1	1	3	3	NA
1	1	1	2	NA
5	3	5	4	16
3	4	5	2	NA
4	3	5	5	16




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300469&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]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=300469&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300469&T=0

As an alternative you can also use a 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 time1 seconds
R ServerBig Analytics Cloud Computing Center







Correlations for all pairs of data series (method=kendall)
EC1EC2EC3EC4TVDC
EC110.380.2110.222-0.009
EC20.3810.1360.1960.027
EC30.2110.13610.1240.161
EC40.2220.1960.1241-0.063
TVDC-0.0090.0270.161-0.0631

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & EC1 & EC2 & EC3 & EC4 & TVDC \tabularnewline
EC1 & 1 & 0.38 & 0.211 & 0.222 & -0.009 \tabularnewline
EC2 & 0.38 & 1 & 0.136 & 0.196 & 0.027 \tabularnewline
EC3 & 0.211 & 0.136 & 1 & 0.124 & 0.161 \tabularnewline
EC4 & 0.222 & 0.196 & 0.124 & 1 & -0.063 \tabularnewline
TVDC & -0.009 & 0.027 & 0.161 & -0.063 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300469&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]EC1[/C][C]EC2[/C][C]EC3[/C][C]EC4[/C][C]TVDC[/C][/ROW]
[ROW][C]EC1[/C][C]1[/C][C]0.38[/C][C]0.211[/C][C]0.222[/C][C]-0.009[/C][/ROW]
[ROW][C]EC2[/C][C]0.38[/C][C]1[/C][C]0.136[/C][C]0.196[/C][C]0.027[/C][/ROW]
[ROW][C]EC3[/C][C]0.211[/C][C]0.136[/C][C]1[/C][C]0.124[/C][C]0.161[/C][/ROW]
[ROW][C]EC4[/C][C]0.222[/C][C]0.196[/C][C]0.124[/C][C]1[/C][C]-0.063[/C][/ROW]
[ROW][C]TVDC[/C][C]-0.009[/C][C]0.027[/C][C]0.161[/C][C]-0.063[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300469&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300469&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Correlations for all pairs of data series (method=kendall)
EC1EC2EC3EC4TVDC
EC110.380.2110.222-0.009
EC20.3810.1360.1960.027
EC30.2110.13610.1240.161
EC40.2220.1960.1241-0.063
TVDC-0.0090.0270.161-0.0631







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
EC1;EC20.45070.44980.3797
p-value(0)(0)(0)
EC1;EC30.22520.24230.2114
p-value(0.0235)(0.0146)(0.0149)
EC1;EC40.25040.26140.2223
p-value(0.0116)(0.0083)(0.0077)
EC1;TVDC-0.0317-0.0123-0.0085
p-value(0.7531)(0.9027)(0.9144)
EC2;EC30.10450.15690.1362
p-value(0.2981)(0.1171)(0.1158)
EC2;EC40.21340.2330.1957
p-value(0.0322)(0.019)(0.0188)
EC2;TVDC0.0220.03810.0266
p-value(0.8272)(0.7051)(0.7363)
EC3;EC40.15840.13850.1235
p-value(0.1136)(0.1672)(0.1679)
EC3;TVDC0.13410.18430.1614
p-value(0.1813)(0.065)(0.0575)
EC4;TVDC-0.0822-0.0775-0.0628
p-value(0.4137)(0.4412)(0.4417)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
EC1;EC2 & 0.4507 & 0.4498 & 0.3797 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
EC1;EC3 & 0.2252 & 0.2423 & 0.2114 \tabularnewline
p-value & (0.0235) & (0.0146) & (0.0149) \tabularnewline
EC1;EC4 & 0.2504 & 0.2614 & 0.2223 \tabularnewline
p-value & (0.0116) & (0.0083) & (0.0077) \tabularnewline
EC1;TVDC & -0.0317 & -0.0123 & -0.0085 \tabularnewline
p-value & (0.7531) & (0.9027) & (0.9144) \tabularnewline
EC2;EC3 & 0.1045 & 0.1569 & 0.1362 \tabularnewline
p-value & (0.2981) & (0.1171) & (0.1158) \tabularnewline
EC2;EC4 & 0.2134 & 0.233 & 0.1957 \tabularnewline
p-value & (0.0322) & (0.019) & (0.0188) \tabularnewline
EC2;TVDC & 0.022 & 0.0381 & 0.0266 \tabularnewline
p-value & (0.8272) & (0.7051) & (0.7363) \tabularnewline
EC3;EC4 & 0.1584 & 0.1385 & 0.1235 \tabularnewline
p-value & (0.1136) & (0.1672) & (0.1679) \tabularnewline
EC3;TVDC & 0.1341 & 0.1843 & 0.1614 \tabularnewline
p-value & (0.1813) & (0.065) & (0.0575) \tabularnewline
EC4;TVDC & -0.0822 & -0.0775 & -0.0628 \tabularnewline
p-value & (0.4137) & (0.4412) & (0.4417) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300469&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]EC1;EC2[/C][C]0.4507[/C][C]0.4498[/C][C]0.3797[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]EC1;EC3[/C][C]0.2252[/C][C]0.2423[/C][C]0.2114[/C][/ROW]
[ROW][C]p-value[/C][C](0.0235)[/C][C](0.0146)[/C][C](0.0149)[/C][/ROW]
[ROW][C]EC1;EC4[/C][C]0.2504[/C][C]0.2614[/C][C]0.2223[/C][/ROW]
[ROW][C]p-value[/C][C](0.0116)[/C][C](0.0083)[/C][C](0.0077)[/C][/ROW]
[ROW][C]EC1;TVDC[/C][C]-0.0317[/C][C]-0.0123[/C][C]-0.0085[/C][/ROW]
[ROW][C]p-value[/C][C](0.7531)[/C][C](0.9027)[/C][C](0.9144)[/C][/ROW]
[ROW][C]EC2;EC3[/C][C]0.1045[/C][C]0.1569[/C][C]0.1362[/C][/ROW]
[ROW][C]p-value[/C][C](0.2981)[/C][C](0.1171)[/C][C](0.1158)[/C][/ROW]
[ROW][C]EC2;EC4[/C][C]0.2134[/C][C]0.233[/C][C]0.1957[/C][/ROW]
[ROW][C]p-value[/C][C](0.0322)[/C][C](0.019)[/C][C](0.0188)[/C][/ROW]
[ROW][C]EC2;TVDC[/C][C]0.022[/C][C]0.0381[/C][C]0.0266[/C][/ROW]
[ROW][C]p-value[/C][C](0.8272)[/C][C](0.7051)[/C][C](0.7363)[/C][/ROW]
[ROW][C]EC3;EC4[/C][C]0.1584[/C][C]0.1385[/C][C]0.1235[/C][/ROW]
[ROW][C]p-value[/C][C](0.1136)[/C][C](0.1672)[/C][C](0.1679)[/C][/ROW]
[ROW][C]EC3;TVDC[/C][C]0.1341[/C][C]0.1843[/C][C]0.1614[/C][/ROW]
[ROW][C]p-value[/C][C](0.1813)[/C][C](0.065)[/C][C](0.0575)[/C][/ROW]
[ROW][C]EC4;TVDC[/C][C]-0.0822[/C][C]-0.0775[/C][C]-0.0628[/C][/ROW]
[ROW][C]p-value[/C][C](0.4137)[/C][C](0.4412)[/C][C](0.4417)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300469&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300469&T=2

As an alternative you can also use a 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
EC1;EC20.45070.44980.3797
p-value(0)(0)(0)
EC1;EC30.22520.24230.2114
p-value(0.0235)(0.0146)(0.0149)
EC1;EC40.25040.26140.2223
p-value(0.0116)(0.0083)(0.0077)
EC1;TVDC-0.0317-0.0123-0.0085
p-value(0.7531)(0.9027)(0.9144)
EC2;EC30.10450.15690.1362
p-value(0.2981)(0.1171)(0.1158)
EC2;EC40.21340.2330.1957
p-value(0.0322)(0.019)(0.0188)
EC2;TVDC0.0220.03810.0266
p-value(0.8272)(0.7051)(0.7363)
EC3;EC40.15840.13850.1235
p-value(0.1136)(0.1672)(0.1679)
EC3;TVDC0.13410.18430.1614
p-value(0.1813)(0.065)(0.0575)
EC4;TVDC-0.0822-0.0775-0.0628
p-value(0.4137)(0.4412)(0.4417)







Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.010.10.20.2
0.020.20.40.4
0.030.30.40.4
0.040.40.40.4
0.050.40.40.4
0.060.40.40.5
0.070.40.50.5
0.080.40.50.5
0.090.40.50.5
0.10.40.50.5

\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 & 0.1 & 0.2 & 0.2 \tabularnewline
0.02 & 0.2 & 0.4 & 0.4 \tabularnewline
0.03 & 0.3 & 0.4 & 0.4 \tabularnewline
0.04 & 0.4 & 0.4 & 0.4 \tabularnewline
0.05 & 0.4 & 0.4 & 0.4 \tabularnewline
0.06 & 0.4 & 0.4 & 0.5 \tabularnewline
0.07 & 0.4 & 0.5 & 0.5 \tabularnewline
0.08 & 0.4 & 0.5 & 0.5 \tabularnewline
0.09 & 0.4 & 0.5 & 0.5 \tabularnewline
0.1 & 0.4 & 0.5 & 0.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300469&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]0.1[/C][C]0.2[/C][C]0.2[/C][/ROW]
[ROW][C]0.02[/C][C]0.2[/C][C]0.4[/C][C]0.4[/C][/ROW]
[ROW][C]0.03[/C][C]0.3[/C][C]0.4[/C][C]0.4[/C][/ROW]
[ROW][C]0.04[/C][C]0.4[/C][C]0.4[/C][C]0.4[/C][/ROW]
[ROW][C]0.05[/C][C]0.4[/C][C]0.4[/C][C]0.4[/C][/ROW]
[ROW][C]0.06[/C][C]0.4[/C][C]0.4[/C][C]0.5[/C][/ROW]
[ROW][C]0.07[/C][C]0.4[/C][C]0.5[/C][C]0.5[/C][/ROW]
[ROW][C]0.08[/C][C]0.4[/C][C]0.5[/C][C]0.5[/C][/ROW]
[ROW][C]0.09[/C][C]0.4[/C][C]0.5[/C][C]0.5[/C][/ROW]
[ROW][C]0.1[/C][C]0.4[/C][C]0.5[/C][C]0.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300469&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300469&T=3

As an alternative you can also use a 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
0.010.10.20.2
0.020.20.40.4
0.030.30.40.4
0.040.40.40.4
0.050.40.40.4
0.060.40.40.5
0.070.40.50.5
0.080.40.50.5
0.090.40.50.5
0.10.40.50.5



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