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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 computationWed, 21 Dec 2016 17:26:17 +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/21/t148233793075c4d69ikvu4ox8.htm/, Retrieved Thu, 20 Feb 2025 19:37:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=302419, Retrieved Thu, 20 Feb 2025 19:37:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144

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] [Correlation matrices] [2016-12-21 16:26:17] [0c0812954c36a879765e84a6a68f060c] [Current]
- RMPD    [Decomposition by Loess] [Decomposition by ...] [2016-12-22 18:27:38] [feec176c93fae6ba480eaf3a53b4a897]
- RMPD    [Classical Decomposition] [Classical decompo...] [2016-12-22 18:29:06] [feec176c93fae6ba480eaf3a53b4a897]
- RMPD    [Univariate Data Series] [Plot & describe F...] [2016-12-22 20:46:49] [feec176c93fae6ba480eaf3a53b4a897]
- RMPD    [Univariate Data Series] [plot & describe F...] [2016-12-22 21:09:52] [feec176c93fae6ba480eaf3a53b4a897]
- RMPD    [Variance Reduction Matrix] [Variance reductio...] [2016-12-22 21:19:41] [feec176c93fae6ba480eaf3a53b4a897]
- RMPD    [ARIMA Forecasting] [ARIMA Forecasting F1] [2016-12-22 22:13:05] [feec176c93fae6ba480eaf3a53b4a897]
- RMPD    [ARIMA Forecasting] [ARIMA Forecasting...] [2016-12-23 07:36:33] [feec176c93fae6ba480eaf3a53b4a897]
- RMPD    [Exponential Smoothing] [Exponential smoot...] [2016-12-23 07:43:01] [feec176c93fae6ba480eaf3a53b4a897]
- RMPD    [ARIMA Backward Selection] [ARIMA Backward se...] [2016-12-23 08:07:47] [feec176c93fae6ba480eaf3a53b4a897]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
11	17
15	12
15	13
13	16
14	15
13	14
15	15
15	12
10	13
11	14
16	18
17	19
14	15
13	14
10	13
17	17
11	12
15	12
12	12
15	14
12	15
19	13
13	14
15	16
13	16
10	15
12	16
15	16
13	17
18	16
15	11
14	15
11	11
14	13
9	13
13	17
13	13
12	12
16	16
15	18
16	12
16	15
13	15
13	15
12	14
11	17
13	15
14	16
13	12
15	10
14	15
13	14
11	14
14	13
17	17
15	16
15	16
13	16
12	17
11	16
14	13
18	17
15	12
18	18
16	15
12	12
14	13
14	13
14	13
13	17
12	15
13	16
13	18
14	16
15	14
13	12
14	14
17	9
15	14
13	17
14	15
17	15
8	20
15	12
10	14
15	16
15	18
14	10
15	13
18	16
19	16
16	17
18	18
13	15
10	14
14	15
12	16
13	12
12	19
13	17
16	14
12	13
14	14
17	14
14	17
14	15
17	16
13	17
14	15
11	10
17	18
15	16
15	14
17	13
12	13
15	14
10	17
13	13
17	14
17	18
16	12
15	14
16	8
16	16
15	13
16	16
14	11
17	15
12	14
15	13
14	17
14	18
13	16
13	16
14	15
13	14
13	15
15	16
13	12
14	19
13	15
12	13

Tables (Output of Computation)





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=302419&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=302419&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302419&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 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=pearson)
V1V2
V110.082
V20.0821

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & V1 & V2 \tabularnewline
V1 & 1 & 0.082 \tabularnewline
V2 & 0.082 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302419&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]V1[/C][C]V2[/C][/ROW]
[ROW][C]V1[/C][C]1[/C][C]0.082[/C][/ROW]
[ROW][C]V2[/C][C]0.082[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302419&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302419&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)
V1V2
V110.082
V20.0821






Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
V1;V20.08230.10930.0817
p-value(0.3301)(0.1954)(0.1989)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
V1;V2 & 0.0823 & 0.1093 & 0.0817 \tabularnewline
p-value & (0.3301) & (0.1954) & (0.1989) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302419&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]V1;V2[/C][C]0.0823[/C][C]0.1093[/C][C]0.0817[/C][/ROW]
[ROW][C]p-value[/C][C](0.3301)[/C][C](0.1954)[/C][C](0.1989)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302419&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302419&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
V1;V20.08230.10930.0817
p-value(0.3301)(0.1954)(0.1989)






Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.01000
0.02000
0.03000
0.04000
0.05000
0.06000
0.07000
0.08000
0.09000
0.1000

\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 & 0 & 0 \tabularnewline
0.04 & 0 & 0 & 0 \tabularnewline
0.05 & 0 & 0 & 0 \tabularnewline
0.06 & 0 & 0 & 0 \tabularnewline
0.07 & 0 & 0 & 0 \tabularnewline
0.08 & 0 & 0 & 0 \tabularnewline
0.09 & 0 & 0 & 0 \tabularnewline
0.1 & 0 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302419&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]0[/C][C]0[/C][/ROW]
[ROW][C]0.04[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]0.05[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]0.06[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]0.07[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]0.08[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]0.09[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]0.1[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302419&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302419&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
0.03000
0.04000
0.05000
0.06000
0.07000
0.08000
0.09000
0.1000


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ;
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$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
x <- na.omit(x)
y <- t(na.omit(t(y)))
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
n <- length(y[,1])
print(n)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ',header=TRUE)
for (i in 1:n) {
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
for (j in 1:n) {
r <- cor.test(y[i,],y[j,],method=par1)
a<-table.element(a,round(r$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')