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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 computationFri, 09 Dec 2011 09:47:53 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/09/t132344212426ahgpx5yjaj9wp.htm/, Retrieved Thu, 02 May 2024 17:25:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=153396, Retrieved Thu, 02 May 2024 17:25:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact72

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] [] [2010-12-05 18:04:16] [b98453cac15ba1066b407e146608df68]
- RMPD    [Kendall tau Correlation Matrix] [Kendall's] [2011-12-09 14:47:53] [e1aba6efa0fba8dc2a9839c208d0186e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	13	12
2	16	11
2	19	14
1	15	12
2	14	21
2	13	12
2	19	22
2	15	11
2	14	10
2	15	13
1	16	10
2	16	8
1	16	15
2	16	14
2	17	10
1	15	14
1	15	14
2	20	11
1	18	10
2	16	13
1	16	7
2	16	14
2	19	12
2	16	14
1	17	11
2	17	9
1	16	11
2	15	15
2	16	14
1	14	13
2	15	9
1	12	15
2	14	10
2	16	11
1	14	13
1	7	8
1	10	20
1	14	12
2	16	10
1	16	10
1	16	9
2	14	14
1	20	8
1	14	14
2	14	11
2	11	13
2	14	9
2	15	11
2	16	15
1	14	11
2	16	10
1	14	14
1	12	18
2	16	14
1	9	11
2	14	12
2	16	13
2	16	9
1	15	10
2	16	15
1	12	20
1	16	12
2	16	12
2	14	14
2	16	13
1	17	11
2	18	17
1	18	12
2	12	13
1	16	14
1	10	13
2	14	15
2	18	13
1	18	10
1	16	11
2	17	19
2	16	13
2	16	17
1	13	13
1	16	9
1	16	11
1	20	10
2	16	9
1	15	12
2	15	12
2	16	13
1	14	13
2	16	12
2	16	15
2	15	22
2	12	13
2	17	15
2	16	13
2	15	15
2	13	10
2	16	11
2	16	16
2	16	11
1	16	11
1	14	10
2	16	10
1	16	16
2	20	12
1	15	11
2	16	16
1	13	19
2	17	11
1	16	16
1	16	15
2	12	24
2	16	14
2	16	15
2	17	11
1	13	15
2	12	12
1	18	10
2	14	14
2	14	13
2	13	9
2	16	15
2	13	15
2	16	14
2	13	11
2	16	8
2	15	11
2	16	11
1	15	8
2	17	10
2	15	11
2	12	13
1	16	11
1	10	20
2	16	10
1	12	15
1	14	12
2	15	14
1	13	23
1	15	14
2	11	16
2	12	11
1	8	12
2	16	10
1	15	14
2	17	12
1	16	12
2	10	11
2	18	12
1	13	13
1	16	11
1	13	19
2	10	12
2	15	17
1	16	9
2	16	12
2	14	19
2	10	18
2	17	15
2	13	14
2	15	11
2	16	9
2	12	18
2	13	16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153396&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153396&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153396&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 time2 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Correlations for all pairs of data series (method=kendall)
GenderLearningDepression
Gender10.0950.056
Learning0.0951-0.189
Depression0.056-0.1891

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & Gender & Learning & Depression \tabularnewline
Gender & 1 & 0.095 & 0.056 \tabularnewline
Learning & 0.095 & 1 & -0.189 \tabularnewline
Depression & 0.056 & -0.189 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153396&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]Gender[/C][C]Learning[/C][C]Depression[/C][/ROW]
[ROW][C]Gender[/C][C]1[/C][C]0.095[/C][C]0.056[/C][/ROW]
[ROW][C]Learning[/C][C]0.095[/C][C]1[/C][C]-0.189[/C][/ROW]
[ROW][C]Depression[/C][C]0.056[/C][C]-0.189[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153396&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153396&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)
GenderLearningDepression
Gender10.0950.056
Learning0.0951-0.189
Depression0.056-0.1891






Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Gender;Learning0.11890.10770.0948
p-value(0.1319)(0.1724)(0.1717)
Gender;Depression0.04480.06540.0561
p-value(0.5711)(0.4082)(0.4065)
Learning;Depression-0.2327-0.2515-0.1892
p-value(0.0029)(0.0012)(0.0015)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
Gender;Learning & 0.1189 & 0.1077 & 0.0948 \tabularnewline
p-value & (0.1319) & (0.1724) & (0.1717) \tabularnewline
Gender;Depression & 0.0448 & 0.0654 & 0.0561 \tabularnewline
p-value & (0.5711) & (0.4082) & (0.4065) \tabularnewline
Learning;Depression & -0.2327 & -0.2515 & -0.1892 \tabularnewline
p-value & (0.0029) & (0.0012) & (0.0015) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153396&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]Gender;Learning[/C][C]0.1189[/C][C]0.1077[/C][C]0.0948[/C][/ROW]
[ROW][C]p-value[/C][C](0.1319)[/C][C](0.1724)[/C][C](0.1717)[/C][/ROW]
[ROW][C]Gender;Depression[/C][C]0.0448[/C][C]0.0654[/C][C]0.0561[/C][/ROW]
[ROW][C]p-value[/C][C](0.5711)[/C][C](0.4082)[/C][C](0.4065)[/C][/ROW]
[ROW][C]Learning;Depression[/C][C]-0.2327[/C][C]-0.2515[/C][C]-0.1892[/C][/ROW]
[ROW][C]p-value[/C][C](0.0029)[/C][C](0.0012)[/C][C](0.0015)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153396&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153396&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
Gender;Learning0.11890.10770.0948
p-value(0.1319)(0.1724)(0.1717)
Gender;Depression0.04480.06540.0561
p-value(0.5711)(0.4082)(0.4065)
Learning;Depression-0.2327-0.2515-0.1892
p-value(0.0029)(0.0012)(0.0015)


Figures (Output of Computation)

Input Parameters & R Code

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$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
n <- length(y[,1])
n
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ',header=TRUE)
for (i in 1:n) {
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
for (j in 1:n) {
r <- cor.test(y[i,],y[j,],method=par1)
a<-table.element(a,round(r$estimate,3))
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
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)
}
}
a<-table.end(a)
table.save(a,file='mytable1.tab')