Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software ModulePatrick.Wessarwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationTue, 14 Dec 2010 15:46:57 +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/2010/Dec/14/t12923415191gobcc967gbb1u5.htm/, Retrieved Thu, 02 May 2024 19:52:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109761, Retrieved Thu, 02 May 2024 19:52:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128
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]
- R PD    [Kendall tau Correlation Matrix] [] [2010-12-14 15:46:57] [1d094c42a82a95b45a19e32ad4bfff5f] [Current]
Feedback Forum

Post a new message
Dataseries X:
0	13	13	14	13	3
0	12	12	8	13	5
1	15	10	12	16	6
1	12	9	7	12	6
1	10	10	10	11	5
1	12	12	7	12	3
0	15	13	16	18	8
1	9	12	11	11	4
0	12	12	14	14	4
0	11	6	6	9	4
1	11	5	16	14	6
0	11	12	11	12	6
0	15	11	16	11	5
1	7	14	12	12	4
0	11	14	7	13	6
1	11	12	13	11	4
1	10	12	11	12	6
0	14	11	15	16	6
0	10	11	7	9	4
0	6	7	9	11	4
0	11	9	7	13	2
0	15	11	14	15	7
0	11	11	15	10	5
0	12	12	7	11	4
1	14	12	15	13	6
1	15	11	17	16	6
0	9	11	15	15	7
1	13	8	14	14	5
1	13	9	14	14	6
1	16	12	8	14	4
1	13	10	8	8	4
0	12	10	14	13	7
1	14	12	14	15	7
1	11	8	8	13	4
0	9	12	11	11	4
0	16	11	16	15	6
1	12	12	10	15	6
0	10	7	8	9	5
1	13	11	14	13	6
1	16	11	16	16	7
1	14	12	13	13	6
1	15	9	5	11	3
1	5	15	8	12	3
0	8	11	10	12	4
0	11	11	8	12	6
1	16	11	13	14	7
1	17	11	15	14	5
1	9	15	6	8	4
1	9	11	12	13	5
1	13	12	16	16	6
1	10	12	5	13	6
0	6	9	15	11	6
1	12	12	12	14	5
1	8	12	8	13	4
1	14	13	13	13	5
1	12	11	14	13	5
0	11	9	12	12	4
0	16	9	16	16	6
1	8	11	10	15	2
0	15	11	15	15	8
1	7	12	8	12	3
0	16	12	16	14	6
1	14	9	19	12	6
1	16	11	14	15	6
1	9	9	6	12	5
0	14	12	13	13	5
1	11	12	15	12	6
1	13	12	7	12	5
1	15	12	13	13	6
0	5	14	4	5	2
1	15	11	14	13	5
0	13	12	13	13	5
0	11	11	11	14	5
1	11	6	14	17	6
0	12	10	12	13	6
0	12	12	15	13	6
0	12	13	14	12	5
1	12	8	13	13	5
1	14	12	8	14	4
1	6	12	6	11	2
0	7	12	7	12	4
0	14	6	13	12	6
0	14	11	13	16	6
1	10	10	11	12	5
0	13	12	5	12	3
0	12	13	12	12	6
0	9	11	8	10	4
1	12	7	11	15	5
1	16	11	14	15	8
0	10	11	9	12	4
1	14	11	10	16	6
0	10	11	13	15	6
1	16	12	16	16	7
0	15	10	16	13	6
1	12	11	11	12	5
0	10	12	8	11	4
0	8	7	4	13	6
1	8	13	7	10	3
1	11	8	14	15	5
0	13	12	11	13	6
1	16	11	17	16	7
0	16	12	15	15	7
1	14	14	17	18	6
0	11	10	5	13	3
1	4	10	4	10	2
1	14	13	10	16	8
1	9	10	11	13	3
0	14	11	15	15	8
0	8	10	10	14	3
1	8	7	9	15	4
1	11	10	12	14	5
1	12	8	15	13	7
1	11	12	7	13	6
1	14	12	13	15	6
1	15	12	12	16	7
1	16	11	14	14	6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109761&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' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109761&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109761&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Correlations for all pairs of data series (method=pearson)
GenderPopularityFindingFriendsKnowingPeopleLikedCelebrity
Gender10.0730.0050.0190.1680.002
Popularity0.07310.0390.5690.5440.601
FindingFriends0.0050.0391-0.029-0.032-0.031
KnowingPeople0.0190.569-0.02910.5570.619
Liked0.1680.544-0.0320.55710.574
Celebrity0.0020.601-0.0310.6190.5741

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & Gender & Popularity & FindingFriends & KnowingPeople & Liked & Celebrity \tabularnewline
Gender & 1 & 0.073 & 0.005 & 0.019 & 0.168 & 0.002 \tabularnewline
Popularity & 0.073 & 1 & 0.039 & 0.569 & 0.544 & 0.601 \tabularnewline
FindingFriends & 0.005 & 0.039 & 1 & -0.029 & -0.032 & -0.031 \tabularnewline
KnowingPeople & 0.019 & 0.569 & -0.029 & 1 & 0.557 & 0.619 \tabularnewline
Liked & 0.168 & 0.544 & -0.032 & 0.557 & 1 & 0.574 \tabularnewline
Celebrity & 0.002 & 0.601 & -0.031 & 0.619 & 0.574 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109761&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]Gender[/C][C]Popularity[/C][C]FindingFriends[/C][C]KnowingPeople[/C][C]Liked[/C][C]Celebrity[/C][/ROW]
[ROW][C]Gender[/C][C]1[/C][C]0.073[/C][C]0.005[/C][C]0.019[/C][C]0.168[/C][C]0.002[/C][/ROW]
[ROW][C]Popularity[/C][C]0.073[/C][C]1[/C][C]0.039[/C][C]0.569[/C][C]0.544[/C][C]0.601[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.005[/C][C]0.039[/C][C]1[/C][C]-0.029[/C][C]-0.032[/C][C]-0.031[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.019[/C][C]0.569[/C][C]-0.029[/C][C]1[/C][C]0.557[/C][C]0.619[/C][/ROW]
[ROW][C]Liked[/C][C]0.168[/C][C]0.544[/C][C]-0.032[/C][C]0.557[/C][C]1[/C][C]0.574[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.002[/C][C]0.601[/C][C]-0.031[/C][C]0.619[/C][C]0.574[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109761&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109761&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=pearson)
GenderPopularityFindingFriendsKnowingPeopleLikedCelebrity
Gender10.0730.0050.0190.1680.002
Popularity0.07310.0390.5690.5440.601
FindingFriends0.0050.0391-0.029-0.032-0.031
KnowingPeople0.0190.569-0.02910.5570.619
Liked0.1680.544-0.0320.55710.574
Celebrity0.0020.601-0.0310.6190.5741







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Gender;Popularity0.0730.08680.0744
p-value(0.436)(0.3541)(0.3518)
Gender;FindingFriends0.00490.00830.0074
p-value(0.9582)(0.9297)(0.9292)
Gender;KnowingPeople0.0195-0.005-0.0042
p-value(0.8356)(0.9579)(0.9576)
Gender;Liked0.16780.16710.146
p-value(0.0718)(0.073)(0.0732)
Gender;Celebrity0.00230.01070.0096
p-value(0.9802)(0.9093)(0.9087)
Popularity;FindingFriends0.03940.05250.0358
p-value(0.6747)(0.5758)(0.6143)
Popularity;KnowingPeople0.56890.57870.4429
p-value(0)(0)(0)
Popularity;Liked0.54350.55940.4344
p-value(0)(0)(0)
Popularity;Celebrity0.60140.57570.4715
p-value(0)(0)(0)
FindingFriends;KnowingPeople-0.0287-0.0567-0.0449
p-value(0.7599)(0.5457)(0.5246)
FindingFriends;Liked-0.0318-0.0376-0.0308
p-value(0.7347)(0.6884)(0.6703)
FindingFriends;Celebrity-0.0308-0.0267-0.0213
p-value(0.7431)(0.7764)(0.7733)
KnowingPeople;Liked0.55680.53540.4221
p-value(0)(0)(0)
KnowingPeople;Celebrity0.61930.62060.5008
p-value(0)(0)(0)
Liked;Celebrity0.57390.58560.4709
p-value(0)(0)(0)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
Gender;Popularity & 0.073 & 0.0868 & 0.0744 \tabularnewline
p-value & (0.436) & (0.3541) & (0.3518) \tabularnewline
Gender;FindingFriends & 0.0049 & 0.0083 & 0.0074 \tabularnewline
p-value & (0.9582) & (0.9297) & (0.9292) \tabularnewline
Gender;KnowingPeople & 0.0195 & -0.005 & -0.0042 \tabularnewline
p-value & (0.8356) & (0.9579) & (0.9576) \tabularnewline
Gender;Liked & 0.1678 & 0.1671 & 0.146 \tabularnewline
p-value & (0.0718) & (0.073) & (0.0732) \tabularnewline
Gender;Celebrity & 0.0023 & 0.0107 & 0.0096 \tabularnewline
p-value & (0.9802) & (0.9093) & (0.9087) \tabularnewline
Popularity;FindingFriends & 0.0394 & 0.0525 & 0.0358 \tabularnewline
p-value & (0.6747) & (0.5758) & (0.6143) \tabularnewline
Popularity;KnowingPeople & 0.5689 & 0.5787 & 0.4429 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Popularity;Liked & 0.5435 & 0.5594 & 0.4344 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Popularity;Celebrity & 0.6014 & 0.5757 & 0.4715 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
FindingFriends;KnowingPeople & -0.0287 & -0.0567 & -0.0449 \tabularnewline
p-value & (0.7599) & (0.5457) & (0.5246) \tabularnewline
FindingFriends;Liked & -0.0318 & -0.0376 & -0.0308 \tabularnewline
p-value & (0.7347) & (0.6884) & (0.6703) \tabularnewline
FindingFriends;Celebrity & -0.0308 & -0.0267 & -0.0213 \tabularnewline
p-value & (0.7431) & (0.7764) & (0.7733) \tabularnewline
KnowingPeople;Liked & 0.5568 & 0.5354 & 0.4221 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
KnowingPeople;Celebrity & 0.6193 & 0.6206 & 0.5008 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Liked;Celebrity & 0.5739 & 0.5856 & 0.4709 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109761&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;Popularity[/C][C]0.073[/C][C]0.0868[/C][C]0.0744[/C][/ROW]
[ROW][C]p-value[/C][C](0.436)[/C][C](0.3541)[/C][C](0.3518)[/C][/ROW]
[ROW][C]Gender;FindingFriends[/C][C]0.0049[/C][C]0.0083[/C][C]0.0074[/C][/ROW]
[ROW][C]p-value[/C][C](0.9582)[/C][C](0.9297)[/C][C](0.9292)[/C][/ROW]
[ROW][C]Gender;KnowingPeople[/C][C]0.0195[/C][C]-0.005[/C][C]-0.0042[/C][/ROW]
[ROW][C]p-value[/C][C](0.8356)[/C][C](0.9579)[/C][C](0.9576)[/C][/ROW]
[ROW][C]Gender;Liked[/C][C]0.1678[/C][C]0.1671[/C][C]0.146[/C][/ROW]
[ROW][C]p-value[/C][C](0.0718)[/C][C](0.073)[/C][C](0.0732)[/C][/ROW]
[ROW][C]Gender;Celebrity[/C][C]0.0023[/C][C]0.0107[/C][C]0.0096[/C][/ROW]
[ROW][C]p-value[/C][C](0.9802)[/C][C](0.9093)[/C][C](0.9087)[/C][/ROW]
[ROW][C]Popularity;FindingFriends[/C][C]0.0394[/C][C]0.0525[/C][C]0.0358[/C][/ROW]
[ROW][C]p-value[/C][C](0.6747)[/C][C](0.5758)[/C][C](0.6143)[/C][/ROW]
[ROW][C]Popularity;KnowingPeople[/C][C]0.5689[/C][C]0.5787[/C][C]0.4429[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Popularity;Liked[/C][C]0.5435[/C][C]0.5594[/C][C]0.4344[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Popularity;Celebrity[/C][C]0.6014[/C][C]0.5757[/C][C]0.4715[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]FindingFriends;KnowingPeople[/C][C]-0.0287[/C][C]-0.0567[/C][C]-0.0449[/C][/ROW]
[ROW][C]p-value[/C][C](0.7599)[/C][C](0.5457)[/C][C](0.5246)[/C][/ROW]
[ROW][C]FindingFriends;Liked[/C][C]-0.0318[/C][C]-0.0376[/C][C]-0.0308[/C][/ROW]
[ROW][C]p-value[/C][C](0.7347)[/C][C](0.6884)[/C][C](0.6703)[/C][/ROW]
[ROW][C]FindingFriends;Celebrity[/C][C]-0.0308[/C][C]-0.0267[/C][C]-0.0213[/C][/ROW]
[ROW][C]p-value[/C][C](0.7431)[/C][C](0.7764)[/C][C](0.7733)[/C][/ROW]
[ROW][C]KnowingPeople;Liked[/C][C]0.5568[/C][C]0.5354[/C][C]0.4221[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]KnowingPeople;Celebrity[/C][C]0.6193[/C][C]0.6206[/C][C]0.5008[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Liked;Celebrity[/C][C]0.5739[/C][C]0.5856[/C][C]0.4709[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109761&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109761&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
Gender;Popularity0.0730.08680.0744
p-value(0.436)(0.3541)(0.3518)
Gender;FindingFriends0.00490.00830.0074
p-value(0.9582)(0.9297)(0.9292)
Gender;KnowingPeople0.0195-0.005-0.0042
p-value(0.8356)(0.9579)(0.9576)
Gender;Liked0.16780.16710.146
p-value(0.0718)(0.073)(0.0732)
Gender;Celebrity0.00230.01070.0096
p-value(0.9802)(0.9093)(0.9087)
Popularity;FindingFriends0.03940.05250.0358
p-value(0.6747)(0.5758)(0.6143)
Popularity;KnowingPeople0.56890.57870.4429
p-value(0)(0)(0)
Popularity;Liked0.54350.55940.4344
p-value(0)(0)(0)
Popularity;Celebrity0.60140.57570.4715
p-value(0)(0)(0)
FindingFriends;KnowingPeople-0.0287-0.0567-0.0449
p-value(0.7599)(0.5457)(0.5246)
FindingFriends;Liked-0.0318-0.0376-0.0308
p-value(0.7347)(0.6884)(0.6703)
FindingFriends;Celebrity-0.0308-0.0267-0.0213
p-value(0.7431)(0.7764)(0.7733)
KnowingPeople;Liked0.55680.53540.4221
p-value(0)(0)(0)
KnowingPeople;Celebrity0.61930.62060.5008
p-value(0)(0)(0)
Liked;Celebrity0.57390.58560.4709
p-value(0)(0)(0)



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')