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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 computationThu, 11 Dec 2014 10:06:42 +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/11/t1418292440i8br7izdxbmpzqt.htm/, Retrieved Thu, 16 May 2024 10:57:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=265667, Retrieved Thu, 16 May 2024 10:57:27 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact118
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2012-10-10 08:21:32] [b98453cac15ba1066b407e146608df68]
- RM    [Kendall tau Correlation Matrix] [WS2 task 2b] [2014-10-08 17:00:53] [5efa6717cfe6505454df834acc87b53b]
-  M D      [Kendall tau Correlation Matrix] [] [2014-12-11 10:06:42] [4621f922aed0297f88122271e88ec2ef] [Current]
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Dataseries X:
0.1	0.11	0.06	0.42	0.44	0.44
0.68	0.66	0.61	1	0.82	0.8
-0.95	-0.79	-0.78	-0.78	-0.74	-0.7
-0.1	-0.11	-0.06	-0.07	-0.1	-0.19
-0.73	-0.68	-0.7	-0.87	-0.8	-0.79
0.56	0.64	0.61	0.8	0.82	0.78
0.68	0.75	0.72	0.8	0.82	0.83
1.02	0.94	0.93	1.27	0.97	0.95
-0.38	-0.35	-0.32	-0.16	-0.16	-0.11
0.05	0.06	0.11	0.11	0.13	0.2
0.13	0.14	0.12	0.4	0.36	0.38
0.33	0.43	0.33	0.27	0.35	0.33
-0.89	-0.78	-0.71	-0.53	-0.57	-0.5
-0.79	-0.81	-0.77	-0.62	-0.7	-0.68
-1.08	-0.89	-0.86	-0.98	-0.79	-0.74
0.59	0.73	0.7	0.56	0.81	0.79
0.17	0.2	0.17	0.16	0.2	0.2
-0.44	-0.47	-0.4	-0.07	-0.08	-0.06
-0.29	-0.36	-0.35	0.07	0.08	0.09
-0.73	-0.77	-0.76	-0.2	-0.2	-0.12
-0.49	-0.53	-0.45	-0.58	-0.57	-0.49
-0.98	-0.82	-0.78	-0.96	-0.78	-0.73
-1.03	-0.92	-0.88	-0.87	-0.87	-0.83
0.4	0.49	0.53	0.04	0.05	0.09
-0.76	-0.77	-0.74	-0.78	-0.74	-0.74
-0.95	-0.94	-0.92	-0.71	-0.59	-0.51
0.87	0.85	0.86	1.02	0.92	0.95
-0.43	-0.43	-0.48	-0.22	-0.25	-0.26
0.59	0.73	0.72	0.62	0.74	0.75
0.21	0.29	0.29	0.16	0.2	0.25
0.63	0.69	0.63	0.87	0.83	0.84
-0.24	-0.26	-0.25	-0.36	-0.53	-0.5
-0.17	-0.21	-0.25	-0.07	-0.08	-0.12
-0.62	-0.76	-0.73	-0.8	-0.75	-0.78
-1.13	-0.92	-0.89	-1.13	-0.78	-0.76




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265667&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265667&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265667&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 time1 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Correlations for all pairs of data series (method=pearson)
meanMsumMcountMmeanFsumFcountF
meanM10.9920.9910.960.9560.95
sumM0.99210.9980.9450.9550.951
countM0.9910.99810.9420.950.947
meanF0.960.9450.94210.9860.984
sumF0.9560.9550.950.98610.998
countF0.950.9510.9470.9840.9981

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & meanM & sumM & countM & meanF & sumF & countF \tabularnewline
meanM & 1 & 0.992 & 0.991 & 0.96 & 0.956 & 0.95 \tabularnewline
sumM & 0.992 & 1 & 0.998 & 0.945 & 0.955 & 0.951 \tabularnewline
countM & 0.991 & 0.998 & 1 & 0.942 & 0.95 & 0.947 \tabularnewline
meanF & 0.96 & 0.945 & 0.942 & 1 & 0.986 & 0.984 \tabularnewline
sumF & 0.956 & 0.955 & 0.95 & 0.986 & 1 & 0.998 \tabularnewline
countF & 0.95 & 0.951 & 0.947 & 0.984 & 0.998 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265667&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]meanM[/C][C]sumM[/C][C]countM[/C][C]meanF[/C][C]sumF[/C][C]countF[/C][/ROW]
[ROW][C]meanM[/C][C]1[/C][C]0.992[/C][C]0.991[/C][C]0.96[/C][C]0.956[/C][C]0.95[/C][/ROW]
[ROW][C]sumM[/C][C]0.992[/C][C]1[/C][C]0.998[/C][C]0.945[/C][C]0.955[/C][C]0.951[/C][/ROW]
[ROW][C]countM[/C][C]0.991[/C][C]0.998[/C][C]1[/C][C]0.942[/C][C]0.95[/C][C]0.947[/C][/ROW]
[ROW][C]meanF[/C][C]0.96[/C][C]0.945[/C][C]0.942[/C][C]1[/C][C]0.986[/C][C]0.984[/C][/ROW]
[ROW][C]sumF[/C][C]0.956[/C][C]0.955[/C][C]0.95[/C][C]0.986[/C][C]1[/C][C]0.998[/C][/ROW]
[ROW][C]countF[/C][C]0.95[/C][C]0.951[/C][C]0.947[/C][C]0.984[/C][C]0.998[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265667&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265667&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)
meanMsumMcountMmeanFsumFcountF
meanM10.9920.9910.960.9560.95
sumM0.99210.9980.9450.9550.951
countM0.9910.99810.9420.950.947
meanF0.960.9450.94210.9860.984
sumF0.9560.9550.950.98610.998
countF0.950.9510.9470.9840.9981







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
meanM;sumM0.99190.99120.9484
p-value(0)(0)(0)
meanM;countM0.99080.98650.9255
p-value(0)(0)(0)
meanM;meanF0.95970.95630.838
p-value(0)(0)(0)
meanM;sumF0.95590.94740.8115
p-value(0)(0)(0)
meanM;countF0.95010.9310.7881
p-value(0)(0)(0)
sumM;countM0.99840.99620.9704
p-value(0)(0)(0)
sumM;meanF0.94490.93810.7881
p-value(0)(0)(0)
sumM;sumF0.95490.9320.7803
p-value(0)(0)(0)
sumM;countF0.95050.91920.7587
p-value(0)(0)(0)
countM;meanF0.94180.93760.7871
p-value(0)(0)(0)
countM;sumF0.95030.930.7742
p-value(0)(0)(0)
countM;countF0.9470.91810.7508
p-value(0)(0)(0)
meanF;sumF0.98590.99360.96
p-value(0)(0)(0)
meanF;countF0.98380.98420.921
p-value(0)(0)(0)
sumF;countF0.99820.99250.9541
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
meanM;sumM & 0.9919 & 0.9912 & 0.9484 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
meanM;countM & 0.9908 & 0.9865 & 0.9255 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
meanM;meanF & 0.9597 & 0.9563 & 0.838 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
meanM;sumF & 0.9559 & 0.9474 & 0.8115 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
meanM;countF & 0.9501 & 0.931 & 0.7881 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
sumM;countM & 0.9984 & 0.9962 & 0.9704 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
sumM;meanF & 0.9449 & 0.9381 & 0.7881 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
sumM;sumF & 0.9549 & 0.932 & 0.7803 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
sumM;countF & 0.9505 & 0.9192 & 0.7587 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
countM;meanF & 0.9418 & 0.9376 & 0.7871 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
countM;sumF & 0.9503 & 0.93 & 0.7742 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
countM;countF & 0.947 & 0.9181 & 0.7508 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
meanF;sumF & 0.9859 & 0.9936 & 0.96 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
meanF;countF & 0.9838 & 0.9842 & 0.921 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
sumF;countF & 0.9982 & 0.9925 & 0.9541 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265667&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]meanM;sumM[/C][C]0.9919[/C][C]0.9912[/C][C]0.9484[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]meanM;countM[/C][C]0.9908[/C][C]0.9865[/C][C]0.9255[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]meanM;meanF[/C][C]0.9597[/C][C]0.9563[/C][C]0.838[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]meanM;sumF[/C][C]0.9559[/C][C]0.9474[/C][C]0.8115[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]meanM;countF[/C][C]0.9501[/C][C]0.931[/C][C]0.7881[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]sumM;countM[/C][C]0.9984[/C][C]0.9962[/C][C]0.9704[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]sumM;meanF[/C][C]0.9449[/C][C]0.9381[/C][C]0.7881[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]sumM;sumF[/C][C]0.9549[/C][C]0.932[/C][C]0.7803[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]sumM;countF[/C][C]0.9505[/C][C]0.9192[/C][C]0.7587[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]countM;meanF[/C][C]0.9418[/C][C]0.9376[/C][C]0.7871[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]countM;sumF[/C][C]0.9503[/C][C]0.93[/C][C]0.7742[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]countM;countF[/C][C]0.947[/C][C]0.9181[/C][C]0.7508[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]meanF;sumF[/C][C]0.9859[/C][C]0.9936[/C][C]0.96[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]meanF;countF[/C][C]0.9838[/C][C]0.9842[/C][C]0.921[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]sumF;countF[/C][C]0.9982[/C][C]0.9925[/C][C]0.9541[/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=265667&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265667&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
meanM;sumM0.99190.99120.9484
p-value(0)(0)(0)
meanM;countM0.99080.98650.9255
p-value(0)(0)(0)
meanM;meanF0.95970.95630.838
p-value(0)(0)(0)
meanM;sumF0.95590.94740.8115
p-value(0)(0)(0)
meanM;countF0.95010.9310.7881
p-value(0)(0)(0)
sumM;countM0.99840.99620.9704
p-value(0)(0)(0)
sumM;meanF0.94490.93810.7881
p-value(0)(0)(0)
sumM;sumF0.95490.9320.7803
p-value(0)(0)(0)
sumM;countF0.95050.91920.7587
p-value(0)(0)(0)
countM;meanF0.94180.93760.7871
p-value(0)(0)(0)
countM;sumF0.95030.930.7742
p-value(0)(0)(0)
countM;countF0.9470.91810.7508
p-value(0)(0)(0)
meanF;sumF0.98590.99360.96
p-value(0)(0)(0)
meanF;countF0.98380.98420.921
p-value(0)(0)(0)
sumF;countF0.99820.99250.9541
p-value(0)(0)(0)



Parameters (Session):
par1 = pearson ;
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')
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')