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Author

*The author of this computation has been verified*

R Software Module

rwasp_pairs.wasp

Title produced by software

Kendall tau Correlation Matrix

Date of computation

Tue, 07 Dec 2010 14:55:46 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/07/t1291733612sinjyn04drwf2f0.htm/, Retrieved Sat, 04 May 2024 05:06:32 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=106382, Retrieved Sat, 04 May 2024 05:06:32 +0000

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Estimated Impact

107

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Kendall tau Correlation Matrix] [] [2010-12-07 14:55:46] [5842cf9dd57f9603e676e11284d3404a] [Current]

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Dataseries X:

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African elephant   6654.000 5712.000 -999.0 -999.0  3.3 38.6 645.0  3  5  3
African giant pouched rat 1.000 6.600  6.3  2.0  8.3  4.5 42.0  3  1  3
Arctic Fox     3.385 44.500 -999.0 -999.0 12.5 14.0 60.0  1  1  1
Arctic ground squirrel  .920 5.700 -999.0 -999.0 16.5 -999.0 25.0  5  2  3
Asian elephant   2547.000 4603.000  2.1  1.8  3.9 69.0 624.0  3  5  4
Baboon      10.550 179.500  9.1  .7  9.8 27.0 180.0  4  4  4
Big brown bat    .023  .300 15.8  3.9 19.7 19.0 35.0  1  1  1
Brazilian tapir   160.000 169.000  5.2  1.0  6.2 30.4 392.0  4  5  4
Cat       3.300 25.600 10.9  3.6 14.5 28.0 63.0  1  2  1
Chimpanzee     52.160 440.000  8.3  1.4  9.7 50.0 230.0  1  1  1
Chinchilla     .425 6.400 11.0  1.5 12.5  7.0 112.0  5  4  4
Cow      465.000 423.000  3.2  .7  3.9 30.0 281.0  5  5  5
Desert hedgehog    .550 2.400  7.6  2.7 10.3 -999.0 -999.0  2  1  2
Donkey     187.100 419.000 -999.0 -999.0  3.1 40.0 365.0  5  5  5
Eastern American mole  .075 1.200  6.3  2.1  8.4  3.5 42.0  1  1  1
Echidna      3.000 25.000  8.6  .0  8.6 50.0 28.0  2  2  2
European hedgehog   .785 3.500  6.6  4.1 10.7  6.0 42.0  2  2  2
Galago      .200 5.000  9.5  1.2 10.7 10.4 120.0  2  2  2
Genet      1.410 17.500  4.8  1.3  6.1 34.0 -999.0  1  2  1
Giant armadillo   60.000 81.000 12.0  6.1 18.1  7.0 -999.0  1  1  1
Giraffe     529.000 680.000 -999.0  .3 -999.0 28.0 400.0  5  5  5
Goat      27.660 115.000  3.3  .5  3.8 20.0 148.0  5  5  5
Golden hamster    .120 1.000 11.0  3.4 14.4  3.9 16.0  3  1  2
Gorilla     207.000 406.000 -999.0 -999.0 12.0 39.3 252.0  1  4  1
Gray seal     85.000 325.000  4.7  1.5  6.2 41.0 310.0  1  3  1
Gray wolf     36.330 119.500 -999.0 -999.0 13.0 16.2 63.0  1  1  1
Ground squirrel    .101 4.000 10.4  3.4 13.8  9.0 28.0  5  1  3
Guinea pig     1.040 5.500  7.4  .8  8.2  7.6 68.0  5  3  4
Horse      521.000 655.000  2.1  .8  2.9 46.0 336.0  5  5  5
Jaguar     100.000 157.000 -999.0 -999.0 10.8 22.4 100.0  1  1  1
Kangaroo     35.000 56.000 -999.0 -999.0 -999.0 16.3 33.0  3  5  4
Lesser short-tailed shrew .005  .140  7.7  1.4  9.1  2.6 21.5  5  2  4
Little brown bat    .010  .250 17.9  2.0 19.9 24.0 50.0  1  1  1
Man      62.000 1320.000  6.1  1.9  8.0 100.0 267.0  1  1  1
Mole rat      .122 3.000  8.2  2.4 10.6 -999.0 30.0  2  1  1
Mountain beaver    1.350 8.100  8.4  2.8 11.2 -999.0 45.0  3  1  3
Mouse      .023  .400 11.9  1.3 13.2  3.2 19.0  4  1  3
Musk shrew     .048  .330 10.8  2.0 12.8  2.0 30.0  4  1  3
N. American opossum   1.700 6.300 13.8  5.6 19.4  5.0 12.0  2  1  1
Nine-banded armadillo  3.500 10.800 14.3  3.1 17.4  6.5 120.0  2  1  1
Okapi      250.000 490.000 -999.0  1.0 -999.0 23.6 440.0  5  5  5
Owl monkey     .480 15.500 15.2  1.8 17.0 12.0 140.0  2  2  2
Patas monkey    10.000 115.000 10.0  .9 10.9 20.2 170.0  4  4  4
Phanlanger     1.620 11.400 11.9  1.8 13.7 13.0 17.0  2  1  2
Pig      192.000 180.000  6.5  1.9  8.4 27.0 115.0  4  4  4
Rabbit      2.500 12.100  7.5  .9  8.4 18.0 31.0  5  5  5
Raccoon      4.288 39.200 -999.0 -999.0 12.5 13.7 63.0  2  2  2
Rat       .280 1.900 10.6  2.6 13.2  4.7 21.0  3  1  3
Red fox      4.235 50.400  7.4  2.4  9.8  9.8 52.0  1  1  1
Rhesus monkey    6.800 179.000  8.4  1.2  9.6 29.0 164.0  2  3  2
Rock hyrax (Hetero. b)  .750 12.300  5.7  .9  6.6  7.0 225.0  2  2  2
Rock hyrax (Procavia hab) 3.600 21.000  4.9  .5  5.4  6.0 225.0  3  2  3
Roe deer     14.830 98.200 -999.0 -999.0  2.6 17.0 150.0  5  5  5
Sheep      55.500 175.000  3.2  .6  3.8 20.0 151.0  5  5  5
Slow loris     1.400 12.500 -999.0 -999.0 11.0 12.7 90.0  2  2  2
Star nosed mole    .060 1.000  8.1  2.2 10.3  3.5 -999.0  3  1  2
Tenrec      .900 2.600 11.0  2.3 13.3  4.5 60.0  2  1  2
Tree hyrax     2.000 12.300  4.9  .5  5.4  7.5 200.0  3  1  3
Tree shrew     .104 2.500 13.2  2.6 15.8  2.3 46.0  3  2  2
Vervet      4.190 58.000  9.7  .6 10.3 24.0 210.0  4  3  4
Water opossum    3.500 3.900 12.8  6.6 19.4  3.0 14.0  2  1  1
Yellow-bellied marmot  4.050 17.000 -999.0 -999.0 -999.0 13.0 38.0  3  1  1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=106382&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=106382&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=106382&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 Output	view raw output of R engine
Computing time	2 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

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')

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Input Parameters & R Code