FreeStatistics.org

Free Statistics

of Irreproducible Research!

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, 31 Oct 2008 07:20:14 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Oct/31/t1225459253ljw5x8vk2vptvn6.htm/, Retrieved Wed, 22 May 2024 07:06:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=20266, Retrieved Wed, 22 May 2024 07:06:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact176

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Mean Plot] [workshop 3] [2007-10-26 12:14:28] [e9ffc5de6f8a7be62f22b142b5b6b1a8]
F RMPD  [Blocked Bootstrap Plot - Central Tendency] [vraag 4] [2008-10-29 19:47:27] [c45c87b96bbf32ffc2144fc37d767b2e]
F RMPD    [Kendall tau Correlation Matrix] [vraag 1] [2008-10-30 14:09:39] [c45c87b96bbf32ffc2144fc37d767b2e]
F    D      [Kendall tau Correlation Matrix] [Hypothesis Testin...] [2008-10-31 13:09:45] [063e4b67ad7d3a8a83eccec794cd5aa7]
F    D          [Kendall tau Correlation Matrix] [Hypothesis Testin...] [2008-10-31 13:20:14] [a3db428e3d00990025d790b4cc7b8bfa] [Current]
Feedback Forum
2008-11-07 12:50:20 [Tom Ardies] [reply] 
Je moet volgens mij de volledige dataset gebruiken. De jaren 1985 en 1993 horen dus wel degelijk tot de dataset en moet je dus gebruiken voor je analyse.
2008-11-09 15:07:11 [Bob Leysen] [reply] 
De grafiek is fout, zie hieronder:

http://www.freestatistics.org/blog/index.php?v=date/2008/Nov/02/t12256472532j8zy95yw5bzx06.htm

De getallen in de rechthoeken zeggen niet wat correlatie is, dit is de betrouwbaarheid van uw correlatie. Hoe hoger het getal, hoe meer louter toeval.
De tweede kolom (RNR) is het meest betrouwbaar (0,01).

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3	1,6	22,3	0,83	34,4
3,8	5,2	25,1	0,84	33,4
4	9,2	27,7	0,85	34,8
3,5	4,6	24,9	0,83	33,7
4,1	10,6	29,5	0,83	36,3

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=20266&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=20266&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20266&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Kendall tau rank correlations for all pairs of data series
pairtaup-value
tau( RNVM , RNR )10.0166666666666666
tau( RNVM , RCF )10.0166666666666666
tau( RNVM , RLEZ )0.3585685828003180.405380556458943
tau( RNVM , REV )0.40.483333333333333
tau( RNR , RCF )10.0166666666666666
tau( RNR , RLEZ )0.3585685828003180.405380556458943
tau( RNR , REV )0.40.483333333333333
tau( RCF , RLEZ )0.3585685828003180.405380556458943
tau( RCF , REV )0.40.483333333333333
tau( RLEZ , REV )-0.1195228609334390.781511294998713

\begin{tabular}{lllllllll}
\hline
Kendall tau rank correlations for all pairs of data series \tabularnewline
pair & tau & p-value \tabularnewline
tau( RNVM , RNR ) & 1 & 0.0166666666666666 \tabularnewline
tau( RNVM , RCF ) & 1 & 0.0166666666666666 \tabularnewline
tau( RNVM , RLEZ ) & 0.358568582800318 & 0.405380556458943 \tabularnewline
tau( RNVM , REV ) & 0.4 & 0.483333333333333 \tabularnewline
tau( RNR , RCF ) & 1 & 0.0166666666666666 \tabularnewline
tau( RNR , RLEZ ) & 0.358568582800318 & 0.405380556458943 \tabularnewline
tau( RNR , REV ) & 0.4 & 0.483333333333333 \tabularnewline
tau( RCF , RLEZ ) & 0.358568582800318 & 0.405380556458943 \tabularnewline
tau( RCF , REV ) & 0.4 & 0.483333333333333 \tabularnewline
tau( RLEZ , REV ) & -0.119522860933439 & 0.781511294998713 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=20266&T=1

[TABLE]
[ROW][C]Kendall tau rank correlations for all pairs of data series[/C][/ROW]
[ROW][C]pair[/C][C]tau[/C][C]p-value[/C][/ROW]
[ROW][C]tau( RNVM , RNR )[/C][C]1[/C][C]0.0166666666666666[/C][/ROW]
[ROW][C]tau( RNVM , RCF )[/C][C]1[/C][C]0.0166666666666666[/C][/ROW]
[ROW][C]tau( RNVM , RLEZ )[/C][C]0.358568582800318[/C][C]0.405380556458943[/C][/ROW]
[ROW][C]tau( RNVM , REV )[/C][C]0.4[/C][C]0.483333333333333[/C][/ROW]
[ROW][C]tau( RNR , RCF )[/C][C]1[/C][C]0.0166666666666666[/C][/ROW]
[ROW][C]tau( RNR , RLEZ )[/C][C]0.358568582800318[/C][C]0.405380556458943[/C][/ROW]
[ROW][C]tau( RNR , REV )[/C][C]0.4[/C][C]0.483333333333333[/C][/ROW]
[ROW][C]tau( RCF , RLEZ )[/C][C]0.358568582800318[/C][C]0.405380556458943[/C][/ROW]
[ROW][C]tau( RCF , REV )[/C][C]0.4[/C][C]0.483333333333333[/C][/ROW]
[ROW][C]tau( RLEZ , REV )[/C][C]-0.119522860933439[/C][C]0.781511294998713[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=20266&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=20266&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:

Kendall tau rank correlations for all pairs of data series
pairtaup-value
tau( RNVM , RNR )10.0166666666666666
tau( RNVM , RCF )10.0166666666666666
tau( RNVM , RLEZ )0.3585685828003180.405380556458943
tau( RNVM , REV )0.40.483333333333333
tau( RNR , RCF )10.0166666666666666
tau( RNR , RLEZ )0.3585685828003180.405380556458943
tau( RNR , REV )0.40.483333333333333
tau( RCF , RLEZ )0.3585685828003180.405380556458943
tau( RCF , REV )0.40.483333333333333
tau( RLEZ , REV )-0.1195228609334390.781511294998713


Figures (Output of Computation)

Input Parameters & R Code

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