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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 computationSun, 12 Dec 2010 20:45:49 +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/12/t12921866434756ymilvo4p0vz.htm/, Retrieved Tue, 07 May 2024 16:05:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108673, Retrieved Tue, 07 May 2024 16:05:58 +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)
F     [Kendall tau Correlation Matrix] [Workshop 10 Kenda...] [2010-12-12 20:39:50] [ebb35fb07def4d07c0eb7ec8d2fd3b0e]
F   P     [Kendall tau Correlation Matrix] [Workshop 10] [2010-12-12 20:45:49] [4bfaadb29d89ff24ebcdd4f425066435] [Current]
Feedback Forum
2010-12-15 19:06:51 [Pascal Wijnen] [reply
De student komt tot een juiste verwerking, er is echter weer geen interpretatie aanwezig! Dit model heeft ook een negatieve parameter, maar we kunnen deze wel gebruiken daar hier niet voldaan moet worden aan de normaal-verdeling vereiste. Ook is deze methode beter daar het niet gevoelig is voor mogelijke outliers.

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Dataseries X:
12008.00	4.00
9169.00	5.90
8788.00	7.10
8417.00	10.50
8247.00	15.10
8197.00	16.80
8236.00	15.30
8253.00	18.40
7733.00	16.10
8366.00	11.30
8626.00	7.90
8863.00	5.60
10102.00	3.40
8463.00	4.80
9114.00	6.50
8563.00	8.50
8872.00	15.10
8301.00	15.70
8301.00	18.70
8278.00	19.20
7736.00	12.90
7973.00	14.40
8268.00	6.20
9476.00	3.30
11100.00	4.60
8962.00	7.10
9173.00	7.80
8738.00	9.90
8459.00	13.60
8078.00	17.10
8411.00	17.80
8291.00	18.60
7810.00	14.70
8616.00	10.50
8312.00	8.60
9692.00	4.40
9911.00	2.30
8915.00	2.80
9452.00	8.80
9112.00	10.70
8472.00	13.90
8230.00	19.30
8384.00	19.50
8625.00	20.40
8221.00	15.30
8649.00	7.90
8625.00	8.30
10443.00	4.50
10357.00	3.20
8586.00	5.00
8892.00	6.60
8329.00	11.10
8101.00	12.80
7922.00	16.30
8120.00	17.40
7838.00	18.90
7735.00	15.80
8406.00	11.70
8209.00	6.40
9451.00	2.90
10041.00	4.70
9411.00	2.40
10405.00	7.20
8467.00	10.70
8464.00	13.40
8102.00	18.30
7627.00	18.40
7513.00	16.80
7510.00	16.60
8291.00	14.10
8064.00	6.10
9383.00	3.50
9706.00	1.70
8579.00	2.30
9474.00	4.50
8318.00	9.30
8213.00	14.20
8059.00	17.30
9111.00	23.00
7708.00	16.30
7680.00	18.40
8014.00	14.20
8007.00	9.10
8718.00	5.90
9486.00	7.20
9113.00	6.80
9025.00	8.00
8476.00	14.30
7952.00	14.60
7759.00	17.50
7835.00	17.20
7600.00	17.20
7651.00	14.10
8319.00	10.40
8812.00	6.80
8630.00	4.10




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=108673&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=108673&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108673&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'Gwilym Jenkins' @ 72.249.127.135







Correlations for all pairs of data series (method=kendall)
SterftegevallenTemperatuur
Sterftegevallen1-0.525
Temperatuur-0.5251

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & Sterftegevallen & Temperatuur \tabularnewline
Sterftegevallen & 1 & -0.525 \tabularnewline
Temperatuur & -0.525 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108673&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]Sterftegevallen[/C][C]Temperatuur[/C][/ROW]
[ROW][C]Sterftegevallen[/C][C]1[/C][C]-0.525[/C][/ROW]
[ROW][C]Temperatuur[/C][C]-0.525[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108673&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108673&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=kendall)
SterftegevallenTemperatuur
Sterftegevallen1-0.525
Temperatuur-0.5251







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Sterftegevallen;Temperatuur-0.6712-0.7231-0.5254
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
Sterftegevallen;Temperatuur & -0.6712 & -0.7231 & -0.5254 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108673&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]Sterftegevallen;Temperatuur[/C][C]-0.6712[/C][C]-0.7231[/C][C]-0.5254[/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=108673&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108673&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
Sterftegevallen;Temperatuur-0.6712-0.7231-0.5254
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')