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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 computationTue, 17 Nov 2009 11:02:43 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/17/t1258481028ui8nc7p0hvfes0e.htm/, Retrieved Thu, 02 May 2024 07:03:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57387, Retrieved Thu, 02 May 2024 07:03:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact167

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Back to Back Histogram] [3/11/2009] [2009-11-02 21:58:53] [b98453cac15ba1066b407e146608df68]
- RM D    [Kendall tau Correlation Matrix] [] [2009-11-17 18:02:43] [df67ec12d4744494b58d8461e1971283] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
36	29	114.1	4.3
24	31	110.3	3.9
10	31	103.9	4
17	33	101.6	4.3
14	37	94.6	4.8
61	30	95.9	4.4
57	20	104.7	4.3
34	19	102.8	4.7
53	17	98.1	4.7
11	22	113.9	4.9
56	12	80.9	5
51	25	95.7	4.2
36	25	113.2	4.3
33	29	105.9	4.8
16	32	108.8	4.8
12	31	102.3	4.8
21	28	99	4.2
63	28	100.7	4.6
55	28	115.5	4.8
47	32	100.7	4.5
48	35	109.9	4.4
12	30	114.6	4.3
48	32	85.4	3.9
59	38	100.5	3.7
41	37	114.8	4
39	28	116.5	4.1
20	34	112.9	3.7
38	35	102	3.8
49	32	106	3.8
59	39	105.3	3.8
36	37	118.8	3.3
42	38	106.1	3.3
46	35	109.3	3.3
8	25	117.2	3.2
53	25	92.5	3.4
50	26	104.2	4.2
47	13	112.5	4.9
24	19	122.4	5.1
17	17	113.3	5.5
45	21	100	5.6
47	23	110.7	6.4
27	18	112.8	6.1
45	12	109.8	7.1
31	7	117.3	7.8
45	4	109.1	7.9
13	14	115.9	7.4
39	16	96	7.5
47	13	99.8	6.8
30	13	116.8	5.2
14	10	115.7	4.7
3	19	99.4	4.1
5	13	94.3	3.9
43	14	91	2.6
53	25	93.2	2.7
35	28	103.1	1.8
21	30	94.1	1
34	31	91.8	0.3
1	42	102.7	1.3
44	41	82.6	1
46	38	89.1	1.1

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=57387&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=57387&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57387&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( Conjunctuurenquête_Bouwnijverheid , consumentenvertrouwen )0.02660611320156580.768722424640131
tau( Conjunctuurenquête_Bouwnijverheid , Industriële_Productie )-0.21286779920250.0169941811177777
tau( Conjunctuurenquête_Bouwnijverheid , Consumptieprijsindex )-0.01208993824345780.893265430263678
tau( consumentenvertrouwen , Industriële_Productie )-0.09421092114313690.294757204095234
tau( consumentenvertrouwen , Consumptieprijsindex )-0.4741480629176171.79835338929608e-07
tau( Industriële_Productie , Consumptieprijsindex )0.2344349964358110.00884273495319166

\begin{tabular}{lllllllll}
\hline
Kendall tau rank correlations for all pairs of data series \tabularnewline
pair & tau & p-value \tabularnewline
tau( Conjunctuurenquête_Bouwnijverheid , consumentenvertrouwen ) & 0.0266061132015658 & 0.768722424640131 \tabularnewline
tau( Conjunctuurenquête_Bouwnijverheid , Industriële_Productie ) & -0.2128677992025 & 0.0169941811177777 \tabularnewline
tau( Conjunctuurenquête_Bouwnijverheid , Consumptieprijsindex ) & -0.0120899382434578 & 0.893265430263678 \tabularnewline
tau( consumentenvertrouwen , Industriële_Productie ) & -0.0942109211431369 & 0.294757204095234 \tabularnewline
tau( consumentenvertrouwen , Consumptieprijsindex ) & -0.474148062917617 & 1.79835338929608e-07 \tabularnewline
tau( Industriële_Productie , Consumptieprijsindex ) & 0.234434996435811 & 0.00884273495319166 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57387&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( Conjunctuurenquête_Bouwnijverheid , consumentenvertrouwen )[/C][C]0.0266061132015658[/C][C]0.768722424640131[/C][/ROW]
[ROW][C]tau( Conjunctuurenquête_Bouwnijverheid , Industriële_Productie )[/C][C]-0.2128677992025[/C][C]0.0169941811177777[/C][/ROW]
[ROW][C]tau( Conjunctuurenquête_Bouwnijverheid , Consumptieprijsindex )[/C][C]-0.0120899382434578[/C][C]0.893265430263678[/C][/ROW]
[ROW][C]tau( consumentenvertrouwen , Industriële_Productie )[/C][C]-0.0942109211431369[/C][C]0.294757204095234[/C][/ROW]
[ROW][C]tau( consumentenvertrouwen , Consumptieprijsindex )[/C][C]-0.474148062917617[/C][C]1.79835338929608e-07[/C][/ROW]
[ROW][C]tau( Industriële_Productie , Consumptieprijsindex )[/C][C]0.234434996435811[/C][C]0.00884273495319166[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57387&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57387&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( Conjunctuurenquête_Bouwnijverheid , consumentenvertrouwen )0.02660611320156580.768722424640131
tau( Conjunctuurenquête_Bouwnijverheid , Industriële_Productie )-0.21286779920250.0169941811177777
tau( Conjunctuurenquête_Bouwnijverheid , Consumptieprijsindex )-0.01208993824345780.893265430263678
tau( consumentenvertrouwen , Industriële_Productie )-0.09421092114313690.294757204095234
tau( consumentenvertrouwen , Consumptieprijsindex )-0.4741480629176171.79835338929608e-07
tau( Industriële_Productie , Consumptieprijsindex )0.2344349964358110.00884273495319166


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')