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Author*The author of this computation has been verified*
R Software ModulePatrick.Wessarwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationMon, 13 Dec 2010 22:02:53 +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/13/t12922777275af8rpchvaukimx.htm/, Retrieved Tue, 07 May 2024 03:34:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109226, Retrieved Tue, 07 May 2024 03:34:03 +0000
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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)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
-   PD    [Kendall tau Correlation Matrix] [WS 10 - Pearson] [2010-12-13 22:02:53] [89d441ae0711e9b79b5d358f420c1317] [Current]
-   P       [Kendall tau Correlation Matrix] [WS 10 - Kendall] [2010-12-13 22:14:54] [18fa53e8b37a5effc0c5f8a5122cdd2d]
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Dataseries X:
100.44	1576.23	29.29	710.45
100.88	1546.37	28.99	720
101.42	1545.05	28.91	720
99.97	1552.34	29.29	720
100.56	1594.3	30.96	754.78
99.51	1605.78	30.57	802.73
98.96	1673.21	30.59	845.24
100.85	1612.94	31.39	893.91
100.66	1566.34	31.28	931.43
100.22	1530.17	31.1	940
100.30	1582.54	31.7	947.73
100.73	1702.16	32.57	960
101.46	1701.93	32.49	996.96
101.35	1811.15	32.46	1000
101.14	1924.2	32.3	1000
101.68	2034.25	32.97	1000
101.47	2011.13	32.9	1013.04
100.59	2013.04	32.93	1095.24
101.18	2151.67	33.72	1159.09
100.87	1902.09	33.33	1200
99.79	1944.01	33.44	1200
100.74	1916.67	33.89	1282.61
99.34	1967.31	34.34	1513.64
100.07	2119.88	33.56	1669.05
103.68	2216.38	32.67	1700
103.52	2522.83	32.57	1700
104.68	2647.64	33.23	1700
103.75	2631.23	32.85	1665.91
103.70	2693.41	32.61	1650
102.98	3021.76	32.57	1650
106.30	2953.67	32.98	1619.57
107.21	2796.8	31.33	1599.05
106.83	2672.05	29.8	1572.73
105.60	2251.23	28.06	1470
104.30	2046.08	25.47	1268
104.43	2420.04	24.65	1217.39
104.36	2608.89	23.94	1154.09
106.21	2660.47	23.89	984
107.34	2493.98	23.54	900
106.92	2541.7	24.28	900
104.80	2554.6	25.51	916.67
103.85	2699.61	27.03	957.73
103.39	2805.48	27.09	966.09
103.38	2956.66	27.3	980
103.93	3149.51	27.11	990.91
104.41	3372.5	26.39	1000.91
104.47	3379.33	27.54	1042.38
103.84	3517.54	26.85	1142.61
103.65	3527.34	26.82	1214.29
103.17	3281.06	25.9	1218
103.40	3089.65	24.96	1202.61
112.72	3222.76	25.4	1200
114.77	3165.76	24.38	1228.57
116.18	3232.43	24.73	1195.91
116.93	3229.54	25.43	1180
115.19	3071.74	26.04	1210.91
114.55	2850.17	25.59	1272.27




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=109226&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=109226&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109226&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=pearson)
ChocoladeCacaoSuikerGrondnoten
Chocolade10.663-0.6090.238
Cacao0.6631-0.5490.389
Suiker-0.609-0.54910.287
Grondnoten0.2380.3890.2871

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & Chocolade & Cacao & Suiker & Grondnoten \tabularnewline
Chocolade & 1 & 0.663 & -0.609 & 0.238 \tabularnewline
Cacao & 0.663 & 1 & -0.549 & 0.389 \tabularnewline
Suiker & -0.609 & -0.549 & 1 & 0.287 \tabularnewline
Grondnoten & 0.238 & 0.389 & 0.287 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109226&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]Chocolade[/C][C]Cacao[/C][C]Suiker[/C][C]Grondnoten[/C][/ROW]
[ROW][C]Chocolade[/C][C]1[/C][C]0.663[/C][C]-0.609[/C][C]0.238[/C][/ROW]
[ROW][C]Cacao[/C][C]0.663[/C][C]1[/C][C]-0.549[/C][C]0.389[/C][/ROW]
[ROW][C]Suiker[/C][C]-0.609[/C][C]-0.549[/C][C]1[/C][C]0.287[/C][/ROW]
[ROW][C]Grondnoten[/C][C]0.238[/C][C]0.389[/C][C]0.287[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109226&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109226&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)
ChocoladeCacaoSuikerGrondnoten
Chocolade10.663-0.6090.238
Cacao0.6631-0.5490.389
Suiker-0.609-0.54910.287
Grondnoten0.2380.3890.2871







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Chocolade;Cacao0.66280.74420.5251
p-value(0)(0)(0)
Chocolade;Suiker-0.6093-0.6039-0.404
p-value(0)(0)(0)
Chocolade;Grondnoten0.23760.3630.2417
p-value(0.0751)(0.0055)(0.0082)
Cacao;Suiker-0.5485-0.4479-0.2334
p-value(0)(5e-04)(0.0104)
Cacao;Grondnoten0.38910.48820.3386
p-value(0.0028)(1e-04)(2e-04)
Suiker;Grondnoten0.28720.26320.1815
p-value(0.0303)(0.048)(0.0473)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
Chocolade;Cacao & 0.6628 & 0.7442 & 0.5251 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Chocolade;Suiker & -0.6093 & -0.6039 & -0.404 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Chocolade;Grondnoten & 0.2376 & 0.363 & 0.2417 \tabularnewline
p-value & (0.0751) & (0.0055) & (0.0082) \tabularnewline
Cacao;Suiker & -0.5485 & -0.4479 & -0.2334 \tabularnewline
p-value & (0) & (5e-04) & (0.0104) \tabularnewline
Cacao;Grondnoten & 0.3891 & 0.4882 & 0.3386 \tabularnewline
p-value & (0.0028) & (1e-04) & (2e-04) \tabularnewline
Suiker;Grondnoten & 0.2872 & 0.2632 & 0.1815 \tabularnewline
p-value & (0.0303) & (0.048) & (0.0473) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109226&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]Chocolade;Cacao[/C][C]0.6628[/C][C]0.7442[/C][C]0.5251[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Chocolade;Suiker[/C][C]-0.6093[/C][C]-0.6039[/C][C]-0.404[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Chocolade;Grondnoten[/C][C]0.2376[/C][C]0.363[/C][C]0.2417[/C][/ROW]
[ROW][C]p-value[/C][C](0.0751)[/C][C](0.0055)[/C][C](0.0082)[/C][/ROW]
[ROW][C]Cacao;Suiker[/C][C]-0.5485[/C][C]-0.4479[/C][C]-0.2334[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](5e-04)[/C][C](0.0104)[/C][/ROW]
[ROW][C]Cacao;Grondnoten[/C][C]0.3891[/C][C]0.4882[/C][C]0.3386[/C][/ROW]
[ROW][C]p-value[/C][C](0.0028)[/C][C](1e-04)[/C][C](2e-04)[/C][/ROW]
[ROW][C]Suiker;Grondnoten[/C][C]0.2872[/C][C]0.2632[/C][C]0.1815[/C][/ROW]
[ROW][C]p-value[/C][C](0.0303)[/C][C](0.048)[/C][C](0.0473)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109226&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109226&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
Chocolade;Cacao0.66280.74420.5251
p-value(0)(0)(0)
Chocolade;Suiker-0.6093-0.6039-0.404
p-value(0)(0)(0)
Chocolade;Grondnoten0.23760.3630.2417
p-value(0.0751)(0.0055)(0.0082)
Cacao;Suiker-0.5485-0.4479-0.2334
p-value(0)(5e-04)(0.0104)
Cacao;Grondnoten0.38910.48820.3386
p-value(0.0028)(1e-04)(2e-04)
Suiker;Grondnoten0.28720.26320.1815
p-value(0.0303)(0.048)(0.0473)



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')