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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 computationSat, 18 Dec 2010 17:39:49 +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/18/t1292693897yp2qbcd4n4fj1ni.htm/, Retrieved Tue, 30 Apr 2024 00:48:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112129, Retrieved Tue, 30 Apr 2024 00:48:57 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact203
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] [WS10 PCM DMA] [2010-12-09 16:41:09] [2099aacba481f75a7f949aa310cab952]
- R  D    [Kendall tau Correlation Matrix] [Workshop 10, Pear...] [2010-12-10 12:51:04] [3635fb7041b1998c5a1332cf9de22bce]
- R  D        [Kendall tau Correlation Matrix] [Paper Pearson Cor...] [2010-12-18 17:39:49] [23a9b79f355c69a75648521a893cf584] [Current]
-   P           [Kendall tau Correlation Matrix] [Paper Kendall Cor...] [2010-12-18 17:46:34] [3635fb7041b1998c5a1332cf9de22bce]
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Dataseries X:
97,06	21.454	631.923	130.678
97,73	23.899	654.294	120.877
98	24.939	671.833	137.114
97,76	23.580	586.840	134.406
97,48	24.562	600.969	120.262
97,77	24.696	625.568	130.846
97,96	23.785	558.110	120.343
98,22	23.812	630.577	98.881
98,51	21.917	628.654	115.678
98,19	19.713	603.184	120.796
98,37	19.282	656.255	94.261
98,31	18.788	600.730	89.151
98,6	21.453	670.326	119.880
98,96	24.482	678.423	131.468
99,11	27.474	641.502	155.089
99,64	27.264	625.311	149.581
100,02	27.349	628.177	122.788
99,98	30.632	589.767	143.900
100,32	29.429	582.471	112.115
100,44	30.084	636.248	109.600
100,51	26.290	599.885	117.446
101	24.379	621.694	118.456
100,88	23.335	637.406	101.901
100,55	21.346	595.994	89.940
100,82	21.106	696.308	129.143
101,5	24.514	674.201	126.102
102,15	28.353	648.861	143.048
102,39	30.805	649.605	142.258
102,54	31.348	672.392	131.011
102,85	34.556	598.396	146.471
103,47	33.855	613.177	114.073
103,56	34.787	638.104	114.642
103,69	32.529	615.632	118.226
103,49	29.998	634.465	111.338
103,47	29.257	638.686	108.701
103,45	28.155	604.243	80.512
103,48	30.466	706.669	146.865
103,93	35.704	677.185	137.179
103,89	39.327	644.328	166.536
104,4	39.351	664.825	137.070
104,79	42.234	605.707	127.090
104,77	43.630	600.136	139.966
105,13	43.722	612.166	122.243
105,26	43.121	599.659	109.097
104,96	37.985	634.210	116.591
104,75	37.135	618.234	111.964
105,01	34.646	613.576	109.754
105,15	33.026	627.200	77.609
105,2	35.087	668.973	138.445
105,77	38.846	651.479	127.901
105,78	42.013	619.661	156.615
106,26	43.908	644.260	133.264
106,13	42.868	579.936	143.521
106,12	44.423	601.752	152.139
106,57	44.167	595.376	131.523
106,44	43.636	588.902	113.925
106,54	44.382	634.341	86.495
107,1	42.142	594.305	127.877
108,1	43.452	606.200	107.017
108,4	36.912	610.926	78.716
108,84	42.413	633.685	138.278
109,62	45.344	639.696	144.238
110,42	44.873	659.451	143.679
110,67	47.510	593.248	159.932
111,66	49.554	606.677	136.781
112,28	47.369	599.434	148.173
112,87	45.998	569.578	125.673
112,18	48.140	629.873	105.573
112,36	48.441	613.438	122.405
112,16	44.928	604.172	128.045
111,49	40.454	658.328	94.467
111,25	38.661	612.633	85.573
111,36	37.246	707.372	121.501
111,74	36.843	739.770	125.074
111,1	36.424	777.535	144.979
111,33	37.594	685.030	142.120
111,25	38.144	730.234	124.213
111,04	38.737	714.154	144.407
110,97	34.560	630.872	125.170
111,31	36.080	719.492	109.267
111,02	33.508	677.023	122.354
111,07	35.462	679.272	122.589
111,36	33.374	718.317	104.982
111,54	32.110	645.672	90.542




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112129&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]4 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=112129&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112129&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Correlations for all pairs of data series (method=pearson)
CPIvacatureswerklozeninschrijvingen
CPI10.80.2490.029
vacatures0.81-0.0990.242
werklozen0.249-0.09910.12
inschrijvingen0.0290.2420.121

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & CPI & vacatures & werklozen & inschrijvingen \tabularnewline
CPI & 1 & 0.8 & 0.249 & 0.029 \tabularnewline
vacatures & 0.8 & 1 & -0.099 & 0.242 \tabularnewline
werklozen & 0.249 & -0.099 & 1 & 0.12 \tabularnewline
inschrijvingen & 0.029 & 0.242 & 0.12 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112129&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]CPI[/C][C]vacatures[/C][C]werklozen[/C][C]inschrijvingen[/C][/ROW]
[ROW][C]CPI[/C][C]1[/C][C]0.8[/C][C]0.249[/C][C]0.029[/C][/ROW]
[ROW][C]vacatures[/C][C]0.8[/C][C]1[/C][C]-0.099[/C][C]0.242[/C][/ROW]
[ROW][C]werklozen[/C][C]0.249[/C][C]-0.099[/C][C]1[/C][C]0.12[/C][/ROW]
[ROW][C]inschrijvingen[/C][C]0.029[/C][C]0.242[/C][C]0.12[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112129&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112129&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)
CPIvacatureswerklozeninschrijvingen
CPI10.80.2490.029
vacatures0.81-0.0990.242
werklozen0.249-0.09910.12
inschrijvingen0.0290.2420.121







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
CPI;vacatures0.80030.81680.6207
p-value(0)(0)(0)
CPI;werklozen0.24880.1380.0887
p-value(0.0225)(0.2107)(0.2326)
CPI;inschrijvingen0.02930.03990.0192
p-value(0.791)(0.7189)(0.7958)
vacatures;werklozen-0.0991-0.1419-0.0912
p-value(0.3697)(0.1974)(0.2193)
vacatures;inschrijvingen0.24170.25910.1756
p-value(0.0267)(0.0175)(0.0181)
werklozen;inschrijvingen0.11970.0940.062
p-value(0.2781)(0.3944)(0.404)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
CPI;vacatures & 0.8003 & 0.8168 & 0.6207 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
CPI;werklozen & 0.2488 & 0.138 & 0.0887 \tabularnewline
p-value & (0.0225) & (0.2107) & (0.2326) \tabularnewline
CPI;inschrijvingen & 0.0293 & 0.0399 & 0.0192 \tabularnewline
p-value & (0.791) & (0.7189) & (0.7958) \tabularnewline
vacatures;werklozen & -0.0991 & -0.1419 & -0.0912 \tabularnewline
p-value & (0.3697) & (0.1974) & (0.2193) \tabularnewline
vacatures;inschrijvingen & 0.2417 & 0.2591 & 0.1756 \tabularnewline
p-value & (0.0267) & (0.0175) & (0.0181) \tabularnewline
werklozen;inschrijvingen & 0.1197 & 0.094 & 0.062 \tabularnewline
p-value & (0.2781) & (0.3944) & (0.404) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112129&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]CPI;vacatures[/C][C]0.8003[/C][C]0.8168[/C][C]0.6207[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]CPI;werklozen[/C][C]0.2488[/C][C]0.138[/C][C]0.0887[/C][/ROW]
[ROW][C]p-value[/C][C](0.0225)[/C][C](0.2107)[/C][C](0.2326)[/C][/ROW]
[ROW][C]CPI;inschrijvingen[/C][C]0.0293[/C][C]0.0399[/C][C]0.0192[/C][/ROW]
[ROW][C]p-value[/C][C](0.791)[/C][C](0.7189)[/C][C](0.7958)[/C][/ROW]
[ROW][C]vacatures;werklozen[/C][C]-0.0991[/C][C]-0.1419[/C][C]-0.0912[/C][/ROW]
[ROW][C]p-value[/C][C](0.3697)[/C][C](0.1974)[/C][C](0.2193)[/C][/ROW]
[ROW][C]vacatures;inschrijvingen[/C][C]0.2417[/C][C]0.2591[/C][C]0.1756[/C][/ROW]
[ROW][C]p-value[/C][C](0.0267)[/C][C](0.0175)[/C][C](0.0181)[/C][/ROW]
[ROW][C]werklozen;inschrijvingen[/C][C]0.1197[/C][C]0.094[/C][C]0.062[/C][/ROW]
[ROW][C]p-value[/C][C](0.2781)[/C][C](0.3944)[/C][C](0.404)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112129&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112129&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
CPI;vacatures0.80030.81680.6207
p-value(0)(0)(0)
CPI;werklozen0.24880.1380.0887
p-value(0.0225)(0.2107)(0.2326)
CPI;inschrijvingen0.02930.03990.0192
p-value(0.791)(0.7189)(0.7958)
vacatures;werklozen-0.0991-0.1419-0.0912
p-value(0.3697)(0.1974)(0.2193)
vacatures;inschrijvingen0.24170.25910.1756
p-value(0.0267)(0.0175)(0.0181)
werklozen;inschrijvingen0.11970.0940.062
p-value(0.2781)(0.3944)(0.404)



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')