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Author's title

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 10:58:49 +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/13/t1292237894mbkqz9z1xhyfu1o.htm/, Retrieved Mon, 06 May 2024 12:19:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108830, Retrieved Mon, 06 May 2024 12:19:03 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact135
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]
F   PD    [Kendall tau Correlation Matrix] [Pearson Correlation] [2010-12-13 10:58:49] [7a87ed98a7b21a29d6a45388a9b7b229] [Current]
Feedback Forum
2010-12-16 12:17:02 [Pascal Wijnen] [reply
De student komt tot een juiste verwerking van de gegevens en geeft zo een correcte interpretatie.
2010-12-17 14:42:27 [Stefanie Van Esbroeck] [reply
Je zorgde ervoor dat bij de berekening alle parameters correct zijn aangepast. Hierdoor maakte je een correcte berekening. Je zorgde ook voor een correcte interpretatie van de gegeven output. De interpretatie is ook volledig uitgelegd.

Post a new message
Dataseries X:
989236	10489.94
1008380	10766.23
1207763	10503.76
1368839	10192.51
1469798	10467.48
1498721	10274.97
1761769	10640.91
1653214	10481.6
1599104	10568.7
1421179	10440.07
1163995	10805.87
1037735	10717.5
1015407	10864.86
1039210	10993.41
1258049	11109.32
1469445	11367.14
1552346	11168.31
1549144	11150.22
1785895	11185.68
1662335	11381.15
1629440	11679.07
1467430	12080.73
1202209	12221.93
1076982	12463.15
1039367	12621.69
1063449	12268.63
1335135	12354.35
1491602	13062.91
1591972	13627.64
1641248	13408.62
1898849	13211.99
1798580	13357.74
1762444	13895.63
1622044	13930.01
1368955	13371.72
1262973	13264.82
1195650	12650.36
1269530	12266.39
1479279	12262.89
1607819	12820.13
1712466	12638.32
1721766	11350.01
1949843	11378.02
1821326	11543.55
1757802	10850.66
1590367	9325.01
1260647	8829.04
1149235	8776.39
1016367	8000.86
1027885	7062.93
1262159	7608.92
1520854	8168.12
1544144	8500.33
1564709	8447
1821776	9171.61
1741365	9496.28
1623386	9712.28
1498658	9712.73
1241822	10344.84
1136029	10428.05




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=108830&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=108830&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108830&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)
PassengersDJIA
Passengers10.203
DJIA0.2031

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & Passengers & DJIA \tabularnewline
Passengers & 1 & 0.203 \tabularnewline
DJIA & 0.203 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108830&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]Passengers[/C][C]DJIA[/C][/ROW]
[ROW][C]Passengers[/C][C]1[/C][C]0.203[/C][/ROW]
[ROW][C]DJIA[/C][C]0.203[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108830&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108830&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)
PassengersDJIA
Passengers10.203
DJIA0.2031







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Passengers;DJIA0.20290.21210.1356
p-value(0.1199)(0.1037)(0.1258)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
Passengers;DJIA & 0.2029 & 0.2121 & 0.1356 \tabularnewline
p-value & (0.1199) & (0.1037) & (0.1258) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108830&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]Passengers;DJIA[/C][C]0.2029[/C][C]0.2121[/C][C]0.1356[/C][/ROW]
[ROW][C]p-value[/C][C](0.1199)[/C][C](0.1037)[/C][C](0.1258)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108830&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108830&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
Passengers;DJIA0.20290.21210.1356
p-value(0.1199)(0.1037)(0.1258)



Parameters (Session):
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')