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*The author of this computation has been verified*

R Software Module

rwasp_pairs.wasp

Title produced by software

Kendall tau Correlation Matrix

Date of computation

Tue, 21 Dec 2010 11:36:21 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/21/t1292931304suf8yezexvamnhz.htm/, Retrieved Sun, 19 May 2024 20:27:25 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113297, Retrieved Sun, 19 May 2024 20:27:25 +0000

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Estimated Impact

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Kendall tau Correlation Matrix] [pearson ws10] [2010-12-21 11:36:21] [4c854bb223ec27caaa7bcfc5e77b0dbd] [Current]

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6282154	162556	807	213118	1	1
4321023	29790	444	81767	1	1
4111912	87550	412	153198	1	1
223193	84738	428	-26007	0	1
1491348	54660	315	126942	1	1
1629616	42634	168	157214	1	0
1398893	40949	263	129352	0	1
1926517	45187	267	234817	1	0
983660	37704	228	60448	1	1
1443586	16275	129	47818	1	1
1073089	25830	104	245546	0	1
984885	12679	122	48020	0	1
1405225	18014	393	-1710	1	1
227132	43556	190	32648	0	1
929118	24811	280	95350	1	1
1071292	6575	63	151352	0	0
638830	7123	102	288170	0	0
856956	21950	265	114337	1	1
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292154	5131	44	86111	0	0
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296261	529	7	63870	0	1
230621	2126	41	21001	1	1
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333505	776	8	64230	0	0
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278990	1526	21	69351	1	0
276898	592	7	64270	0	0
327007	1182	11	70694	0	0
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304555	989	12	58930	0	1
298096	438	9	58320	0	0
231861	726	3	69980	0	1
309422	1303	57	69863	0	1
286963	7419	21	63255	1	1
269753	1164	15	57320	1	1
448243	3310	32	75230	1	1
165404	1920	11	79420	0	1
204325	965	2	73490	0	0
407159	3256	23	35250	0	1
290476	1135	20	62285	1	0
275311	1270	24	69206	0	0
246541	661	1	65920	0	0
253468	1013	1	69770	0	0
240897	2844	74	72683	0	1
-83265	11528	68	-14545	1	1
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272713	2264	20	55174	0	1
215362	5109	82	67038	1	1
42754	3999	21	51252	0	1
306275	35624	244	157278	0	1
253537	9252	32	79510	0	1
372631	15236	86	77440	0	1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

\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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
R Framework error message & Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113297&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[ROW][C]R Framework error message[/C][C]Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113297&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113297&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Correlations for all pairs of data series (method=pearson)
	Wealth	Costs	Orders	Dividends	Group	Gender
Wealth	1	0.808	-0.463	0.568	0.046	-0.239
Costs	0.808	1	-0.234	0.358	0.092	-0.122
Orders	-0.463	-0.234	1	-0.778	0.225	0.504
Dividends	0.568	0.358	-0.778	1	-0.115	-0.401
Group	0.046	0.092	0.225	-0.115	1	0.147
Gender	-0.239	-0.122	0.504	-0.401	0.147	1

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & Wealth & Costs & Orders & Dividends & Group & Gender
 \tabularnewline
Wealth & 1 & 0.808 & -0.463 & 0.568 & 0.046 & -0.239 \tabularnewline
Costs & 0.808 & 1 & -0.234 & 0.358 & 0.092 & -0.122 \tabularnewline
Orders & -0.463 & -0.234 & 1 & -0.778 & 0.225 & 0.504 \tabularnewline
Dividends & 0.568 & 0.358 & -0.778 & 1 & -0.115 & -0.401 \tabularnewline
Group & 0.046 & 0.092 & 0.225 & -0.115 & 1 & 0.147 \tabularnewline
Gender
 & -0.239 & -0.122 & 0.504 & -0.401 & 0.147 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113297&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]Wealth[/C][C]Costs[/C][C]Orders[/C][C]Dividends[/C][C]Group[/C][C]Gender
[/C][/ROW]
[ROW][C]Wealth[/C][C]1[/C][C]0.808[/C][C]-0.463[/C][C]0.568[/C][C]0.046[/C][C]-0.239[/C][/ROW]
[ROW][C]Costs[/C][C]0.808[/C][C]1[/C][C]-0.234[/C][C]0.358[/C][C]0.092[/C][C]-0.122[/C][/ROW]
[ROW][C]Orders[/C][C]-0.463[/C][C]-0.234[/C][C]1[/C][C]-0.778[/C][C]0.225[/C][C]0.504[/C][/ROW]
[ROW][C]Dividends[/C][C]0.568[/C][C]0.358[/C][C]-0.778[/C][C]1[/C][C]-0.115[/C][C]-0.401[/C][/ROW]
[ROW][C]Group[/C][C]0.046[/C][C]0.092[/C][C]0.225[/C][C]-0.115[/C][C]1[/C][C]0.147[/C][/ROW]
[ROW][C]Gender
[/C][C]-0.239[/C][C]-0.122[/C][C]0.504[/C][C]-0.401[/C][C]0.147[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113297&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113297&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)
	Wealth	Costs	Orders	Dividends	Group	Gender
Wealth	1	0.808	-0.463	0.568	0.046	-0.239
Costs	0.808	1	-0.234	0.358	0.092	-0.122
Orders	-0.463	-0.234	1	-0.778	0.225	0.504
Dividends	0.568	0.358	-0.778	1	-0.115	-0.401
Group	0.046	0.092	0.225	-0.115	1	0.147
Gender	-0.239	-0.122	0.504	-0.401	0.147	1

Correlations for all pairs of data series with p-values
pair	Pearson r	Spearman rho	Kendall tau
Wealth;Costs	0.808	0.7468	0.6208
p-value	(0)	(0)	(0)
Wealth;Orders	-0.4631	-0.6444	-0.3742
p-value	(0)	(0)	(0)
Wealth;Dividends	0.5682	0.8089	0.6158
p-value	(0)	(0)	(0)
Wealth;Group	0.0456	-0.1232	-0.1025
p-value	(0.346)	(0.0106)	(0.0107)
Wealth;Gender	-0.2388	-0.755	-0.5581
p-value	(0)	(0)	(0)
Costs;Orders	-0.2337	-0.2576	-0.0972
p-value	(0)	(0)	(0.0042)
Costs;Dividends	0.3583	0.6438	0.4882
p-value	(0)	(0)	(0)
Costs;Group	0.092	-0.0163	-0.0137
p-value	(0.0565)	(0.7368)	(0.7363)
Costs;Gender	-0.1222	-0.5365	-0.3942
p-value	(0.0112)	(0)	(0)
Orders;Dividends	-0.7781	-0.6756	-0.4233
p-value	(0)	(0)	(0)
Orders;Group	0.2252	0.249	0.2077
p-value	(0)	(0)	(0)
Orders;Gender	0.5042	0.7675	0.5797
p-value	(0)	(0)	(0)
Dividends;Group	-0.1152	-0.1052	-0.0909
p-value	(0.0168)	(0.0292)	(0.0294)
Dividends;Gender	-0.4015	-0.733	-0.5417
p-value	(0)	(0)	(0)
Group;Gender	0.147	0.2702	0.2384
p-value	(0.0022)	(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
Wealth;Costs & 0.808 & 0.7468 & 0.6208 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Wealth;Orders & -0.4631 & -0.6444 & -0.3742 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Wealth;Dividends & 0.5682 & 0.8089 & 0.6158 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Wealth;Group & 0.0456 & -0.1232 & -0.1025 \tabularnewline
p-value & (0.346) & (0.0106) & (0.0107) \tabularnewline
Wealth;Gender
 & -0.2388 & -0.755 & -0.5581 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Costs;Orders & -0.2337 & -0.2576 & -0.0972 \tabularnewline
p-value & (0) & (0) & (0.0042) \tabularnewline
Costs;Dividends & 0.3583 & 0.6438 & 0.4882 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Costs;Group & 0.092 & -0.0163 & -0.0137 \tabularnewline
p-value & (0.0565) & (0.7368) & (0.7363) \tabularnewline
Costs;Gender
 & -0.1222 & -0.5365 & -0.3942 \tabularnewline
p-value & (0.0112) & (0) & (0) \tabularnewline
Orders;Dividends & -0.7781 & -0.6756 & -0.4233 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Orders;Group & 0.2252 & 0.249 & 0.2077 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Orders;Gender
 & 0.5042 & 0.7675 & 0.5797 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Dividends;Group & -0.1152 & -0.1052 & -0.0909 \tabularnewline
p-value & (0.0168) & (0.0292) & (0.0294) \tabularnewline
Dividends;Gender
 & -0.4015 & -0.733 & -0.5417 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Group;Gender
 & 0.147 & 0.2702 & 0.2384 \tabularnewline
p-value & (0.0022) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113297&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]Wealth;Costs[/C][C]0.808[/C][C]0.7468[/C][C]0.6208[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Wealth;Orders[/C][C]-0.4631[/C][C]-0.6444[/C][C]-0.3742[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Wealth;Dividends[/C][C]0.5682[/C][C]0.8089[/C][C]0.6158[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Wealth;Group[/C][C]0.0456[/C][C]-0.1232[/C][C]-0.1025[/C][/ROW]
[ROW][C]p-value[/C][C](0.346)[/C][C](0.0106)[/C][C](0.0107)[/C][/ROW]
[ROW][C]Wealth;Gender
[/C][C]-0.2388[/C][C]-0.755[/C][C]-0.5581[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Costs;Orders[/C][C]-0.2337[/C][C]-0.2576[/C][C]-0.0972[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0.0042)[/C][/ROW]
[ROW][C]Costs;Dividends[/C][C]0.3583[/C][C]0.6438[/C][C]0.4882[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Costs;Group[/C][C]0.092[/C][C]-0.0163[/C][C]-0.0137[/C][/ROW]
[ROW][C]p-value[/C][C](0.0565)[/C][C](0.7368)[/C][C](0.7363)[/C][/ROW]
[ROW][C]Costs;Gender
[/C][C]-0.1222[/C][C]-0.5365[/C][C]-0.3942[/C][/ROW]
[ROW][C]p-value[/C][C](0.0112)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Orders;Dividends[/C][C]-0.7781[/C][C]-0.6756[/C][C]-0.4233[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Orders;Group[/C][C]0.2252[/C][C]0.249[/C][C]0.2077[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Orders;Gender
[/C][C]0.5042[/C][C]0.7675[/C][C]0.5797[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Dividends;Group[/C][C]-0.1152[/C][C]-0.1052[/C][C]-0.0909[/C][/ROW]
[ROW][C]p-value[/C][C](0.0168)[/C][C](0.0292)[/C][C](0.0294)[/C][/ROW]
[ROW][C]Dividends;Gender
[/C][C]-0.4015[/C][C]-0.733[/C][C]-0.5417[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Group;Gender
[/C][C]0.147[/C][C]0.2702[/C][C]0.2384[/C][/ROW]
[ROW][C]p-value[/C][C](0.0022)[/C][C](0)[/C][C](0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113297&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113297&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
pair	Pearson r	Spearman rho	Kendall tau
Wealth;Costs	0.808	0.7468	0.6208
p-value	(0)	(0)	(0)
Wealth;Orders	-0.4631	-0.6444	-0.3742
p-value	(0)	(0)	(0)
Wealth;Dividends	0.5682	0.8089	0.6158
p-value	(0)	(0)	(0)
Wealth;Group	0.0456	-0.1232	-0.1025
p-value	(0.346)	(0.0106)	(0.0107)
Wealth;Gender	-0.2388	-0.755	-0.5581
p-value	(0)	(0)	(0)
Costs;Orders	-0.2337	-0.2576	-0.0972
p-value	(0)	(0)	(0.0042)
Costs;Dividends	0.3583	0.6438	0.4882
p-value	(0)	(0)	(0)
Costs;Group	0.092	-0.0163	-0.0137
p-value	(0.0565)	(0.7368)	(0.7363)
Costs;Gender	-0.1222	-0.5365	-0.3942
p-value	(0.0112)	(0)	(0)
Orders;Dividends	-0.7781	-0.6756	-0.4233
p-value	(0)	(0)	(0)
Orders;Group	0.2252	0.249	0.2077
p-value	(0)	(0)	(0)
Orders;Gender	0.5042	0.7675	0.5797
p-value	(0)	(0)	(0)
Dividends;Group	-0.1152	-0.1052	-0.0909
p-value	(0.0168)	(0.0292)	(0.0294)
Dividends;Gender	-0.4015	-0.733	-0.5417
p-value	(0)	(0)	(0)
Group;Gender	0.147	0.2702	0.2384
p-value	(0.0022)	(0)	(0)

Figure 1

PNG link

Postscript link

PDF link

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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code