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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, 21 Dec 2010 11:42:39 +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/21/t129293163591kfaqmwcdumgjk.htm/, Retrieved Sun, 19 May 2024 21:35:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113306, Retrieved Sun, 19 May 2024 21:35:19 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact123

Tree of Dependent Computations

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 11:42:39] [4c854bb223ec27caaa7bcfc5e77b0dbd] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
4321023	29790	444	81767	1	1
4111912	87550	412	153198	1	1
223193	84738	428	-26007	0	1
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283910	3425	9	83620	1	0
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438493	1442	239	121173	1	1
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230621	2126	41	21001	1	1
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276898	592	7	64270	0	0
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317046	621	11	68005	0	1
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
-7170	18073	69	27284	0	1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113306&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]5 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]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113306&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113306&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 time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Correlations for all pairs of data series (method=pearson)
WealthCostsOrdersDividendsGroupGender
Wealth10.6420.6430.3240.2190.076
Costs0.64210.8380.2480.180.154
Orders0.6430.83810.3220.2080.185
Dividends0.3240.2480.32210.1340.056
Group0.2190.180.2080.13410.256
Gender 0.0760.1540.1850.0560.2561

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & Wealth & Costs & Orders & Dividends & Group & Gender
 \tabularnewline
Wealth & 1 & 0.642 & 0.643 & 0.324 & 0.219 & 0.076 \tabularnewline
Costs & 0.642 & 1 & 0.838 & 0.248 & 0.18 & 0.154 \tabularnewline
Orders & 0.643 & 0.838 & 1 & 0.322 & 0.208 & 0.185 \tabularnewline
Dividends & 0.324 & 0.248 & 0.322 & 1 & 0.134 & 0.056 \tabularnewline
Group & 0.219 & 0.18 & 0.208 & 0.134 & 1 & 0.256 \tabularnewline
Gender
 & 0.076 & 0.154 & 0.185 & 0.056 & 0.256 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113306&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]0.642[/C][C]0.643[/C][C]0.324[/C][C]0.219[/C][C]0.076[/C][/ROW]
[ROW][C]Costs[/C][C]0.642[/C][C]1[/C][C]0.838[/C][C]0.248[/C][C]0.18[/C][C]0.154[/C][/ROW]
[ROW][C]Orders[/C][C]0.643[/C][C]0.838[/C][C]1[/C][C]0.322[/C][C]0.208[/C][C]0.185[/C][/ROW]
[ROW][C]Dividends[/C][C]0.324[/C][C]0.248[/C][C]0.322[/C][C]1[/C][C]0.134[/C][C]0.056[/C][/ROW]
[ROW][C]Group[/C][C]0.219[/C][C]0.18[/C][C]0.208[/C][C]0.134[/C][C]1[/C][C]0.256[/C][/ROW]
[ROW][C]Gender
[/C][C]0.076[/C][C]0.154[/C][C]0.185[/C][C]0.056[/C][C]0.256[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113306&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113306&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:

Correlations for all pairs of data series (method=pearson)
WealthCostsOrdersDividendsGroupGender
Wealth10.6420.6430.3240.2190.076
Costs0.64210.8380.2480.180.154
Orders0.6430.83810.3220.2080.185
Dividends0.3240.2480.32210.1340.056
Group0.2190.180.2080.13410.256
Gender 0.0760.1540.1850.0560.2561






Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Wealth;Costs0.64150.25180.1741
p-value(0)(0)(0)
Wealth;Orders0.64340.27680.1956
p-value(0)(0)(0)
Wealth;Dividends0.32390.3240.2421
p-value(0)(0)(0)
Wealth;Group0.21930.17940.1508
p-value(0)(2e-04)(2e-04)
Wealth;Gender 0.07580.07530.0633
p-value(0.1164)(0.1189)(0.1188)
Costs;Orders0.83850.92210.7803
p-value(0)(0)(0)
Costs;Dividends0.24810.40840.3271
p-value(0)(0)(0)
Costs;Group0.18040.18370.1544
p-value(2e-04)(1e-04)(1e-04)
Costs;Gender 0.15360.13570.1141
p-value(0.0014)(0.0048)(0.0049)
Orders;Dividends0.32160.42880.3279
p-value(0)(0)(0)
Orders;Group0.20790.15920.1344
p-value(0)(9e-04)(0.001)
Orders;Gender 0.18470.11150.0942
p-value(1e-04)(0.0207)(0.0209)
Dividends;Group0.13440.16890.1431
p-value(0.0053)(4e-04)(5e-04)
Dividends;Gender 0.05560.05840.0494
p-value(0.2502)(0.227)(0.2266)
Group;Gender 0.25640.25640.2564
p-value(0)(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 & 0.6415 & 0.2518 & 0.1741 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Wealth;Orders & 0.6434 & 0.2768 & 0.1956 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Wealth;Dividends & 0.3239 & 0.324 & 0.2421 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Wealth;Group & 0.2193 & 0.1794 & 0.1508 \tabularnewline
p-value & (0) & (2e-04) & (2e-04) \tabularnewline
Wealth;Gender
 & 0.0758 & 0.0753 & 0.0633 \tabularnewline
p-value & (0.1164) & (0.1189) & (0.1188) \tabularnewline
Costs;Orders & 0.8385 & 0.9221 & 0.7803 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Costs;Dividends & 0.2481 & 0.4084 & 0.3271 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Costs;Group & 0.1804 & 0.1837 & 0.1544 \tabularnewline
p-value & (2e-04) & (1e-04) & (1e-04) \tabularnewline
Costs;Gender
 & 0.1536 & 0.1357 & 0.1141 \tabularnewline
p-value & (0.0014) & (0.0048) & (0.0049) \tabularnewline
Orders;Dividends & 0.3216 & 0.4288 & 0.3279 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Orders;Group & 0.2079 & 0.1592 & 0.1344 \tabularnewline
p-value & (0) & (9e-04) & (0.001) \tabularnewline
Orders;Gender
 & 0.1847 & 0.1115 & 0.0942 \tabularnewline
p-value & (1e-04) & (0.0207) & (0.0209) \tabularnewline
Dividends;Group & 0.1344 & 0.1689 & 0.1431 \tabularnewline
p-value & (0.0053) & (4e-04) & (5e-04) \tabularnewline
Dividends;Gender
 & 0.0556 & 0.0584 & 0.0494 \tabularnewline
p-value & (0.2502) & (0.227) & (0.2266) \tabularnewline
Group;Gender
 & 0.2564 & 0.2564 & 0.2564 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113306&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]0.6415[/C][C]0.2518[/C][C]0.1741[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Wealth;Orders[/C][C]0.6434[/C][C]0.2768[/C][C]0.1956[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Wealth;Dividends[/C][C]0.3239[/C][C]0.324[/C][C]0.2421[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Wealth;Group[/C][C]0.2193[/C][C]0.1794[/C][C]0.1508[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](2e-04)[/C][C](2e-04)[/C][/ROW]
[ROW][C]Wealth;Gender
[/C][C]0.0758[/C][C]0.0753[/C][C]0.0633[/C][/ROW]
[ROW][C]p-value[/C][C](0.1164)[/C][C](0.1189)[/C][C](0.1188)[/C][/ROW]
[ROW][C]Costs;Orders[/C][C]0.8385[/C][C]0.9221[/C][C]0.7803[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Costs;Dividends[/C][C]0.2481[/C][C]0.4084[/C][C]0.3271[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Costs;Group[/C][C]0.1804[/C][C]0.1837[/C][C]0.1544[/C][/ROW]
[ROW][C]p-value[/C][C](2e-04)[/C][C](1e-04)[/C][C](1e-04)[/C][/ROW]
[ROW][C]Costs;Gender
[/C][C]0.1536[/C][C]0.1357[/C][C]0.1141[/C][/ROW]
[ROW][C]p-value[/C][C](0.0014)[/C][C](0.0048)[/C][C](0.0049)[/C][/ROW]
[ROW][C]Orders;Dividends[/C][C]0.3216[/C][C]0.4288[/C][C]0.3279[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Orders;Group[/C][C]0.2079[/C][C]0.1592[/C][C]0.1344[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](9e-04)[/C][C](0.001)[/C][/ROW]
[ROW][C]Orders;Gender
[/C][C]0.1847[/C][C]0.1115[/C][C]0.0942[/C][/ROW]
[ROW][C]p-value[/C][C](1e-04)[/C][C](0.0207)[/C][C](0.0209)[/C][/ROW]
[ROW][C]Dividends;Group[/C][C]0.1344[/C][C]0.1689[/C][C]0.1431[/C][/ROW]
[ROW][C]p-value[/C][C](0.0053)[/C][C](4e-04)[/C][C](5e-04)[/C][/ROW]
[ROW][C]Dividends;Gender
[/C][C]0.0556[/C][C]0.0584[/C][C]0.0494[/C][/ROW]
[ROW][C]p-value[/C][C](0.2502)[/C][C](0.227)[/C][C](0.2266)[/C][/ROW]
[ROW][C]Group;Gender
[/C][C]0.2564[/C][C]0.2564[/C][C]0.2564[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113306&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113306&T=2

As an alternative you can also use a QR Code:  
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
Wealth;Costs0.64150.25180.1741
p-value(0)(0)(0)
Wealth;Orders0.64340.27680.1956
p-value(0)(0)(0)
Wealth;Dividends0.32390.3240.2421
p-value(0)(0)(0)
Wealth;Group0.21930.17940.1508
p-value(0)(2e-04)(2e-04)
Wealth;Gender 0.07580.07530.0633
p-value(0.1164)(0.1189)(0.1188)
Costs;Orders0.83850.92210.7803
p-value(0)(0)(0)
Costs;Dividends0.24810.40840.3271
p-value(0)(0)(0)
Costs;Group0.18040.18370.1544
p-value(2e-04)(1e-04)(1e-04)
Costs;Gender 0.15360.13570.1141
p-value(0.0014)(0.0048)(0.0049)
Orders;Dividends0.32160.42880.3279
p-value(0)(0)(0)
Orders;Group0.20790.15920.1344
p-value(0)(9e-04)(0.001)
Orders;Gender 0.18470.11150.0942
p-value(1e-04)(0.0207)(0.0209)
Dividends;Group0.13440.16890.1431
p-value(0.0053)(4e-04)(5e-04)
Dividends;Gender 0.05560.05840.0494
p-value(0.2502)(0.227)(0.2266)
Group;Gender 0.25640.25640.2564
p-value(0)(0)(0)


Figures (Output of Computation)

Input Parameters & R Code

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')