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Description of Statistical Computation

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 computationTue, 14 Dec 2010 08:55:42 +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/14/t1292316818ofe52mr6gkq2dm8.htm/, Retrieved Thu, 02 May 2024 21:28:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109269, Retrieved Thu, 02 May 2024 21:28:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact175

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] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
- R PD  [Kendall tau Correlation Matrix] [Workshop 10; Pear...] [2010-12-13 21:11:58] [8ffb4cfa64b4677df0d2c448735a40bb]
-    D      [Kendall tau Correlation Matrix] [Workshop 10; Pear...] [2010-12-14 08:55:42] [50e0b5177c9c80b42996aa89930b928a] [Current]
-   P         [Kendall tau Correlation Matrix] [Workshop 10; Pear...] [2010-12-14 09:01:17] [8ffb4cfa64b4677df0d2c448735a40bb]
-               [Kendall tau Correlation Matrix] [Workshop 10; Kend...] [2010-12-14 09:02:23] [8ffb4cfa64b4677df0d2c448735a40bb]
-    D        [Kendall tau Correlation Matrix] [Paper; Pearson Co...] [2010-12-22 08:34:36] [8ffb4cfa64b4677df0d2c448735a40bb]
-   PD          [Kendall tau Correlation Matrix] [Paper; Kendall's ...] [2010-12-22 08:54:08] [8ffb4cfa64b4677df0d2c448735a40bb]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
107.11	236.67	8.92	1
122.23	258.1	9.32	2
134.69	241.52	8.9	3
128.79	190.71	8.53	4
126.16	200.32	8.51	5
119.98	223.41	9.03	6
108.45	201.38	9.6	7
108.43	211.83	9.88	8
98.17	224.41	10.81	9
106.09	211.57	11.61	10
108.81	194.77	11.81	11
103.03	201.86	13.93	12
124.36	225	16.19	1
118.52	278.9	18.05	2
112.2	259.74	17.08	3
114.71	230.45	17.46	4
107.96	238.26	16.9	5
101.21	250.14	15.69	6
102.77	263.81	15.86	7
112.13	247.22	12.98	8
109.36	229.81	12.31	9
110.91	224.27	11.51	10
123.57	213.23	11.73	11
129.95	239.57	11.7	12
124.46	249.7	10.9	1
122.34	212.5	10.57	2
116.61	203.27	10.37	3
114.59	192.05	9.59	4
112.52	190.04	9.09	5
118.67	202.05	9.26	6
116.8	211.91	9.9	7
123.63	210.39	9.61	8
128.04	231.25	9.85	9
134.57	224.3	9.99	10
130.33	209.64	9.9	11
136.47	206.05	10.45	12
139.05	229.7	11.66	1
158.21	264.67	13.61	2
148.07	246.29	12.88	3
137.74	260.91	12.52	4
139.74	265.14	10.93	5
144.08	284.52	12.07	6
145.35	287.48	13.21	7
145.77	321.9	13.68	8
140.56	321.59	14.02	9
121.41	282.39	11.7	10
120.44	241	11.83	11
116.97	228.48	11.32	12
128.03	261.59	12.24	1
128.51	270	13.31	2
127.76	262.86	12.93	3
134.58	277.41	13.47	4
147.64	288	15.47	5
144.46	287.14	16.58	6
137.6	337.65	17.8	7
146.87	328.38	21.72	8
145.67	374.41	23.45	9
151.95	344.77	23.16	10
150.23	361.05	22.77	11
155.86	374.22	24.9	12

Tables (Output of Computation)





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=109269&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=109269&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109269&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Correlations for all pairs of data series (method=pearson)
CoffeeTeaSugarMonth
Coffee10.6580.414-0.014
Tea0.65810.8080.108
Sugar0.4140.80810.168
Month-0.0140.1080.1681

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & Coffee & Tea & Sugar & Month \tabularnewline
Coffee & 1 & 0.658 & 0.414 & -0.014 \tabularnewline
Tea & 0.658 & 1 & 0.808 & 0.108 \tabularnewline
Sugar & 0.414 & 0.808 & 1 & 0.168 \tabularnewline
Month & -0.014 & 0.108 & 0.168 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109269&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]Coffee[/C][C]Tea[/C][C]Sugar[/C][C]Month[/C][/ROW]
[ROW][C]Coffee[/C][C]1[/C][C]0.658[/C][C]0.414[/C][C]-0.014[/C][/ROW]
[ROW][C]Tea[/C][C]0.658[/C][C]1[/C][C]0.808[/C][C]0.108[/C][/ROW]
[ROW][C]Sugar[/C][C]0.414[/C][C]0.808[/C][C]1[/C][C]0.168[/C][/ROW]
[ROW][C]Month[/C][C]-0.014[/C][C]0.108[/C][C]0.168[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109269&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109269&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)
CoffeeTeaSugarMonth
Coffee10.6580.414-0.014
Tea0.65810.8080.108
Sugar0.4140.80810.168
Month-0.0140.1080.1681






Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Coffee;Tea0.65810.59780.4271
p-value(0)(0)(0)
Coffee;Sugar0.41410.31320.2329
p-value(0.001)(0.0148)(0.0086)
Coffee;Month-0.0141-0.0201-0.014
p-value(0.915)(0.879)(0.8778)
Tea;Sugar0.80840.73210.5642
p-value(0)(0)(0)
Tea;Month0.108-0.0449-0.0117
p-value(0.4116)(0.7335)(0.8981)
Sugar;Month0.16750.10180.0796
p-value(0.2008)(0.4391)(0.3838)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
Coffee;Tea & 0.6581 & 0.5978 & 0.4271 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Coffee;Sugar & 0.4141 & 0.3132 & 0.2329 \tabularnewline
p-value & (0.001) & (0.0148) & (0.0086) \tabularnewline
Coffee;Month & -0.0141 & -0.0201 & -0.014 \tabularnewline
p-value & (0.915) & (0.879) & (0.8778) \tabularnewline
Tea;Sugar & 0.8084 & 0.7321 & 0.5642 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Tea;Month & 0.108 & -0.0449 & -0.0117 \tabularnewline
p-value & (0.4116) & (0.7335) & (0.8981) \tabularnewline
Sugar;Month & 0.1675 & 0.1018 & 0.0796 \tabularnewline
p-value & (0.2008) & (0.4391) & (0.3838) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109269&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]Coffee;Tea[/C][C]0.6581[/C][C]0.5978[/C][C]0.4271[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Coffee;Sugar[/C][C]0.4141[/C][C]0.3132[/C][C]0.2329[/C][/ROW]
[ROW][C]p-value[/C][C](0.001)[/C][C](0.0148)[/C][C](0.0086)[/C][/ROW]
[ROW][C]Coffee;Month[/C][C]-0.0141[/C][C]-0.0201[/C][C]-0.014[/C][/ROW]
[ROW][C]p-value[/C][C](0.915)[/C][C](0.879)[/C][C](0.8778)[/C][/ROW]
[ROW][C]Tea;Sugar[/C][C]0.8084[/C][C]0.7321[/C][C]0.5642[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Tea;Month[/C][C]0.108[/C][C]-0.0449[/C][C]-0.0117[/C][/ROW]
[ROW][C]p-value[/C][C](0.4116)[/C][C](0.7335)[/C][C](0.8981)[/C][/ROW]
[ROW][C]Sugar;Month[/C][C]0.1675[/C][C]0.1018[/C][C]0.0796[/C][/ROW]
[ROW][C]p-value[/C][C](0.2008)[/C][C](0.4391)[/C][C](0.3838)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109269&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109269&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
Coffee;Tea0.65810.59780.4271
p-value(0)(0)(0)
Coffee;Sugar0.41410.31320.2329
p-value(0.001)(0.0148)(0.0086)
Coffee;Month-0.0141-0.0201-0.014
p-value(0.915)(0.879)(0.8778)
Tea;Sugar0.80840.73210.5642
p-value(0)(0)(0)
Tea;Month0.108-0.0449-0.0117
p-value(0.4116)(0.7335)(0.8981)
Sugar;Month0.16750.10180.0796
p-value(0.2008)(0.4391)(0.3838)


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')