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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 computationMon, 13 Dec 2010 21:28:45 +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/t1292275654guyflfmmwpht69k.htm/, Retrieved Tue, 07 May 2024 01:22:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109212, Retrieved Tue, 07 May 2024 01:22:58 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact137
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]
-   PD      [Kendall tau Correlation Matrix] [Workshop 10; Kend...] [2010-12-13 21:28:45] [50e0b5177c9c80b42996aa89930b928a] [Current]
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Dataseries X:
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	13
118.52	278.9	18.05	14
112.2	259.74	17.08	15
114.71	230.45	17.46	16
107.96	238.26	16.9	17
101.21	250.14	15.69	18
102.77	263.81	15.86	19
112.13	247.22	12.98	20
109.36	229.81	12.31	21
110.91	224.27	11.51	22
123.57	213.23	11.73	23
129.95	239.57	11.7	24
124.46	249.7	10.9	25
122.34	212.5	10.57	26
116.61	203.27	10.37	27
114.59	192.05	9.59	28
112.52	190.04	9.09	29
118.67	202.05	9.26	30
116.8	211.91	9.9	31
123.63	210.39	9.61	32
128.04	231.25	9.85	33
134.57	224.3	9.99	34
130.33	209.64	9.9	35
136.47	206.05	10.45	36
139.05	229.7	11.66	37
158.21	264.67	13.61	38
148.07	246.29	12.88	39
137.74	260.91	12.52	40
139.74	265.14	10.93	41
144.08	284.52	12.07	42
145.35	287.48	13.21	43
145.77	321.9	13.68	44
140.56	321.59	14.02	45
121.41	282.39	11.7	46
120.44	241	11.83	47
116.97	228.48	11.32	48
128.03	261.59	12.24	49
128.51	270	13.31	50
127.76	262.86	12.93	51
134.58	277.41	13.47	52
147.64	288	15.47	53
144.46	287.14	16.58	54
137.6	337.65	17.8	55
146.87	328.38	21.72	56
145.67	374.41	23.45	57
151.95	344.77	23.16	58
150.23	361.05	22.77	59
155.86	374.22	24.9	60




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109212&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109212&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109212&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'George Udny Yule' @ 72.249.76.132







Correlations for all pairs of data series (method=kendall)
CoffeeTeaSugarMonth
Coffee10.4270.2330.531
Tea0.42710.5640.476
Sugar0.2330.56410.391
Month0.5310.4760.3911

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & Coffee & Tea & Sugar & Month \tabularnewline
Coffee & 1 & 0.427 & 0.233 & 0.531 \tabularnewline
Tea & 0.427 & 1 & 0.564 & 0.476 \tabularnewline
Sugar & 0.233 & 0.564 & 1 & 0.391 \tabularnewline
Month & 0.531 & 0.476 & 0.391 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109212&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/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]0.427[/C][C]0.233[/C][C]0.531[/C][/ROW]
[ROW][C]Tea[/C][C]0.427[/C][C]1[/C][C]0.564[/C][C]0.476[/C][/ROW]
[ROW][C]Sugar[/C][C]0.233[/C][C]0.564[/C][C]1[/C][C]0.391[/C][/ROW]
[ROW][C]Month[/C][C]0.531[/C][C]0.476[/C][C]0.391[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109212&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109212&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=kendall)
CoffeeTeaSugarMonth
Coffee10.4270.2330.531
Tea0.42710.5640.476
Sugar0.2330.56410.391
Month0.5310.4760.3911







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;Month0.71350.70820.5311
p-value(0)(0)(0)
Tea;Sugar0.80840.73210.5642
p-value(0)(0)(0)
Tea;Month0.6870.65190.4757
p-value(0)(0)(0)
Sugar;Month0.51750.51780.3912
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
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 & 0.7135 & 0.7082 & 0.5311 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Tea;Sugar & 0.8084 & 0.7321 & 0.5642 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Tea;Month & 0.687 & 0.6519 & 0.4757 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Sugar;Month & 0.5175 & 0.5178 & 0.3912 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109212&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]0.7135[/C][C]0.7082[/C][C]0.5311[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/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]0.687[/C][C]0.6519[/C][C]0.4757[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Sugar;Month[/C][C]0.5175[/C][C]0.5178[/C][C]0.3912[/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=109212&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109212&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
Coffee;Tea0.65810.59780.4271
p-value(0)(0)(0)
Coffee;Sugar0.41410.31320.2329
p-value(0.001)(0.0148)(0.0086)
Coffee;Month0.71350.70820.5311
p-value(0)(0)(0)
Tea;Sugar0.80840.73210.5642
p-value(0)(0)(0)
Tea;Month0.6870.65190.4757
p-value(0)(0)(0)
Sugar;Month0.51750.51780.3912
p-value(0)(0)(0)



Parameters (Session):
par1 = kendall ;
Parameters (R input):
par1 = kendall ;
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')