Free Statistics

of Irreproducible Research!

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 computationSun, 12 Dec 2010 11:24:34 +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/12/t1292153137eofsq28tsbromh4.htm/, Retrieved Tue, 07 May 2024 12:16:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108381, Retrieved Tue, 07 May 2024 12:16:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact120
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]
-   PD    [Kendall tau Correlation Matrix] [] [2010-12-12 11:24:34] [6fde1c772c7be11768d9b6a0344566b2] [Current]
Feedback Forum

Post a new message
Dataseries X:
24	14	11	24	5	3
25	11	7	25	4	2
17	6	17	30	4	4
18	12	10	19	4	2
18	8	12	22	2	4
16	10	12	22	5	2
20	10	11	25	5	3
16	11	11	23	4	4
18	16	12	17	3	1
17	11	13	21	4	2
23	13	14	19	4	2
30	12	16	19	NA	2
23	8	11	15	3	2
18	12	10	16	3	1
15	11	11	23	4	2
12	4	15	27	5	4
21	9	9	22	4	2
15	8	11	14	2	2
20	8	17	22	4	3
31	14	17	23	4	2
27	15	11	23	4	3
34	16	18	21	4	2
21	9	14	19	4	3
31	14	10	18	4	2
19	11	11	20	5	3
16	8	15	23	4	3
20	9	15	25	4	4
21	9	13	19	4	2
22	9	16	24	4	4
17	9	13	22	4	3
24	10	9	25	4	4
25	16	18	26	4	3
26	11	18	29	2	4
25	8	12	32	4	4
17	9	17	25	4	3
32	16	9	29	5	5
33	11	9	28	5	4
13	16	12	17	4	2
32	12	18	28	3	4
25	12	12	29	4	4
29	14	18	26	5	5
22	9	14	25	4	4
18	10	15	14	4	2
17	9	16	25	5	4
20	10	10	26	4	4
15	12	11	20	5	1
20	14	14	18	4	2
33	14	9	32	4	5
29	10	12	25	4	4
23	14	17	25	3	2
26	16	5	23	4	3
18	9	12	21	4	4
20	10	12	20	4	2
11	6	6	15	2	1
28	8	24	30	3	4
26	13	12	24	5	2
22	10	12	26	4	4
17	8	14	24	4	2
12	7	7	22	5	3
14	15	13	14	3	2
17	9	12	24	5	3
21	10	13	24	4	2
19	12	14	24	5	2
18	13	8	24	4	3
10	10	11	19	4	1
29	11	9	31	5	5
31	8	11	22	4	4
19	9	13	27	4	4
9	13	10	19	5	1
20	11	11	25	4	2
28	8	12	20	3	2
19	9	9	21	4	2
30	9	15	27	5	4
29	15	18	23	5	2
26	9	15	25	5	3
23	10	12	20	4	2
13	14	13	21	4	2
21	12	14	22	5	2
19	12	10	23	5	4
28	11	13	25	4	3
23	14	13	25	5	3
18	6	11	17	3	2
21	12	13	19	4	2
20	8	16	25	4	3
23	14	8	19	4	2
21	11	16	20	4	2
21	10	11	26	4	3
15	14	9	23	5	4
28	12	16	27	4	4
19	10	12	17	4	2
26	14	14	17	4	2
10	5	8	19	5	2
16	11	9	17	3	2
22	10	15	22	3	3
19	9	11	21	5	2
31	10	21	32	5	5
31	16	14	21	4	2
29	13	18	21	4	4
19	9	12	18	4	3
22	10	13	18	4	3
23	10	15	23	4	3
15	7	12	19	4	2
20	9	19	20	4	3
18	8	15	21	4	2
23	14	11	20	5	2
25	14	11	17	2	1
21	8	10	18	4	2
24	9	13	19	2	2
25	14	15	22	4	3
17	14	12	15	5	2
13	8	12	14	3	2
28	8	16	18	4	2
21	8	9	24	3	2
25	7	18	35	4	5
9	6	8	29	4	4
16	8	13	21	4	3
19	6	17	25	2	4
17	11	9	20	1	2
25	14	15	22	4	2
20	11	8	13	3	1
29	11	7	26	4	4
14	11	12	17	3	2
22	14	14	25	3	4
15	8	6	20	5	1
19	20	8	19	4	2
20	11	17	21	3	3
15	8	10	22	4	2
20	11	11	24	4	3
18	10	14	21	4	2
33	14	11	26	4	4
22	11	13	24	3	3
16	9	12	16	4	2
17	9	11	23	4	4
16	8	9	18	4	3
21	10	12	16	4	2
26	13	20	26	4	4
18	13	12	19	3	1
18	12	13	21	4	2
17	8	12	21	4	1
22	13	12	22	2	2
30	14	9	23	2	3
30	12	15	29	4	4
24	14	24	21	2	4
21	15	7	21	4	4
21	13	17	23	3	3
29	16	11	27	4	4
31	9	17	25	3	4
20	9	11	21	2	3
16	9	12	10	2	1
22	8	14	20	4	2
20	7	11	26	3	4
28	16	16	24	4	3
38	11	21	29	4	5
22	9	14	19	2	3
20	11	20	24	5	4
17	9	13	19	4	2
28	14	11	24	4	4
22	13	15	22	4	3
31	16	19	17	3	3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108381&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]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108381&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108381&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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Correlations for all pairs of data series (method=pearson)
CMDPEPSOrganizationGoals
CM10.3950.3290.4250.0170.425
D0.39510.027-0.0320.071-0.048
PE0.3290.02710.244-0.0610.28
PS0.425-0.0320.24410.2760.751
Organization0.0170.071-0.0610.27610.151
Goals0.425-0.0480.280.7510.1511

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & CM & D & PE & PS & Organization & Goals \tabularnewline
CM & 1 & 0.395 & 0.329 & 0.425 & 0.017 & 0.425 \tabularnewline
D & 0.395 & 1 & 0.027 & -0.032 & 0.071 & -0.048 \tabularnewline
PE & 0.329 & 0.027 & 1 & 0.244 & -0.061 & 0.28 \tabularnewline
PS & 0.425 & -0.032 & 0.244 & 1 & 0.276 & 0.751 \tabularnewline
Organization & 0.017 & 0.071 & -0.061 & 0.276 & 1 & 0.151 \tabularnewline
Goals & 0.425 & -0.048 & 0.28 & 0.751 & 0.151 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108381&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]CM[/C][C]D[/C][C]PE[/C][C]PS[/C][C]Organization[/C][C]Goals[/C][/ROW]
[ROW][C]CM[/C][C]1[/C][C]0.395[/C][C]0.329[/C][C]0.425[/C][C]0.017[/C][C]0.425[/C][/ROW]
[ROW][C]D[/C][C]0.395[/C][C]1[/C][C]0.027[/C][C]-0.032[/C][C]0.071[/C][C]-0.048[/C][/ROW]
[ROW][C]PE[/C][C]0.329[/C][C]0.027[/C][C]1[/C][C]0.244[/C][C]-0.061[/C][C]0.28[/C][/ROW]
[ROW][C]PS[/C][C]0.425[/C][C]-0.032[/C][C]0.244[/C][C]1[/C][C]0.276[/C][C]0.751[/C][/ROW]
[ROW][C]Organization[/C][C]0.017[/C][C]0.071[/C][C]-0.061[/C][C]0.276[/C][C]1[/C][C]0.151[/C][/ROW]
[ROW][C]Goals[/C][C]0.425[/C][C]-0.048[/C][C]0.28[/C][C]0.751[/C][C]0.151[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108381&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108381&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)
CMDPEPSOrganizationGoals
CM10.3950.3290.4250.0170.425
D0.39510.027-0.0320.071-0.048
PE0.3290.02710.244-0.0610.28
PS0.425-0.0320.24410.2760.751
Organization0.0170.071-0.0610.27610.151
Goals0.425-0.0480.280.7510.1511







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
CM;D0.39530.39490.3021
p-value(0)(0)(0)
CM;PE0.32880.28420.2144
p-value(0)(3e-04)(2e-04)
CM;PS0.4250.40770.3013
p-value(0)(0)(0)
CM;Organization0.0169-0.0132-0.011
p-value(0.8333)(0.8688)(0.8607)
CM;Goals0.42510.38760.3104
p-value(0)(0)(0)
D;PE0.02720.02130.0124
p-value(0.7332)(0.7897)(0.8314)
D;PS-0.03240.00270.002
p-value(0.6847)(0.9734)(0.9724)
D;Organization0.07150.08260.0673
p-value(0.3721)(0.302)(0.2965)
D;Goals-0.0482-0.0407-0.0315
p-value(0.5463)(0.6105)(0.6166)
PE;PS0.24360.19870.1512
p-value(0.002)(0.0121)(0.0084)
PE;Organization-0.0607-0.0666-0.0536
p-value(0.4486)(0.4059)(0.4017)
PE;Goals0.28010.22010.1855
p-value(3e-04)(0.0053)(0.0029)
PS;Organization0.27560.26030.2097
p-value(5e-04)(0.001)(9e-04)
PS;Goals0.75090.74270.622
p-value(0)(0)(0)
Organization;Goals0.15060.13820.121
p-value(0.0589)(0.0834)(0.0785)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
CM;D & 0.3953 & 0.3949 & 0.3021 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
CM;PE & 0.3288 & 0.2842 & 0.2144 \tabularnewline
p-value & (0) & (3e-04) & (2e-04) \tabularnewline
CM;PS & 0.425 & 0.4077 & 0.3013 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
CM;Organization & 0.0169 & -0.0132 & -0.011 \tabularnewline
p-value & (0.8333) & (0.8688) & (0.8607) \tabularnewline
CM;Goals & 0.4251 & 0.3876 & 0.3104 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
D;PE & 0.0272 & 0.0213 & 0.0124 \tabularnewline
p-value & (0.7332) & (0.7897) & (0.8314) \tabularnewline
D;PS & -0.0324 & 0.0027 & 0.002 \tabularnewline
p-value & (0.6847) & (0.9734) & (0.9724) \tabularnewline
D;Organization & 0.0715 & 0.0826 & 0.0673 \tabularnewline
p-value & (0.3721) & (0.302) & (0.2965) \tabularnewline
D;Goals & -0.0482 & -0.0407 & -0.0315 \tabularnewline
p-value & (0.5463) & (0.6105) & (0.6166) \tabularnewline
PE;PS & 0.2436 & 0.1987 & 0.1512 \tabularnewline
p-value & (0.002) & (0.0121) & (0.0084) \tabularnewline
PE;Organization & -0.0607 & -0.0666 & -0.0536 \tabularnewline
p-value & (0.4486) & (0.4059) & (0.4017) \tabularnewline
PE;Goals & 0.2801 & 0.2201 & 0.1855 \tabularnewline
p-value & (3e-04) & (0.0053) & (0.0029) \tabularnewline
PS;Organization & 0.2756 & 0.2603 & 0.2097 \tabularnewline
p-value & (5e-04) & (0.001) & (9e-04) \tabularnewline
PS;Goals & 0.7509 & 0.7427 & 0.622 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Organization;Goals & 0.1506 & 0.1382 & 0.121 \tabularnewline
p-value & (0.0589) & (0.0834) & (0.0785) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108381&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]CM;D[/C][C]0.3953[/C][C]0.3949[/C][C]0.3021[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]CM;PE[/C][C]0.3288[/C][C]0.2842[/C][C]0.2144[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](3e-04)[/C][C](2e-04)[/C][/ROW]
[ROW][C]CM;PS[/C][C]0.425[/C][C]0.4077[/C][C]0.3013[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]CM;Organization[/C][C]0.0169[/C][C]-0.0132[/C][C]-0.011[/C][/ROW]
[ROW][C]p-value[/C][C](0.8333)[/C][C](0.8688)[/C][C](0.8607)[/C][/ROW]
[ROW][C]CM;Goals[/C][C]0.4251[/C][C]0.3876[/C][C]0.3104[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]D;PE[/C][C]0.0272[/C][C]0.0213[/C][C]0.0124[/C][/ROW]
[ROW][C]p-value[/C][C](0.7332)[/C][C](0.7897)[/C][C](0.8314)[/C][/ROW]
[ROW][C]D;PS[/C][C]-0.0324[/C][C]0.0027[/C][C]0.002[/C][/ROW]
[ROW][C]p-value[/C][C](0.6847)[/C][C](0.9734)[/C][C](0.9724)[/C][/ROW]
[ROW][C]D;Organization[/C][C]0.0715[/C][C]0.0826[/C][C]0.0673[/C][/ROW]
[ROW][C]p-value[/C][C](0.3721)[/C][C](0.302)[/C][C](0.2965)[/C][/ROW]
[ROW][C]D;Goals[/C][C]-0.0482[/C][C]-0.0407[/C][C]-0.0315[/C][/ROW]
[ROW][C]p-value[/C][C](0.5463)[/C][C](0.6105)[/C][C](0.6166)[/C][/ROW]
[ROW][C]PE;PS[/C][C]0.2436[/C][C]0.1987[/C][C]0.1512[/C][/ROW]
[ROW][C]p-value[/C][C](0.002)[/C][C](0.0121)[/C][C](0.0084)[/C][/ROW]
[ROW][C]PE;Organization[/C][C]-0.0607[/C][C]-0.0666[/C][C]-0.0536[/C][/ROW]
[ROW][C]p-value[/C][C](0.4486)[/C][C](0.4059)[/C][C](0.4017)[/C][/ROW]
[ROW][C]PE;Goals[/C][C]0.2801[/C][C]0.2201[/C][C]0.1855[/C][/ROW]
[ROW][C]p-value[/C][C](3e-04)[/C][C](0.0053)[/C][C](0.0029)[/C][/ROW]
[ROW][C]PS;Organization[/C][C]0.2756[/C][C]0.2603[/C][C]0.2097[/C][/ROW]
[ROW][C]p-value[/C][C](5e-04)[/C][C](0.001)[/C][C](9e-04)[/C][/ROW]
[ROW][C]PS;Goals[/C][C]0.7509[/C][C]0.7427[/C][C]0.622[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Organization;Goals[/C][C]0.1506[/C][C]0.1382[/C][C]0.121[/C][/ROW]
[ROW][C]p-value[/C][C](0.0589)[/C][C](0.0834)[/C][C](0.0785)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108381&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108381&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
CM;D0.39530.39490.3021
p-value(0)(0)(0)
CM;PE0.32880.28420.2144
p-value(0)(3e-04)(2e-04)
CM;PS0.4250.40770.3013
p-value(0)(0)(0)
CM;Organization0.0169-0.0132-0.011
p-value(0.8333)(0.8688)(0.8607)
CM;Goals0.42510.38760.3104
p-value(0)(0)(0)
D;PE0.02720.02130.0124
p-value(0.7332)(0.7897)(0.8314)
D;PS-0.03240.00270.002
p-value(0.6847)(0.9734)(0.9724)
D;Organization0.07150.08260.0673
p-value(0.3721)(0.302)(0.2965)
D;Goals-0.0482-0.0407-0.0315
p-value(0.5463)(0.6105)(0.6166)
PE;PS0.24360.19870.1512
p-value(0.002)(0.0121)(0.0084)
PE;Organization-0.0607-0.0666-0.0536
p-value(0.4486)(0.4059)(0.4017)
PE;Goals0.28010.22010.1855
p-value(3e-04)(0.0053)(0.0029)
PS;Organization0.27560.26030.2097
p-value(5e-04)(0.001)(9e-04)
PS;Goals0.75090.74270.622
p-value(0)(0)(0)
Organization;Goals0.15060.13820.121
p-value(0.0589)(0.0834)(0.0785)



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')