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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 computationThu, 14 Dec 2017 20:31:25 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/14/t1513279999xu6bv3g67j75tkc.htm/, Retrieved Tue, 14 May 2024 15:31:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309581, Retrieved Tue, 14 May 2024 15:31:18 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact47

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Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Kendall tau Correlation Matrix] [normale corr matr...] [2017-12-14 19:31:25] [ec772448347bb766a411d58621b503be] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
10	35	24
12	37	39
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13	39	38
10	33	36
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12	32	41
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6	37	21
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11	34	33
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11	31	32
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12	33	35
10	36	32
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12	37	38
8	39	33
12	34	32
11	34	33
15	42	39
13	41	41
12	34	34
12	38	37
11	33	35
11	34	27
13	35	39
14	37	38
14	42	34
12	38	39
10	29	36
11	27	28
12	42	31
15	41	35
10	29	34
11	34	33
13	36	39
10	39	32
12	33	29
11	34	33
12	33	30
12	40	37
13	42	40
12	38	31
12	38	37
9	27	31
11	35	32
8	23	27
13	32	29
12	24	28
12	24	28
13	43	37
12	38	36
15	42	43
9	25	24
9	30	24
9	33	37
11	35	35
11	36	36
7	27	23
7	29	23
9	23	29
12	31	35
12	37	38
12	28	36
9	34	24
10	33	29
10	29	25
10	36	34
12	31	40
6	25	28
12	28	32
11	43	35
11	39	36
11	26	29
10	25	26
12	34	35
6	23	23
13	36	37
13	37	38
10	32	30
11	28	31
7	16	20
8	26	28
14	41	40
12	36	38
11	39	34
9	34	36
10	37	36
8	30	32
11	41	33
12	40	33
12	43	44
13	35	35
11	40	36
13	41	39
10	28	26
12	28	34
13	39	32
9	25	28
7	27	27
11	29	30
9	35	36
12	36	39
12	30	36
10	26	27
12	30	36
11	34	35
14	39	40
12	33	39
12	35	39
11	32	33
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12	39	38
13	39	33
12	34	39
9	38	37
11	35	37
9	32	22
7	28	21
13	35	40
11	40	33
10	35	38
11	40	41
14	36	39
11	33	32
13	34	39
12	34	33
12	37	34
12	35	35
10	17	26
9	28	30
12	31	31
12	30	34
11	28	33
11	44	38
12	33	37
13	36	42
10	29	27
9	26	26
10	40	37
11	34	28
12	36	39
9	32	36
13	38	37
12	41	39
12	30	32
12	30	32
10	21	18
13	41	37
12	33	26
10	30	32
14	36	42
13	37	38
13	36	33
8	22	29
11	33	36
11	27	34
7	31	27
10	29	34
11	32	34
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10	36	28
12	30	35
12	39	38
10	29	32
12	40	49
10	27	29
10	29	33
12	35	35
12	36	37
12	41	38
12	41	38
12	41	38
12	40	39
8	30	31
14	40	40
9	37	32
7	34	25
12	43	35
6	21	21
13	39	40
6	17	22
7	15	19
6	18	19
12	41	39
11	43	31
9	27	30
12	33	38
13	42	41
8	16	12
10	28	30
12	38	40
9	27	30
10	24	29
6	16	20
10	25	27
7	22	27
9	27	30
12	36	40
10	29	42
14	34	38
13	35	37
13	29	37
10	39	40
11	38	36
13	31	35
14	40	41
7	32	29
12	43	42
13	38	38
13	39	40
8	29	26
12	30	35
11	39	32
11	35	28
12	31	38
8	31	25
10	29	38
12	32	39
15	39	41
12	36	29
12	32	34
10	27	34
13	30	38
12	41	37
11	33	32
12	36	37
15	33	42
13	29	34
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10	31	35
9	39	38
11	32	29
11	31	39
15	40	40
12	35	37
10	27	36
12	35	35
12	29	32
9	27	31
11	36	34
11	32	38
13	40	42
12	32	36
12	34	35
12	35	31
9	31	29
10	30	30
13	41	34
9	31	33
11	23	29
11	33	34
10	36	36
9	30	29
11	36	37
13	39	43
11	33	37
10	33	33
10	34	31
12	34	32
7	20	31
9	24	33
12	34	38
12	31	39
13	34	34
9	35	31
10	27	31
12	34	31
12	36	36
9	35	35
11	31	31
12	40	33
11	33	38
14	34	42
10	32	32
6	25	19
7	25	19
6	19	26
12	23	33
5	18	21
9	28	30
12	34	36
12	30	37
13	29	31
10	42	36
14	40	43
14	36	43
13	38	41
9	28	34
11	25	30
9	33	36
14	43	43
13	36	30
13	32	33
8	29	23
12	36	38
11	35	34
8	32	32
12	32	32
9	32	32
10	31	33
11	20	28
10	28	36
10	30	37
12	39	37
15	45	45
12	30	38
9	28	32
13	39	48
8	28	35
12	38	37
12	37	40
10	33	34
11	34	38
12	31	36
12	31	41
9	29	26
10	28	32
12	42	35
15	38	44
12	35	42
11	25	33
9	29	30
10	33	36
10	31	34
10	28	29
12	36	39
9	31	38
10	29	38
15	44	44
13	37	43
9	30	33
10	19	36
10	28	35
11	25	27
8	25	28
11	35	35
7	20	28
10	36	34
11	37	35
8	27	27
13	39	36
9	21	25
12	37	36
9	26	34
11	40	39
10	27	32
9	26	26
12	27	33
10	31	31
13	37	38
11	34	37
10	37	35
12	27	34
11	29	29
12	34	37
7	23	14
10	40	37
13	34	37
8	29	33
8	30	26
10	31	35
11	30	37
9	25	31
11	31	33
14	39	41
11	29	29
8	34	30
9	25	31
15	30	37
15	39	48
11	31	31
12	28	28
10	39	41
12	34	35
11	36	35
10	31	32
8	32	29
11	33	32
14	41	40
12	30	39
11	32	35

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309581&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309581&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309581&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center






Correlations for all pairs of data series (method=pearson)
snelheidvervangbaarheidoverzichtelijkheid
snelheid10.620.702
vervangbaarheid0.6210.653
overzichtelijkheid0.7020.6531

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & snelheid & vervangbaarheid & overzichtelijkheid \tabularnewline
snelheid & 1 & 0.62 & 0.702 \tabularnewline
vervangbaarheid & 0.62 & 1 & 0.653 \tabularnewline
overzichtelijkheid & 0.702 & 0.653 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309581&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]snelheid[/C][C]vervangbaarheid[/C][C]overzichtelijkheid[/C][/ROW]
[ROW][C]snelheid[/C][C]1[/C][C]0.62[/C][C]0.702[/C][/ROW]
[ROW][C]vervangbaarheid[/C][C]0.62[/C][C]1[/C][C]0.653[/C][/ROW]
[ROW][C]overzichtelijkheid[/C][C]0.702[/C][C]0.653[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309581&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309581&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)
snelheidvervangbaarheidoverzichtelijkheid
snelheid10.620.702
vervangbaarheid0.6210.653
overzichtelijkheid0.7020.6531






Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
snelheid;vervangbaarheid0.62030.59330.4611
p-value(0)(0)(0)
snelheid;overzichtelijkheid0.70230.66090.5261
p-value(0)(0)(0)
vervangbaarheid;overzichtelijkheid0.65290.61930.4657
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
snelheid;vervangbaarheid & 0.6203 & 0.5933 & 0.4611 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
snelheid;overzichtelijkheid & 0.7023 & 0.6609 & 0.5261 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
vervangbaarheid;overzichtelijkheid & 0.6529 & 0.6193 & 0.4657 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309581&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]snelheid;vervangbaarheid[/C][C]0.6203[/C][C]0.5933[/C][C]0.4611[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]snelheid;overzichtelijkheid[/C][C]0.7023[/C][C]0.6609[/C][C]0.5261[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]vervangbaarheid;overzichtelijkheid[/C][C]0.6529[/C][C]0.6193[/C][C]0.4657[/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=309581&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309581&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
snelheid;vervangbaarheid0.62030.59330.4611
p-value(0)(0)(0)
snelheid;overzichtelijkheid0.70230.66090.5261
p-value(0)(0)(0)
vervangbaarheid;overzichtelijkheid0.65290.61930.4657
p-value(0)(0)(0)






Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.01111
0.02111
0.03111
0.04111
0.05111
0.06111
0.07111
0.08111
0.09111
0.1111

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Correlation Tests \tabularnewline
Number of significant by total number of Correlations \tabularnewline
Type I error & Pearson r & Spearman rho & Kendall tau \tabularnewline
0.01 & 1 & 1 & 1 \tabularnewline
0.02 & 1 & 1 & 1 \tabularnewline
0.03 & 1 & 1 & 1 \tabularnewline
0.04 & 1 & 1 & 1 \tabularnewline
0.05 & 1 & 1 & 1 \tabularnewline
0.06 & 1 & 1 & 1 \tabularnewline
0.07 & 1 & 1 & 1 \tabularnewline
0.08 & 1 & 1 & 1 \tabularnewline
0.09 & 1 & 1 & 1 \tabularnewline
0.1 & 1 & 1 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309581&T=3

[TABLE]
[ROW][C]Meta Analysis of Correlation Tests[/C][/ROW]
[ROW][C]Number of significant by total number of Correlations[/C][/ROW]
[ROW][C]Type I error[/C][C]Pearson r[/C][C]Spearman rho[/C][C]Kendall tau[/C][/ROW]
[ROW][C]0.01[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.02[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.03[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.04[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.05[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.06[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.07[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.08[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.09[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[ROW][C]0.1[/C][C]1[/C][C]1[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309581&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309581&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.01111
0.02111
0.03111
0.04111
0.05111
0.06111
0.07111
0.08111
0.09111
0.1111


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
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$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
x <- na.omit(x)
y <- t(na.omit(t(y)))
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
n <- length(y[,1])
print(n)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ',header=TRUE)
for (i in 1:n) {
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
for (j in 1:n) {
r <- cor.test(y[i,],y[j,],method=par1)
a<-table.element(a,round(r$estimate,3))
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
ncorrs <- (n*n -n)/2
mycorrs <- array(0, dim=c(10,3))
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)
for (iii in 1:10) {
iiid100 <- iii / 100
if (rp$p.value < iiid100) mycorrs[iii, 1] = mycorrs[iii, 1] + 1
if (rs$p.value < iiid100) mycorrs[iii, 2] = mycorrs[iii, 2] + 1
if (rk$p.value < iiid100) mycorrs[iii, 3] = mycorrs[iii, 3] + 1
}
}
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Correlation Tests',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Number of significant by total number of Correlations',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Type I error',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)
for (iii in 1:10) {
iiid100 <- iii / 100
a<-table.row.start(a)
a<-table.element(a,round(iiid100,2),header=T)
a<-table.element(a,round(mycorrs[iii,1]/ncorrs,2))
a<-table.element(a,round(mycorrs[iii,2]/ncorrs,2))
a<-table.element(a,round(mycorrs[iii,3]/ncorrs,2))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')