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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, 15 Dec 2015 13:57:12 +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/2015/Dec/15/t1450187835106mg68s77c79t1.htm/, Retrieved Sat, 18 May 2024 06:30:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=286494, Retrieved Sat, 18 May 2024 06:30:27 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact62

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] [] [2015-12-15 13:57:12] [20fcaaf1d4bc4a12bf87c6c50d624c14] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0.4 46.4 392
0.61 45.7 118
0.53 45.3 44
0.53 38.6 158
0.53 37.2 81
0.37 35 374
0.3 34 187
0.19 28.3 993
0.12 24.7 1723
0.2 24.7 287
0.19 24.4 970
0.12 22.7 885
0.53 22.3 200
0.14 21.7 575
0.34 21.6 688
0.69 21.3 48
0.49 21.2 572
0.42 20.8 239
0.48 20.3 244
0.25 18.9 472
0.52 18.8 134
0.19 18.6 633
0.44 18 295
0.24 17.6 906
0.16 17 1045
0.1 16.7 775
0.15 15.9 619
0.05 15.3 901
0.24 15 910
0.22 14.8 556

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'Gertrude Mary Cox' @ cox.wessa.net

\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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286494&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286494&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286494&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'Gertrude Mary Cox' @ cox.wessa.net






Correlations for all pairs of data series (method=pearson)
Prop_Population_on_FarmsHIV_RiskPer_Capita_Income
Prop_Population_on_Farms10.48-0.775
HIV_Risk0.481-0.419
Per_Capita_Income-0.775-0.4191

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & Prop_Population_on_Farms & HIV_Risk & Per_Capita_Income \tabularnewline
Prop_Population_on_Farms & 1 & 0.48 & -0.775 \tabularnewline
HIV_Risk & 0.48 & 1 & -0.419 \tabularnewline
Per_Capita_Income & -0.775 & -0.419 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286494&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]Prop_Population_on_Farms[/C][C]HIV_Risk[/C][C]Per_Capita_Income[/C][/ROW]
[ROW][C]Prop_Population_on_Farms[/C][C]1[/C][C]0.48[/C][C]-0.775[/C][/ROW]
[ROW][C]HIV_Risk[/C][C]0.48[/C][C]1[/C][C]-0.419[/C][/ROW]
[ROW][C]Per_Capita_Income[/C][C]-0.775[/C][C]-0.419[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286494&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286494&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)
Prop_Population_on_FarmsHIV_RiskPer_Capita_Income
Prop_Population_on_Farms10.48-0.775
HIV_Risk0.481-0.419
Per_Capita_Income-0.775-0.4191






Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Prop_Population_on_Farms;HIV_Risk0.48040.42160.3287
p-value(0.0072)(0.0203)(0.0117)
Prop_Population_on_Farms;Per_Capita_Income-0.7755-0.8293-0.652
p-value(0)(0)(0)
HIV_Risk;Per_Capita_Income-0.4191-0.4232-0.29
p-value(0.0212)(0.0198)(0.0246)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
Prop_Population_on_Farms;HIV_Risk & 0.4804 & 0.4216 & 0.3287 \tabularnewline
p-value & (0.0072) & (0.0203) & (0.0117) \tabularnewline
Prop_Population_on_Farms;Per_Capita_Income & -0.7755 & -0.8293 & -0.652 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
HIV_Risk;Per_Capita_Income & -0.4191 & -0.4232 & -0.29 \tabularnewline
p-value & (0.0212) & (0.0198) & (0.0246) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286494&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]Prop_Population_on_Farms;HIV_Risk[/C][C]0.4804[/C][C]0.4216[/C][C]0.3287[/C][/ROW]
[ROW][C]p-value[/C][C](0.0072)[/C][C](0.0203)[/C][C](0.0117)[/C][/ROW]
[ROW][C]Prop_Population_on_Farms;Per_Capita_Income[/C][C]-0.7755[/C][C]-0.8293[/C][C]-0.652[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]HIV_Risk;Per_Capita_Income[/C][C]-0.4191[/C][C]-0.4232[/C][C]-0.29[/C][/ROW]
[ROW][C]p-value[/C][C](0.0212)[/C][C](0.0198)[/C][C](0.0246)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286494&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286494&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
Prop_Population_on_Farms;HIV_Risk0.48040.42160.3287
p-value(0.0072)(0.0203)(0.0117)
Prop_Population_on_Farms;Per_Capita_Income-0.7755-0.8293-0.652
p-value(0)(0)(0)
HIV_Risk;Per_Capita_Income-0.4191-0.4232-0.29
p-value(0.0212)(0.0198)(0.0246)






Meta Analysis of Correlation Tests
Number of significant by total number of Correlations
Type I errorPearson rSpearman rhoKendall tau
0.010.670.330.33
0.020.670.670.67
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 & 0.67 & 0.33 & 0.33 \tabularnewline
0.02 & 0.67 & 0.67 & 0.67 \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=286494&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]0.67[/C][C]0.33[/C][C]0.33[/C][/ROW]
[ROW][C]0.02[/C][C]0.67[/C][C]0.67[/C][C]0.67[/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=286494&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286494&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
0.010.670.330.33
0.020.670.670.67
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):
par1 = 1 ; par2 = 2 ; par3 = FALSE ;
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$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
x <- na.omit(x)
y <- t(na.omit(t(y)))
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
n <- length(y[,1])
n
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ',header=TRUE)
for (i in 1:n) {
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
for (j in 1:n) {
r <- cor.test(y[i,],y[j,],method=par1)
a<-table.element(a,round(r$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')