Free Statistics

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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 17:14:55 +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/t1292346824677i5jiikkx2tty.htm/, Retrieved Thu, 02 May 2024 23:04:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109908, Retrieved Thu, 02 May 2024 23:04:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact174
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]
F R PD    [Kendall tau Correlation Matrix] [] [2010-12-14 17:14:55] [e26438ba7029caa0090c95690001dbf5] [Current]
-    D      [Kendall tau Correlation Matrix] [] [2010-12-21 14:21:43] [8ef75e99f9f5061c72c54640f2f1c3e7]
Feedback Forum
2010-12-22 04:42:32 [f0479c8ad85b1406c7a3120008048c58] [reply
Het is inderdaad zo dat er weinig verband is tussen de verschillende gegevens. De p-waarden zijn significant verschillend van nul, dus er is geen samenhang aanwezig

Post a new message
Dataseries X:
2	73	69
1	58	53
1	68	43
2	69	60
1	62	49
1	68	62
1	65	45
1	65	50
1	81	75
1	73	82
2	64	60
1	68	59
1	51	21
1	68	40
1	61	62
2	77	54
1	69	47
1	73	59
2	61	37
2	62	43
1	63	48
1	69	59
2	47	79
2	66	62
1	58	16
2	63	38
1	69	58
2	59	60
1	59	72
2	63	67
2	65	55
1	65	47
2	71	59
1	60	49
2	66	47
1	67	57
2	81	39
1	62	49
1	63	26
2	73	53
2	55	75
1	59	65
1	64	49
2	63	48
2	74	45
2	67	31
1	64	67
1	73	61
1	54	49
1	76	69
2	74	54
2	63	80
2	73	57
2	67	34
2	68	69
1	66	44
2	62	70
2	71	51
1	68	66
1	63	18
1	75	74
1	77	59
2	62	48
1	74	55
2	67	44
2	56	56
2	60	65
2	58	77
1	65	46
2	49	70
1	61	39
2	66	55
2	64	44
2	65	45
1	46	45
2	81	25
2	65	49
1	72	65
2	65	45
2	74	71
1	69	48
1	59	41
2	58	40
1	71	64
2	79	56
2	68	52
1	66	41
2	62	45
1	69	49
1	60	42
2	63	54
1	62	40
1	61	40
2	65	51
1	64	48
2	67	80
2	56	38
2	56	57
1	48	28
1	74	51
1	69	46
1	62	58
1	73	67
1	64	72
1	57	26
1	57	54
2	60	53
2	61	69
1	72	64
1	57	47
1	51	43
1	63	66
1	54	54
1	72	62
1	62	52
1	68	64
1	62	55
2	63	57
1	77	74
1	57	32
1	57	38
1	61	66
2	66	37
1	65	26
1	63	64
1	59	28
2	66	66
1	68	65
1	72	48
1	68	44
2	68	64
1	67	39
1	59	50
1	56	52
1	62	66
2	55	48
2	72	70
2	68	66
1	67	61
1	54	31
2	69	61
1	61	54
1	55	34
2	75	62
1	55	47
1	49	52
2	54	37
1	51	46
1	66	50
1	73	61
2	63	70
2	61	38
1	74	63
2	81	34
1	58	46
1	62	40
1	64	30
1	62	35
1	85	51
1	74	56
1	51	68
1	66	39
2	61	44
1	72	58




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109908&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]2 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=109908&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109908&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Correlations for all pairs of data series (method=pearson)
gendernonverbalanxiety
gender10.0740.131
nonverbal0.07410.221
anxiety0.1310.2211

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & gender & nonverbal & anxiety \tabularnewline
gender & 1 & 0.074 & 0.131 \tabularnewline
nonverbal & 0.074 & 1 & 0.221 \tabularnewline
anxiety & 0.131 & 0.221 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109908&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]gender[/C][C]nonverbal[/C][C]anxiety[/C][/ROW]
[ROW][C]gender[/C][C]1[/C][C]0.074[/C][C]0.131[/C][/ROW]
[ROW][C]nonverbal[/C][C]0.074[/C][C]1[/C][C]0.221[/C][/ROW]
[ROW][C]anxiety[/C][C]0.131[/C][C]0.221[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109908&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109908&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)
gendernonverbalanxiety
gender10.0740.131
nonverbal0.07410.221
anxiety0.1310.2211







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
gender;nonverbal0.07360.06390.0534
p-value(0.3491)(0.416)(0.4143)
gender;anxiety0.13080.10650.0881
p-value(0.095)(0.1745)(0.1737)
nonverbal;anxiety0.22080.26360.1864
p-value(0.0045)(6e-04)(6e-04)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
gender;nonverbal & 0.0736 & 0.0639 & 0.0534 \tabularnewline
p-value & (0.3491) & (0.416) & (0.4143) \tabularnewline
gender;anxiety & 0.1308 & 0.1065 & 0.0881 \tabularnewline
p-value & (0.095) & (0.1745) & (0.1737) \tabularnewline
nonverbal;anxiety & 0.2208 & 0.2636 & 0.1864 \tabularnewline
p-value & (0.0045) & (6e-04) & (6e-04) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109908&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]gender;nonverbal[/C][C]0.0736[/C][C]0.0639[/C][C]0.0534[/C][/ROW]
[ROW][C]p-value[/C][C](0.3491)[/C][C](0.416)[/C][C](0.4143)[/C][/ROW]
[ROW][C]gender;anxiety[/C][C]0.1308[/C][C]0.1065[/C][C]0.0881[/C][/ROW]
[ROW][C]p-value[/C][C](0.095)[/C][C](0.1745)[/C][C](0.1737)[/C][/ROW]
[ROW][C]nonverbal;anxiety[/C][C]0.2208[/C][C]0.2636[/C][C]0.1864[/C][/ROW]
[ROW][C]p-value[/C][C](0.0045)[/C][C](6e-04)[/C][C](6e-04)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109908&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109908&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
gender;nonverbal0.07360.06390.0534
p-value(0.3491)(0.416)(0.4143)
gender;anxiety0.13080.10650.0881
p-value(0.095)(0.1745)(0.1737)
nonverbal;anxiety0.22080.26360.1864
p-value(0.0045)(6e-04)(6e-04)



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')