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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 computationFri, 10 Dec 2010 20:48:36 +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/10/t12920140009bo596t332gggj6.htm/, Retrieved Mon, 29 Apr 2024 10:52:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=107934, Retrieved Mon, 29 Apr 2024 10:52:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact66
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [Correlation Matri...] [2010-12-10 11:13:07] [1429a1a14191a86916b95357f6de790b]
F    D    [Kendall tau Correlation Matrix] [Correlation Matri...] [2010-12-10 20:48:36] [e192c8164fa91adb027f71579ac0a49a] [Current]
-    D      [Kendall tau Correlation Matrix] [] [2011-12-13 18:15:40] [e21b9c93af4eb9605ecfaf58a559e5ab]
- RM        [Kendall tau Correlation Matrix] [] [2011-12-13 20:16:21] [74be16979710d4c4e7c6647856088456]
Feedback Forum
2010-12-19 10:37:14 [48eb36e2c01435ad7e4ea7854a9d98fe] [reply
De hiernavolgende feedback heeft betrekking op deze (samenvattende) correlatiematrix, maar ook op de drie voorgaande correlatiematrices.

De student heeft hier een zeer uitgebreide analyse gemaakt van alle gegevensreeksen die hij of zij wenst te analyseren. Op die manier krijgt de lezer een duidelijk beeld van alle verbanden. Echter zijn er enkele opmerkingen:

1) De correlatiecoefficienten zijn - in de meeste gevallen - niet zeer groot. Als we weten dat een coefficient van 0 wijst op de afwezigheid van correlatie, is het zeer belangrijk te gaan onderzoeken of de correlatiecoefficienten wel significant verschillen van 0. Hiervoor kunnen we de P waarden (opgenomen in de correlatiematrix bekijken).
2) Echter om deze P waarden te mogen interpreteren, moeten alle variabelen normaal verdeeld zijn. Ook dit is te zien in de correlatiematrix, meer bepaald in de histogrammen. We zien dat dit hier niet het geval is, dit betekent eigenlijk dat we een ander type correlatie - bijvoorbeeld Kendall Tau - moeten gebruiken. Dit laatste kan door een kleine aanpassing in de software, maar ontbreekt bij de student.

Post a new message
Dataseries X:
73	62	66
58	54	54
68	41	82
62	49	61
65	49	65
81	72	77
73	78	66
64	58	66
68	58	66
51	23	48
68	39	57
61	63	80
69	46	60
73	58	70
61	39	85
62	44	59
63	49	72
69	57	70
47	76	74
66	63	70
58	18	51
63	40	70
69	59	71
59	62	72
59	70	50
63	65	69
65	56	73
65	45	66
71	57	73
60	50	58
81	40	78
67	58	83
66	49	76
62	49	77
63	27	79
73	51	71
55	75	79
59	65	60
64	47	73
63	49	70
64	65	42
73	61	74
54	46	68
76	69	83
74	55	62
63	78	79
73	58	61
67	34	86
68	67	64
66	45	75
62	68	59
71	49	82
63	19	61
75	72	69
77	59	60
62	46	59
74	56	81
67	45	65
56	53	60
60	67	60
58	73	45
65	46	75
49	70	84
61	38	77
66	54	64
64	46	54
65	46	72
46	45	56
65	47	67
81	25	81
72	63	73
65	46	67
74	69	72
59	43	69
69	49	71
58	39	77
71	65	63
79	54	49
68	50	74
66	42	76
62	45	65
69	50	65
63	55	69
62	38	71
61	40	68
65	51	49
64	49	86
56	39	63
56	57	77
48	30	52
74	51	73
69	48	63
62	56	54
73	66	56
64	72	54
57	28	61
57	52	70
60	53	68
61	70	63
72	63	76
57	46	69
51	45	71
63	68	39
54	54	54
72	60	64
62	50	70
68	66	76
62	56	71
63	54	73
77	72	81
57	34	50
57	39	42
61	66	66
65	27	77
63	63	62
66	65	66
68	63	69
72	49	72
68	42	67
59	51	59
56	50	66
62	64	68
72	68	72
68	66	73
67	59	69
54	32	57
69	62	55
61	52	72
55	34	68
75	63	83
55	48	74
49	53	72
54	39	66
66	51	61
73	60	86
63	70	81
61	40	79
74	61	73
81	35	59
62	39	64
64	31	75
62	36	68
85	51	84
74	55	68
51	67	68
66	40	69




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107934&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107934&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107934&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'George Udny Yule' @ 72.249.76.132







Correlations for all pairs of data series (method=pearson)
Non-verbaleAnxietyGroupsfeeling
Non-verbale10.1980.254
Anxiety0.19810.056
Groupsfeeling0.2540.0561

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & Non-verbale & Anxiety & Groupsfeeling \tabularnewline
Non-verbale & 1 & 0.198 & 0.254 \tabularnewline
Anxiety & 0.198 & 1 & 0.056 \tabularnewline
Groupsfeeling & 0.254 & 0.056 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107934&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]Non-verbale[/C][C]Anxiety[/C][C]Groupsfeeling[/C][/ROW]
[ROW][C]Non-verbale[/C][C]1[/C][C]0.198[/C][C]0.254[/C][/ROW]
[ROW][C]Anxiety[/C][C]0.198[/C][C]1[/C][C]0.056[/C][/ROW]
[ROW][C]Groupsfeeling[/C][C]0.254[/C][C]0.056[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107934&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107934&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)
Non-verbaleAnxietyGroupsfeeling
Non-verbale10.1980.254
Anxiety0.19810.056
Groupsfeeling0.2540.0561







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Non-verbale;Anxiety0.19820.23960.1743
p-value(0.0165)(0.0036)(0.0025)
Non-verbale;Groupsfeeling0.25410.24580.1735
p-value(0.002)(0.0028)(0.0026)
Anxiety;Groupsfeeling0.05620.04670.0298
p-value(0.5001)(0.5757)(0.6029)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
Non-verbale;Anxiety & 0.1982 & 0.2396 & 0.1743 \tabularnewline
p-value & (0.0165) & (0.0036) & (0.0025) \tabularnewline
Non-verbale;Groupsfeeling & 0.2541 & 0.2458 & 0.1735 \tabularnewline
p-value & (0.002) & (0.0028) & (0.0026) \tabularnewline
Anxiety;Groupsfeeling & 0.0562 & 0.0467 & 0.0298 \tabularnewline
p-value & (0.5001) & (0.5757) & (0.6029) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107934&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]Non-verbale;Anxiety[/C][C]0.1982[/C][C]0.2396[/C][C]0.1743[/C][/ROW]
[ROW][C]p-value[/C][C](0.0165)[/C][C](0.0036)[/C][C](0.0025)[/C][/ROW]
[ROW][C]Non-verbale;Groupsfeeling[/C][C]0.2541[/C][C]0.2458[/C][C]0.1735[/C][/ROW]
[ROW][C]p-value[/C][C](0.002)[/C][C](0.0028)[/C][C](0.0026)[/C][/ROW]
[ROW][C]Anxiety;Groupsfeeling[/C][C]0.0562[/C][C]0.0467[/C][C]0.0298[/C][/ROW]
[ROW][C]p-value[/C][C](0.5001)[/C][C](0.5757)[/C][C](0.6029)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107934&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107934&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
Non-verbale;Anxiety0.19820.23960.1743
p-value(0.0165)(0.0036)(0.0025)
Non-verbale;Groupsfeeling0.25410.24580.1735
p-value(0.002)(0.0028)(0.0026)
Anxiety;Groupsfeeling0.05620.04670.0298
p-value(0.5001)(0.5757)(0.6029)



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')