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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 computationTue, 13 Dec 2011 15:15:26 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/13/t1323807341ncoqm7npa95w746.htm/, Retrieved Thu, 02 May 2024 20:11:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154690, Retrieved Thu, 02 May 2024 20:11:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact89
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Kendall tau Correlation Matrix] [] [2011-12-13 20:15:26] [6140f0163e532fc168d2f211324acd0a] [Current]
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Dataseries X:
1	Female	21	210907	80
1	Female	23	120982	128
1	Male	22	176508	123
1	NA	NA	179321	0
1	NA	NA	123185	0
1	NA	NA	52746	0
1	Female	22	385534	125
1	NA	NA	33170	0
0	NA	NA	101645	0
1	Female	21	149061	96
1	Male	22	165446	138
1	Male	21	237213	142
1	NA	NA	173326	0
1	Male	21	133131	101
1	NA	NA	258873	0
1	NA	NA	180083	0
1	Male	21	324799	126
1	Female	21	230964	133
1	Male	23	236785	106
1	Female	21	135473	121
1	NA	NA	202925	0
1	Female	21	215147	114
1	Male	22	344297	172
1	Female	22	153935	115
1	NA	NA	132943	0
1	Male	25+	174724	113
1	Male	21	174415	121
1	Female	22	225548	126
1	Male	23	223632	139
1	Male	22	124817	138
1	NA	NA	221698	0
1	Female	22	210767	123
1	Female	21	170266	127
1	NA	NA	260561	0
1	NA	NA	84853	0
1	Female	21	294424	135
0	NA	NA	101011	0
1	NA	NA	215641	0
1	Male	21	325107	113
0	Male	20	7176	131
1	NA	NA	167542	0
1	Female	21	106408	147
0	Female	25	96560	145
1	Male	21	265769	113
1	NA	NA	269651	0
1	Female	21	149112	130
0	Male	20	175824	123
1	Female	24	152871	161
1	Male	23	111665	140
1	NA	NA	116408	0
1	Male	21	362301	140
1	NA	NA	78800	0
1	Female	24	183167	134
1	NA	NA	277965	0
1	NA	NA	150629	0
1	Male	21	168809	124
1	Male	23	24188	128
1	Male	23	329267	133
1	NA	NA	65029	0
1	NA	NA	101097	0
1	Male	21	218946	140
1	Male	22	244052	127
0	Male	20	341570	132
0	Female	18	103597	122
1	NA	NA	233328	0
1	Male	22	256462	141
1	NA	NA	206161	0
1	NA	NA	311473	0
1	Female	21	235800	110
1	NA	NA	177939	0
1	NA	NA	207176	0
1	Male	21	196553	135
1	Male	23	174184	139
1	Female	22	143246	133
1	Male	21	187559	129
1	Female	21	187681	122
1	NA	NA	119016	0
1	NA	NA	182192	0
1	Male	21	73566	122
1	NA	NA	194979	0
1	Female	22	167488	106
1	Female	21	143756	126
1	NA	NA	275541	0
1	Female	21	243199	158
1	Male	23	182999	132
1	NA	NA	135649	0
1	Male	21	152299	137
1	NA	NA	120221	0
1	Male	25+	346485	117
1	NA	NA	145790	0
1	Male	22	193339	130
1	NA	NA	80953	0
1	Male	22	122774	120
1	Female	20	130585	128
0	Male	21	112611	129
1	Male	21	286468	126
1	NA	NA	241066	0
1	Male	21	148446	117
1	NA	NA	204713	0
1	Female	21	182079	127
1	Male	22	140344	95
1	Male	21	220516	111
1	Male	24	243060	122
1	Male	22	162765	111
1	NA	NA	182613	0
1	Male	24	232138	115
1	Female	21	265318	100
0	Male	22	85574	98
1	Female	22	310839	126
1	Female	21	225060	135
1	Male	24	232317	130
1	Female	21	144966	126
1	NA	NA	43287	0
1	NA	NA	155754	0
1	Male	22	164709	115
1	NA	NA	201940	0
1	NA	NA	235454	0
0	Male	19	220801	134
1	Female	22	99466	147
0	Male	23	92661	114
0	Male	20	133328	135
0	Male	20	61361	121
0	NA	NA	125930	0
1	Male	23	100750	107
1	NA	NA	224549	0
0	NA	NA	82316	0
0	Female	20	102010	142
0	Male	20	101523	144
1	Male	23	243511	129
1	Male	25	22938	145
0	NA	NA	41566	0
1	Male	21	152474	131
1	NA	NA	61857	0
0	Female	22	99923	123
1	Male	21	132487	132
1	Female	22	317394	119
1	Male	22	21054	94
1	Male	23	209641	111
0	Female	21	22648	112
1	Female	21	31414	127
0	Male	20	46698	123
0	Male	19	131698	115
0	NA	NA	91735	0
1	Male	22	244749	129
1	NA	NA	184510	0
0	NA	NA	79863	0
1	Female	21	128423	131
1	Female	21	97839	131
1	NA	NA	38214	0
1	NA	NA	151101	0
1	Male	21	272458	109
1	NA	21	172494	123
0	Male	21	108043	115
1	Female	21	328107	147
1	NA	NA	250579	0
1	Male	22	351067	123
1	Female	22	158015	117
0	NA	NA	98866	0
1	NA	NA	85439	0
1	Male	22	229242	117
1	NA	NA	351619	0
1	Male	22	84207	117
0	Female	18	120445	105
1	Female	21	324598	119
1	Female	23	131069	119
1	Female	21	204271	121
1	NA	NA	165543	0
1	NA	NA	141722	0
0	Female	19	116048	122
0	Male	19	250047	126
1	Male	23	299775	118
1	Female	21	195838	119
1	Male	21	173260	118
1	Female	21	254488	116
1	NA	NA	104389	0
0	NA	NA	136084	0
1	NA	NA	199476	0
0	Male	21	92499	102
1	Female	20	224330	136
0	Female	19	135781	106
0	Male	21	74408	127
0	Female	22	81240	121
1	NA	NA	14688	0
1	Male	21	181633	128
1	Male	25	271856	144
1	NA	NA	7199	0
1	NA	NA	46660	0
1	NA	NA	17547	0
0	NA	NA	133368	0
1	Male	23	95227	122
1	NA	NA	152601	0
0	Female	19	98146	119
0	NA	NA	79619	0
0	Female	19	59194	132
0	Male	19	139942	122
0	Female	19	118612	125
0	Male	19	72880	134
0	Male	20	65475	136
0	NA	NA	99643	0
0	Male	19	71965	114
0	NA	NA	77272	0
0	NA	NA	49289	0
0	Female	19	135131	102
0	Female	19	108446	109
0	NA	NA	89746	0
0	NA	NA	44296	0
0	NA	NA	77648	0
0	Male	19	181528	129
0	Male	19	134019	130
0	NA	NA	124064	0
0	NA	NA	92630	0
0	Female	20	121848	145
0	NA	NA	52915	0
0	Female	19	81872	118
0	Female	19	58981	131
0	Female	18	53515	131
0	NA	NA	60812	0
0	Male	19	56375	122
0	Male	19	65490	147
0	NA	NA	80949	0
0	Male	21	76302	110
0	Male	18	104011	143
0	Female	18	98104	111
0	NA	NA	67989	0
0	Male	21	30989	96
0	Female	20	135458	132
0	NA	NA	73504	0
0	Male	19	63123	113
0	NA	NA	61254	0
0	Male	21	74914	138
0	Female	21	31774	142
0	Male	20	81437	131
0	NA	NA	87186	0
0	NA	NA	50090	0
0	Male	24	65745	134
0	Male	22	56653	110
0	Male	21	158399	138
0	NA	NA	46455	0
0	Male	21	73624	132
0	NA	NA	38395	0
0	Male	19	91899	122
0	Male	18	139526	134
0	NA	NA	52164	0
0	Female	19	51567	145
0	NA	NA	70551	0
0	NA	NA	84856	0
0	Female	19	102538	146
0	Male	19	86678	99
0	NA	NA	85709	0
0	NA	NA	34662	0
0	Male	20	150580	137
0	Male	18	99611	123
0	NA	NA	19349	0
0	Female	19	99373	117
0	Female	19	86230	124
0	Female	20	30837	126
0	Male	20	31706	142
0	Male	21	89806	119
0	NA	NA	62088	0
0	NA	NA	40151	0
0	NA	NA	27634	0
0	NA	NA	76990	0
0	NA	NA	37460	0
0	NA	NA	54157	0
0	NA	NA	49862	0
0	NA	NA	84337	0
0	Female	20	64175	127
0	Female	21	59382	131
0	Female	18	119308	122
0	Female	19	76702	115
0	NA	NA	103425	0
0	NA	NA	70344	0
0	NA	NA	43410	0
0	NA	NA	104838	0
0	NA	NA	62215	0
0	NA	NA	69304	0
0	NA	NA	53117	0
0	Male	19	19764	103
0	NA	NA	86680	0
0	Female	19	84105	136
0	NA	NA	77945	0
0	NA	NA	89113	0
0	NA	NA	91005	0
0	NA	NA	40248	0
0	Male	19	64187	131
0	NA	NA	50857	0
0	NA	NA	56613	0
0	NA	NA	62792	0
0	Male	19	72535	131




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'George Udny Yule' @ yule.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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154690&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154690&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154690&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 time1 seconds
R Server'George Udny Yule' @ yule.wessa.net



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')