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Author's title

Author*Unverified author*
R Software Modulerwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationSun, 18 Dec 2011 03:21:48 -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/18/t1324196532b13jwwbcz15x3gm.htm/, Retrieved Sat, 04 May 2024 20:17:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=156606, Retrieved Sat, 04 May 2024 20:17:57 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact119
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]
- RMPD  [Kendall tau Correlation Matrix] [] [2011-12-12 09:41:19] [86f7284edee3dbb8ea5c7e2dec87d892]
-    D    [Kendall tau Correlation Matrix] [] [2011-12-17 14:08:52] [74be16979710d4c4e7c6647856088456]
-    D        [Kendall tau Correlation Matrix] [] [2011-12-18 08:21:48] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
0	210907	146283
0	120982	98364
1	176508	86146
0	385534	195663
0	149061	95757
1	165446	85584
1	237213	143983
1	133131	59238
1	324799	151511
0	230964	136368
1	236785	112642
0	135473	94728
0	215147	121527
1	344297	127766
0	153935	98958
1	174724	85646
1	174415	98579
0	225548	130767
1	223632	131741
1	124817	53907
0	210767	146761
0	170266	82036
0	294424	171975
1	325107	159676
1	7176	1929
0	106408	58391
0	96560	31580
1	265769	136815
0	149112	69107
1	175824	50495
0	152871	108016
1	111665	46341
1	362301	79336
0	183167	93176
1	168809	127969
1	24188	15049
1	329267	155135
1	218946	102996
1	244052	160604
1	341570	158051
0	103597	44547
1	256462	174141
0	235800	184301
1	196553	129847
1	174184	117286
0	143246	71180
1	187559	109377
0	187681	85298
1	73566	23824
0	167488	82981
0	143756	73815
0	243199	132190
1	182999	128754
1	152299	67808
1	346485	131722
1	193339	106175
1	122774	25157
0	130585	76669
1	112611	57283
1	286468	105805
1	148446	72413
0	182079	96971
1	140344	71299
1	220516	77494
1	243060	120336
1	162765	93913
1	232138	181248
0	265318	146123
1	85574	32036
0	310839	186646
0	225060	102255
1	232317	168237
0	144966	64219
1	164709	115338
1	220801	84845
0	99466	153197
1	92661	29877
1	133328	63506
1	61361	22445
1	100750	68370
0	102010	42071
1	101523	50517
1	243511	103950
1	22938	5841
1	152474	84396
0	99923	35753
1	132487	55515
0	317394	209056
1	21054	6622
1	209641	115814
0	22648	11609
0	31414	13155
1	46698	18274
1	131698	72875
1	244749	142775
0	128423	20112
0	97839	61023
1	272458	132432
1	108043	45109
0	328107	170875
1	351067	214921
0	158015	100226
1	229242	78876
1	84207	6940
0	120445	49025
0	324598	122037
0	131069	53782
0	204271	127748
0	116048	77395
1	250047	89324
1	299775	103300
0	195838	112283
1	173260	10901
0	254488	120691
1	92499	25899
0	224330	139296
0	135781	52678
1	74408	23853
0	81240	17306
1	181633	89455
1	271856	147866
1	95227	14336
0	98146	30059
0	59194	22097
1	139942	96841
0	118612	41907
1	72880	27080
1	65475	35885
1	71965	28313
0	135131	36134
0	108446	55764
1	181528	66956
1	134019	47487
0	121848	35619
0	81872	45608
0	58981	7721
0	53515	20634
1	56375	31931
1	65490	37754
1	76302	40557
1	104011	94238
0	98104	44197
1	30989	4103
0	135458	44144
1	63123	27640
1	74914	28990
0	31774	4694
1	81437	42648
1	65745	25836
1	56653	22779
1	158399	40820
1	73624	32378
1	91899	39613
1	139526	60865
0	51567	20107
0	102538	48231
1	86678	39725
1	150580	62991
1	99611	49363
0	99373	24552
0	86230	31493
0	30837	3439
1	31706	19555
1	89806	21228
0	64175	28893
0	59382	21425
0	119308	50276
0	76702	37643
1	19764	9927
0	84105	27184
1	64187	18475
1	72535	35873




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'AstonUniversity' @ aston.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 & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156606&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]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156606&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156606&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'AstonUniversity' @ aston.wessa.net







Correlations for all pairs of data series (method=pearson)
GenderTotal_Time_spent_in_RFC_in_secondsCompendium_Writing:total_number_of_seconds
Gender10.043-0.033
Total_Time_spent_in_RFC_in_seconds0.04310.874
Compendium_Writing:total_number_of_seconds-0.0330.8741

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & Gender & Total_Time_spent_in_RFC_in_seconds & Compendium_Writing:total_number_of_seconds \tabularnewline
Gender & 1 & 0.043 & -0.033 \tabularnewline
Total_Time_spent_in_RFC_in_seconds & 0.043 & 1 & 0.874 \tabularnewline
Compendium_Writing:total_number_of_seconds & -0.033 & 0.874 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156606&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]Gender[/C][C]Total_Time_spent_in_RFC_in_seconds[/C][C]Compendium_Writing:total_number_of_seconds[/C][/ROW]
[ROW][C]Gender[/C][C]1[/C][C]0.043[/C][C]-0.033[/C][/ROW]
[ROW][C]Total_Time_spent_in_RFC_in_seconds[/C][C]0.043[/C][C]1[/C][C]0.874[/C][/ROW]
[ROW][C]Compendium_Writing:total_number_of_seconds[/C][C]-0.033[/C][C]0.874[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156606&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156606&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)
GenderTotal_Time_spent_in_RFC_in_secondsCompendium_Writing:total_number_of_seconds
Gender10.043-0.033
Total_Time_spent_in_RFC_in_seconds0.04310.874
Compendium_Writing:total_number_of_seconds-0.0330.8741







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Gender;Total_Time_spent_in_RFC_in_seconds0.04260.0320.0262
p-value(0.5793)(0.6765)(0.6752)
Gender;Compendium_Writing:total_number_of_seconds-0.0327-0.0237-0.0194
p-value(0.6699)(0.7573)(0.7563)
Total_Time_spent_in_RFC_in_seconds;Compendium_Writing:total_number_of_seconds0.87430.90310.7428
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
Gender;Total_Time_spent_in_RFC_in_seconds & 0.0426 & 0.032 & 0.0262 \tabularnewline
p-value & (0.5793) & (0.6765) & (0.6752) \tabularnewline
Gender;Compendium_Writing:total_number_of_seconds & -0.0327 & -0.0237 & -0.0194 \tabularnewline
p-value & (0.6699) & (0.7573) & (0.7563) \tabularnewline
Total_Time_spent_in_RFC_in_seconds;Compendium_Writing:total_number_of_seconds & 0.8743 & 0.9031 & 0.7428 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156606&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;Total_Time_spent_in_RFC_in_seconds[/C][C]0.0426[/C][C]0.032[/C][C]0.0262[/C][/ROW]
[ROW][C]p-value[/C][C](0.5793)[/C][C](0.6765)[/C][C](0.6752)[/C][/ROW]
[ROW][C]Gender;Compendium_Writing:total_number_of_seconds[/C][C]-0.0327[/C][C]-0.0237[/C][C]-0.0194[/C][/ROW]
[ROW][C]p-value[/C][C](0.6699)[/C][C](0.7573)[/C][C](0.7563)[/C][/ROW]
[ROW][C]Total_Time_spent_in_RFC_in_seconds;Compendium_Writing:total_number_of_seconds[/C][C]0.8743[/C][C]0.9031[/C][C]0.7428[/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=156606&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156606&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;Total_Time_spent_in_RFC_in_seconds0.04260.0320.0262
p-value(0.5793)(0.6765)(0.6752)
Gender;Compendium_Writing:total_number_of_seconds-0.0327-0.0237-0.0194
p-value(0.6699)(0.7573)(0.7563)
Total_Time_spent_in_RFC_in_seconds;Compendium_Writing:total_number_of_seconds0.87430.90310.7428
p-value(0)(0)(0)



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')