Free Statistics

of Irreproducible Research!

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 computationSat, 17 Dec 2011 20:53:03 -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/17/t1324173369coae2w36zzx1dp0.htm/, Retrieved Fri, 29 Mar 2024 12:18:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=156596, Retrieved Fri, 29 Mar 2024 12:18:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact60
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 18:04:16] [b98453cac15ba1066b407e146608df68]
- RMPD    [Kendall tau Correlation Matrix] [] [2011-12-18 01:53:03] [2be7aedefc35278abdba659ba29c8de8] [Current]
Feedback Forum

Post a new message
Dataseries X:
79	30	94	112285	146283	26
58	28	103	84786	98364	51
60	38	93	83123	86146	57
121	25	91	119182	195663	56
43	26	93	116174	95757	37
69	25	60	57635	85584	60
78	38	123	66198	143983	67
44	30	90	57793	59238	43
158	47	168	97668	151511	52
102	30	115	133824	136368	52
77	31	71	101481	112642	43
82	23	66	99645	94728	57
101	36	117	99052	121527	55
80	30	108	67654	127766	84
50	25	84	65553	98958	57
123	34	120	69112	85646	67
73	31	114	82753	98579	49
105	33	120	72654	131741	70
47	25	81	30727	53907	52
94	35	133	117478	146761	44
44	42	122	74007	82036	54
107	33	124	101494	171975	58
33	14	37	31081	58391	68
96	32	120	83122	136815	43
56	35	95	60578	69107	56
59	28	90	79892	108016	74
39	28	80	49810	46341	65
76	34	110	100708	79336	63
91	39	138	82875	93176	58
76	28	100	72260	127969	54
8	4	7	5950	15049	56
79	39	140	115762	155135	57
76	29	96	80670	102996	63
101	44	164	143558	160604	53
123	35	124	105195	174141	63
105	23	62	149193	184301	43
41	29	99	95260	129847	64
72	25	70	55183	117286	60
67	27	104	106671	71180	53
75	36	116	73511	109377	58
114	28	91	92945	85298	49
22	23	67	22618	23824	48
69	28	72	83737	82981	29
105	34	120	69094	73815	54
88	28	105	95536	132190	73
73	34	104	225920	128754	51
62	33	98	61370	67808	58
118	38	111	106117	131722	43
100	35	71	84651	106175	51
24	24	69	15986	25157	53
67	29	107	95364	76669	54
57	29	107	89691	105805	61
135	37	129	126846	72413	48
124	33	118	102860	96971	47
33	25	73	51715	71299	39
98	32	119	55801	77494	48
58	29	104	111813	120336	50
131	31	90	161647	181248	39
110	52	197	115929	146123	44
130	24	85	162901	186646	65
118	33	106	129838	168237	58
39	32	50	37510	64219	42
81	31	63	87771	115338	49
28	23	69	192565	153197	67
83	30	93	140867	68370	43
133	42	110	101338	103950	62
12	1	0	1168	5841	64
106	32	83	65567	84396	57
71	36	98	40735	55515	54
4	0	0	855	6622	48
62	24	60	97068	115814	49
18	8	9	14116	13155	46
98	33	115	76643	142775	61
32	38	120	92696	20112	56
25	24	66	94785	61023	41
100	43	152	93815	132432	43
46	43	139	86687	112494	53
129	41	144	105547	170875	66
136	45	160	213688	214921	58
59	31	114	71220	100226	53
63	31	119	91721	78876	46
14	30	101	111194	6940	52
113	37	133	135777	122037	51
47	30	83	51513	53782	51
92	35	116	74163	127748	45
91	31	97	98952	103300	45
111	31	98	102372	112283	37
41	21	78	37238	10901	59
120	39	117	103772	120691	42
131	39	132	130115	139296	66
47	30	73	64466	89455	53
109	37	86	54990	147866	69
37	32	48	34777	14336	56




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156596&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156596&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156596&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'Gwilym Jenkins' @ jenkins.wessa.net







Correlations for all pairs of data series (method=kendall)
blogged_computationscompendiums_reviewedfeedback_messages_p120totsizetotsecondsInt.Mot.
blogged_computations10.4210.4220.4270.520.019
compendiums_reviewed0.42110.6370.2240.2770.019
feedback_messages_p1200.4220.63710.3180.3430.056
totsize0.4270.2240.31810.493-0.115
totseconds0.520.2770.3430.49310.081
Int.Mot.0.0190.0190.056-0.1150.0811

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=kendall) \tabularnewline
  & blogged_computations & compendiums_reviewed & feedback_messages_p120 & totsize & totseconds & Int.Mot. \tabularnewline
blogged_computations & 1 & 0.421 & 0.422 & 0.427 & 0.52 & 0.019 \tabularnewline
compendiums_reviewed & 0.421 & 1 & 0.637 & 0.224 & 0.277 & 0.019 \tabularnewline
feedback_messages_p120 & 0.422 & 0.637 & 1 & 0.318 & 0.343 & 0.056 \tabularnewline
totsize & 0.427 & 0.224 & 0.318 & 1 & 0.493 & -0.115 \tabularnewline
totseconds & 0.52 & 0.277 & 0.343 & 0.493 & 1 & 0.081 \tabularnewline
Int.Mot. & 0.019 & 0.019 & 0.056 & -0.115 & 0.081 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156596&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=kendall)[/C][/ROW]
[ROW][C] [/C][C]blogged_computations[/C][C]compendiums_reviewed[/C][C]feedback_messages_p120[/C][C]totsize[/C][C]totseconds[/C][C]Int.Mot.[/C][/ROW]
[ROW][C]blogged_computations[/C][C]1[/C][C]0.421[/C][C]0.422[/C][C]0.427[/C][C]0.52[/C][C]0.019[/C][/ROW]
[ROW][C]compendiums_reviewed[/C][C]0.421[/C][C]1[/C][C]0.637[/C][C]0.224[/C][C]0.277[/C][C]0.019[/C][/ROW]
[ROW][C]feedback_messages_p120[/C][C]0.422[/C][C]0.637[/C][C]1[/C][C]0.318[/C][C]0.343[/C][C]0.056[/C][/ROW]
[ROW][C]totsize[/C][C]0.427[/C][C]0.224[/C][C]0.318[/C][C]1[/C][C]0.493[/C][C]-0.115[/C][/ROW]
[ROW][C]totseconds[/C][C]0.52[/C][C]0.277[/C][C]0.343[/C][C]0.493[/C][C]1[/C][C]0.081[/C][/ROW]
[ROW][C]Int.Mot.[/C][C]0.019[/C][C]0.019[/C][C]0.056[/C][C]-0.115[/C][C]0.081[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156596&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156596&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=kendall)
blogged_computationscompendiums_reviewedfeedback_messages_p120totsizetotsecondsInt.Mot.
blogged_computations10.4210.4220.4270.520.019
compendiums_reviewed0.42110.6370.2240.2770.019
feedback_messages_p1200.4220.63710.3180.3430.056
totsize0.4270.2240.31810.493-0.115
totseconds0.520.2770.3430.49310.081
Int.Mot.0.0190.0190.056-0.1150.0811







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
blogged_computations;compendiums_reviewed0.60670.55040.4207
p-value(0)(0)(0)
blogged_computations;feedback_messages_p1200.62660.56240.4219
p-value(0)(0)(0)
blogged_computations;totsize0.54270.57230.4265
p-value(0)(0)(0)
blogged_computations;totseconds0.72440.68160.5199
p-value(0)(0)(0)
blogged_computations;Int.Mot.0.0320.02950.019
p-value(0.7611)(0.7788)(0.7903)
compendiums_reviewed;feedback_messages_p1200.8810.79220.6371
p-value(0)(0)(0)
compendiums_reviewed;totsize0.45640.30890.2241
p-value(0)(0.0026)(0.0018)
compendiums_reviewed;totseconds0.49110.37630.2766
p-value(0)(2e-04)(1e-04)
compendiums_reviewed;Int.Mot.-0.01520.02950.0194
p-value(0.8853)(0.7789)(0.79)
feedback_messages_p120;totsize0.51370.44460.3182
p-value(0)(0)(0)
feedback_messages_p120;totseconds0.55050.470.3426
p-value(0)(0)(0)
feedback_messages_p120;Int.Mot.0.04160.08870.0555
p-value(0.6924)(0.398)(0.4387)
totsize;totseconds0.69670.6530.4932
p-value(0)(0)(0)
totsize;Int.Mot.-0.1012-0.172-0.1153
p-value(0.3343)(0.0992)(0.1063)
totseconds;Int.Mot.0.09360.12640.0811
p-value(0.3722)(0.2273)(0.2558)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
blogged_computations;compendiums_reviewed & 0.6067 & 0.5504 & 0.4207 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
blogged_computations;feedback_messages_p120 & 0.6266 & 0.5624 & 0.4219 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
blogged_computations;totsize & 0.5427 & 0.5723 & 0.4265 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
blogged_computations;totseconds & 0.7244 & 0.6816 & 0.5199 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
blogged_computations;Int.Mot. & 0.032 & 0.0295 & 0.019 \tabularnewline
p-value & (0.7611) & (0.7788) & (0.7903) \tabularnewline
compendiums_reviewed;feedback_messages_p120 & 0.881 & 0.7922 & 0.6371 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
compendiums_reviewed;totsize & 0.4564 & 0.3089 & 0.2241 \tabularnewline
p-value & (0) & (0.0026) & (0.0018) \tabularnewline
compendiums_reviewed;totseconds & 0.4911 & 0.3763 & 0.2766 \tabularnewline
p-value & (0) & (2e-04) & (1e-04) \tabularnewline
compendiums_reviewed;Int.Mot. & -0.0152 & 0.0295 & 0.0194 \tabularnewline
p-value & (0.8853) & (0.7789) & (0.79) \tabularnewline
feedback_messages_p120;totsize & 0.5137 & 0.4446 & 0.3182 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
feedback_messages_p120;totseconds & 0.5505 & 0.47 & 0.3426 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
feedback_messages_p120;Int.Mot. & 0.0416 & 0.0887 & 0.0555 \tabularnewline
p-value & (0.6924) & (0.398) & (0.4387) \tabularnewline
totsize;totseconds & 0.6967 & 0.653 & 0.4932 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
totsize;Int.Mot. & -0.1012 & -0.172 & -0.1153 \tabularnewline
p-value & (0.3343) & (0.0992) & (0.1063) \tabularnewline
totseconds;Int.Mot. & 0.0936 & 0.1264 & 0.0811 \tabularnewline
p-value & (0.3722) & (0.2273) & (0.2558) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156596&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]blogged_computations;compendiums_reviewed[/C][C]0.6067[/C][C]0.5504[/C][C]0.4207[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]blogged_computations;feedback_messages_p120[/C][C]0.6266[/C][C]0.5624[/C][C]0.4219[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]blogged_computations;totsize[/C][C]0.5427[/C][C]0.5723[/C][C]0.4265[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]blogged_computations;totseconds[/C][C]0.7244[/C][C]0.6816[/C][C]0.5199[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]blogged_computations;Int.Mot.[/C][C]0.032[/C][C]0.0295[/C][C]0.019[/C][/ROW]
[ROW][C]p-value[/C][C](0.7611)[/C][C](0.7788)[/C][C](0.7903)[/C][/ROW]
[ROW][C]compendiums_reviewed;feedback_messages_p120[/C][C]0.881[/C][C]0.7922[/C][C]0.6371[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]compendiums_reviewed;totsize[/C][C]0.4564[/C][C]0.3089[/C][C]0.2241[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0.0026)[/C][C](0.0018)[/C][/ROW]
[ROW][C]compendiums_reviewed;totseconds[/C][C]0.4911[/C][C]0.3763[/C][C]0.2766[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](2e-04)[/C][C](1e-04)[/C][/ROW]
[ROW][C]compendiums_reviewed;Int.Mot.[/C][C]-0.0152[/C][C]0.0295[/C][C]0.0194[/C][/ROW]
[ROW][C]p-value[/C][C](0.8853)[/C][C](0.7789)[/C][C](0.79)[/C][/ROW]
[ROW][C]feedback_messages_p120;totsize[/C][C]0.5137[/C][C]0.4446[/C][C]0.3182[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]feedback_messages_p120;totseconds[/C][C]0.5505[/C][C]0.47[/C][C]0.3426[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]feedback_messages_p120;Int.Mot.[/C][C]0.0416[/C][C]0.0887[/C][C]0.0555[/C][/ROW]
[ROW][C]p-value[/C][C](0.6924)[/C][C](0.398)[/C][C](0.4387)[/C][/ROW]
[ROW][C]totsize;totseconds[/C][C]0.6967[/C][C]0.653[/C][C]0.4932[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]totsize;Int.Mot.[/C][C]-0.1012[/C][C]-0.172[/C][C]-0.1153[/C][/ROW]
[ROW][C]p-value[/C][C](0.3343)[/C][C](0.0992)[/C][C](0.1063)[/C][/ROW]
[ROW][C]totseconds;Int.Mot.[/C][C]0.0936[/C][C]0.1264[/C][C]0.0811[/C][/ROW]
[ROW][C]p-value[/C][C](0.3722)[/C][C](0.2273)[/C][C](0.2558)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156596&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156596&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
blogged_computations;compendiums_reviewed0.60670.55040.4207
p-value(0)(0)(0)
blogged_computations;feedback_messages_p1200.62660.56240.4219
p-value(0)(0)(0)
blogged_computations;totsize0.54270.57230.4265
p-value(0)(0)(0)
blogged_computations;totseconds0.72440.68160.5199
p-value(0)(0)(0)
blogged_computations;Int.Mot.0.0320.02950.019
p-value(0.7611)(0.7788)(0.7903)
compendiums_reviewed;feedback_messages_p1200.8810.79220.6371
p-value(0)(0)(0)
compendiums_reviewed;totsize0.45640.30890.2241
p-value(0)(0.0026)(0.0018)
compendiums_reviewed;totseconds0.49110.37630.2766
p-value(0)(2e-04)(1e-04)
compendiums_reviewed;Int.Mot.-0.01520.02950.0194
p-value(0.8853)(0.7789)(0.79)
feedback_messages_p120;totsize0.51370.44460.3182
p-value(0)(0)(0)
feedback_messages_p120;totseconds0.55050.470.3426
p-value(0)(0)(0)
feedback_messages_p120;Int.Mot.0.04160.08870.0555
p-value(0.6924)(0.398)(0.4387)
totsize;totseconds0.69670.6530.4932
p-value(0)(0)(0)
totsize;Int.Mot.-0.1012-0.172-0.1153
p-value(0.3343)(0.0992)(0.1063)
totseconds;Int.Mot.0.09360.12640.0811
p-value(0.3722)(0.2273)(0.2558)



Parameters (Session):
Parameters (R input):
par1 = kendall ;
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')