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Author*The author of this computation has been verified*
R Software Modulerwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationFri, 23 Dec 2011 05:44:27 -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/23/t1324637080oo0yrjptpkaosud.htm/, Retrieved Mon, 29 Apr 2024 20:13:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160260, Retrieved Mon, 29 Apr 2024 20:13:19 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact95
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2011-12-09 10:21:31] [14511500b645ce5186c706473940fe45]
-   PD  [Multiple Regression] [test] [2011-12-23 10:07:12] [8501ca4b76170905b8a207a77f626994]
- RMP       [Kendall tau Correlation Matrix] [Paper: Pearson Co...] [2011-12-23 10:44:27] [3e64eea457df40fcb7af8f28e1ee6256] [Current]
- R           [Kendall tau Correlation Matrix] [Paper: Kendall ta...] [2011-12-23 10:49:49] [f722e8e78b9e5c5ebaa2263f273aa636]
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Dataseries X:
210907	56	79	94	0	2
179321	89	108	103	0	4
149061	44	43	93	0	0
237213	84	78	123	0	0
173326	88	86	148	0	-4
133131	55	44	90	0	4
258873	60	104	124	0	4
324799	154	158	168	0	0
230964	53	102	115	0	-1
236785	119	77	71	0	0
344297	75	80	108	0	1
174724	92	123	120	0	0
174415	100	73	114	0	3
223632	73	105	120	0	-1
294424	77	107	124	0	4
325107	99	84	126	0	3
106408	30	33	37	0	1
96560	76	42	38	1	0
265769	146	96	120	0	-2
269651	67	106	93	0	-3
149112	56	56	95	0	-4
152871	58	59	90	0	2
362301	119	76	110	0	2
183167	66	91	138	0	-4
277965	89	115	133	0	3
218946	41	76	96	0	2
244052	68	101	164	0	2
341570	168	94	78	1	0
233328	132	92	102	0	5
206161	71	75	99	0	-2
311473	112	128	129	0	0
207176	70	56	114	0	-2
196553	57	41	99	0	-3
143246	103	67	104	0	2
182192	52	77	138	0	2
194979	62	66	151	0	2
167488	45	69	72	0	0
143756	46	105	120	0	4
275541	63	116	115	0	4
152299	53	62	98	0	2
193339	78	100	71	0	2
130585	46	67	107	0	-4
112611	41	46	73	1	3
148446	91	135	129	0	3
182079	63	124	118	0	2
243060	63	58	104	0	-1
162765	32	68	107	0	-3
85574	34	37	36	1	0
225060	93	93	139	0	1
133328	55	56	56	1	-3
100750	72	83	93	0	3
101523	42	59	87	1	0
243511	71	133	110	0	0
152474	65	106	83	0	0
132487	41	71	98	0	3
317394	86	116	82	0	-3
244749	95	98	115	0	0
184510	49	64	140	0	-4
128423	64	32	120	0	2
97839	38	25	66	0	-1
172494	52	46	139	0	3
229242	247	63	119	0	2
351619	139	95	141	0	5
324598	110	113	133	0	2
195838	67	111	98	0	-2
254488	83	120	117	0	0
199476	70	87	105	0	3
92499	32	25	55	1	-2
224330	83	131	132	0	0
181633	70	47	73	0	6
271856	103	109	86	0	-3
95227	34	37	48	0	3
98146	40	15	48	1	0
118612	46	54	43	1	-2
65475	18	16	46	1	1
108446	60	22	65	1	0
121848	39	37	52	1	2
76302	31	29	68	1	2
98104	54	55	47	1	-3
30989	14	5	41	1	-2
31774	23	0	47	1	1
150580	77	27	71	1	-4
54157	19	37	30	1	0
59382	49	29	24	1	1
84105	20	17	63	1	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160260&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160260&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160260&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'Gertrude Mary Cox' @ cox.wessa.net







Correlations for all pairs of data series (method=pearson)
timeloginsBCLFMCourseTotaal
time10.6720.7230.654-0.5760.111
logins0.67210.5230.469-0.3540.133
BC0.7230.52310.68-0.6150.111
LFM0.6540.4690.681-0.7170.143
Course-0.576-0.354-0.615-0.7171-0.188
Totaal0.1110.1330.1110.143-0.1881

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & time & logins & BC & LFM & Course & Totaal \tabularnewline
time & 1 & 0.672 & 0.723 & 0.654 & -0.576 & 0.111 \tabularnewline
logins & 0.672 & 1 & 0.523 & 0.469 & -0.354 & 0.133 \tabularnewline
BC & 0.723 & 0.523 & 1 & 0.68 & -0.615 & 0.111 \tabularnewline
LFM & 0.654 & 0.469 & 0.68 & 1 & -0.717 & 0.143 \tabularnewline
Course & -0.576 & -0.354 & -0.615 & -0.717 & 1 & -0.188 \tabularnewline
Totaal & 0.111 & 0.133 & 0.111 & 0.143 & -0.188 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160260&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]time[/C][C]logins[/C][C]BC[/C][C]LFM[/C][C]Course[/C][C]Totaal[/C][/ROW]
[ROW][C]time[/C][C]1[/C][C]0.672[/C][C]0.723[/C][C]0.654[/C][C]-0.576[/C][C]0.111[/C][/ROW]
[ROW][C]logins[/C][C]0.672[/C][C]1[/C][C]0.523[/C][C]0.469[/C][C]-0.354[/C][C]0.133[/C][/ROW]
[ROW][C]BC[/C][C]0.723[/C][C]0.523[/C][C]1[/C][C]0.68[/C][C]-0.615[/C][C]0.111[/C][/ROW]
[ROW][C]LFM[/C][C]0.654[/C][C]0.469[/C][C]0.68[/C][C]1[/C][C]-0.717[/C][C]0.143[/C][/ROW]
[ROW][C]Course[/C][C]-0.576[/C][C]-0.354[/C][C]-0.615[/C][C]-0.717[/C][C]1[/C][C]-0.188[/C][/ROW]
[ROW][C]Totaal[/C][C]0.111[/C][C]0.133[/C][C]0.111[/C][C]0.143[/C][C]-0.188[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160260&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160260&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)
timeloginsBCLFMCourseTotaal
time10.6720.7230.654-0.5760.111
logins0.67210.5230.469-0.3540.133
BC0.7230.52310.68-0.6150.111
LFM0.6540.4690.681-0.7170.143
Course-0.576-0.354-0.615-0.7171-0.188
Totaal0.1110.1330.1110.143-0.1881







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
time;logins0.67230.74510.5677
p-value(0)(0)(0)
time;BC0.72320.74880.5631
p-value(0)(0)(0)
time;LFM0.65360.65970.4831
p-value(0)(0)(0)
time;Course-0.5757-0.6138-0.5041
p-value(0)(0)(0)
time;Totaal0.11080.08340.062
p-value(0.3129)(0.4478)(0.4262)
logins;BC0.52320.66560.4705
p-value(0)(0)(0)
logins;LFM0.46940.54450.3863
p-value(0)(0)(0)
logins;Course-0.3535-0.4669-0.3849
p-value(9e-04)(0)(0)
logins;Totaal0.13280.11220.0812
p-value(0.2257)(0.3067)(0.2992)
BC;LFM0.67960.64920.4669
p-value(0)(0)(0)
BC;Course-0.6155-0.6161-0.5073
p-value(0)(0)(0)
BC;Totaal0.11080.12640.0946
p-value(0.3125)(0.2492)(0.2258)
LFM;Course-0.7165-0.6807-0.5617
p-value(0)(0)(0)
LFM;Totaal0.14310.18380.1271
p-value(0.1914)(0.0922)(0.1046)
Course;Totaal-0.188-0.2042-0.1772
p-value(0.0849)(0.0609)(0.0613)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
time;logins & 0.6723 & 0.7451 & 0.5677 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
time;BC & 0.7232 & 0.7488 & 0.5631 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
time;LFM & 0.6536 & 0.6597 & 0.4831 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
time;Course & -0.5757 & -0.6138 & -0.5041 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
time;Totaal & 0.1108 & 0.0834 & 0.062 \tabularnewline
p-value & (0.3129) & (0.4478) & (0.4262) \tabularnewline
logins;BC & 0.5232 & 0.6656 & 0.4705 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
logins;LFM & 0.4694 & 0.5445 & 0.3863 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
logins;Course & -0.3535 & -0.4669 & -0.3849 \tabularnewline
p-value & (9e-04) & (0) & (0) \tabularnewline
logins;Totaal & 0.1328 & 0.1122 & 0.0812 \tabularnewline
p-value & (0.2257) & (0.3067) & (0.2992) \tabularnewline
BC;LFM & 0.6796 & 0.6492 & 0.4669 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
BC;Course & -0.6155 & -0.6161 & -0.5073 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
BC;Totaal & 0.1108 & 0.1264 & 0.0946 \tabularnewline
p-value & (0.3125) & (0.2492) & (0.2258) \tabularnewline
LFM;Course & -0.7165 & -0.6807 & -0.5617 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
LFM;Totaal & 0.1431 & 0.1838 & 0.1271 \tabularnewline
p-value & (0.1914) & (0.0922) & (0.1046) \tabularnewline
Course;Totaal & -0.188 & -0.2042 & -0.1772 \tabularnewline
p-value & (0.0849) & (0.0609) & (0.0613) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160260&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]time;logins[/C][C]0.6723[/C][C]0.7451[/C][C]0.5677[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]time;BC[/C][C]0.7232[/C][C]0.7488[/C][C]0.5631[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]time;LFM[/C][C]0.6536[/C][C]0.6597[/C][C]0.4831[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]time;Course[/C][C]-0.5757[/C][C]-0.6138[/C][C]-0.5041[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]time;Totaal[/C][C]0.1108[/C][C]0.0834[/C][C]0.062[/C][/ROW]
[ROW][C]p-value[/C][C](0.3129)[/C][C](0.4478)[/C][C](0.4262)[/C][/ROW]
[ROW][C]logins;BC[/C][C]0.5232[/C][C]0.6656[/C][C]0.4705[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]logins;LFM[/C][C]0.4694[/C][C]0.5445[/C][C]0.3863[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]logins;Course[/C][C]-0.3535[/C][C]-0.4669[/C][C]-0.3849[/C][/ROW]
[ROW][C]p-value[/C][C](9e-04)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]logins;Totaal[/C][C]0.1328[/C][C]0.1122[/C][C]0.0812[/C][/ROW]
[ROW][C]p-value[/C][C](0.2257)[/C][C](0.3067)[/C][C](0.2992)[/C][/ROW]
[ROW][C]BC;LFM[/C][C]0.6796[/C][C]0.6492[/C][C]0.4669[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]BC;Course[/C][C]-0.6155[/C][C]-0.6161[/C][C]-0.5073[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]BC;Totaal[/C][C]0.1108[/C][C]0.1264[/C][C]0.0946[/C][/ROW]
[ROW][C]p-value[/C][C](0.3125)[/C][C](0.2492)[/C][C](0.2258)[/C][/ROW]
[ROW][C]LFM;Course[/C][C]-0.7165[/C][C]-0.6807[/C][C]-0.5617[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]LFM;Totaal[/C][C]0.1431[/C][C]0.1838[/C][C]0.1271[/C][/ROW]
[ROW][C]p-value[/C][C](0.1914)[/C][C](0.0922)[/C][C](0.1046)[/C][/ROW]
[ROW][C]Course;Totaal[/C][C]-0.188[/C][C]-0.2042[/C][C]-0.1772[/C][/ROW]
[ROW][C]p-value[/C][C](0.0849)[/C][C](0.0609)[/C][C](0.0613)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160260&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160260&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
time;logins0.67230.74510.5677
p-value(0)(0)(0)
time;BC0.72320.74880.5631
p-value(0)(0)(0)
time;LFM0.65360.65970.4831
p-value(0)(0)(0)
time;Course-0.5757-0.6138-0.5041
p-value(0)(0)(0)
time;Totaal0.11080.08340.062
p-value(0.3129)(0.4478)(0.4262)
logins;BC0.52320.66560.4705
p-value(0)(0)(0)
logins;LFM0.46940.54450.3863
p-value(0)(0)(0)
logins;Course-0.3535-0.4669-0.3849
p-value(9e-04)(0)(0)
logins;Totaal0.13280.11220.0812
p-value(0.2257)(0.3067)(0.2992)
BC;LFM0.67960.64920.4669
p-value(0)(0)(0)
BC;Course-0.6155-0.6161-0.5073
p-value(0)(0)(0)
BC;Totaal0.11080.12640.0946
p-value(0.3125)(0.2492)(0.2258)
LFM;Course-0.7165-0.6807-0.5617
p-value(0)(0)(0)
LFM;Totaal0.14310.18380.1271
p-value(0.1914)(0.0922)(0.1046)
Course;Totaal-0.188-0.2042-0.1772
p-value(0.0849)(0.0609)(0.0613)



Parameters (Session):
par1 = Valutakoersen Eur-Dollar ; par4 = 12 ;
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')