FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationMon, 01 Dec 2008 13:13:53 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/01/t1228162500n829mzgriphhp16.htm/, Retrieved Sun, 05 May 2024 18:47:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27319, Retrieved Sun, 05 May 2024 18:47:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact206

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
F RMPD  [Variance Reduction Matrix] [Non Stationary Ti...] [2008-12-01 17:48:47] [b943bd7078334192ff8343563ee31113]
- RMP     [Spectral Analysis] [Non Stationary Ti...] [2008-12-01 19:56:04] [b943bd7078334192ff8343563ee31113]
- RMPD        [Cross Correlation Function] [Non Stationary Ti...] [2008-12-01 20:13:53] [620b6ad5c4696049e39cb73ce029682c] [Current]
- RMPD          [(Partial) Autocorrelation Function] [Non Stationary Ti...] [2008-12-01 20:27:10] [b943bd7078334192ff8343563ee31113]
-   PD            [(Partial) Autocorrelation Function] [Non Stationary Ti...] [2008-12-01 20:29:06] [b943bd7078334192ff8343563ee31113]
-   P               [(Partial) Autocorrelation Function] [Non Stationary Ti...] [2008-12-01 20:31:48] [b943bd7078334192ff8343563ee31113]
- RMP                 [Variance Reduction Matrix] [Non Stationary Ti...] [2008-12-01 20:34:07] [b943bd7078334192ff8343563ee31113]
- RMP                   [Spectral Analysis] [Non Stationary Ti...] [2008-12-01 20:37:58] [b943bd7078334192ff8343563ee31113]
- RMP                     [Standard Deviation-Mean Plot] [Non Stationary Ti...] [2008-12-01 20:41:45] [b943bd7078334192ff8343563ee31113]
- RMPD                      [(Partial) Autocorrelation Function] [Non Stationary Ti...] [2008-12-02 07:15:23] [b943bd7078334192ff8343563ee31113]
-   PD                        [(Partial) Autocorrelation Function] [Non Stationary Ti...] [2008-12-02 07:17:05] [b943bd7078334192ff8343563ee31113]
-   P                           [(Partial) Autocorrelation Function] [Non Stationary Ti...] [2008-12-02 07:19:06] [b943bd7078334192ff8343563ee31113]
- RMP                             [Variance Reduction Matrix] [Non Stationary Ti...] [2008-12-02 07:22:03] [b943bd7078334192ff8343563ee31113]
- RMP                               [Spectral Analysis] [Non Stationary Ti...] [2008-12-02 07:25:43] [b943bd7078334192ff8343563ee31113]
- RMP                                 [Standard Deviation-Mean Plot] [Non Stationary Ti...] [2008-12-02 07:32:20] [b943bd7078334192ff8343563ee31113]
-   PD          [Cross Correlation Function] [Non Stationary Ti...] [2008-12-02 07:37:59] [b943bd7078334192ff8343563ee31113]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1593
1477,9
1733,7
1569,7
1843,7
1950,3
1657,5
1772,1
1568,3
1809,8
1646,7
1808,5
1763,9
1625,5
1538,8
1342,4
1645,1
1619,9
1338,1
1505,5
1529,1
1511,9
1656,7
1694,4
1662,3
1588,7
1483,3
1585,6
1658,9
1584,4
1470,6
1618,7
1407,6
1473,9
1515,3
1485,4
1496,1
1493,5
1298,4
1375,3
1507,9
1455,3
1363,3
1392,8
1348,8
1880,3
1669,2
1543,6
1701,2
1516,5
1466,8
1484,1
1577,2
1684,5
1414,7
1674,5
1598,7
1739,1
1674,6
1671,8
1802
1526,8
1580,9
1634,8
1610,3
1712
1678,8
1708,1
1680,6
2056
1624
2021,4
1861,1
1750,8
1767,5
1710,3
2151,5
2047,9
1915,4
1984,7
1896,5
2170,8
2139,9
2330,5
2121,8
2226,8
1857,9
2155,9
2341,7
2290,2
2006,5
2111,9
1731,3
1762,2
1863,2
1943,5
1975,2
Dataseries Y:
Download CSV
Histogram 
0,8721
0,8552
0,8564
0,8973
0,9383
0,9217
0,9095
0,892
0,8742
0,8532
0,8607
0,9005
0,9111
0,9059
0,8883
0,8924
0,8833
0,87
0,8758
0,8858
0,917
0,9554
0,9922
0,9778
0,9808
0,9811
1,0014
1,0183
1,0622
1,0773
1,0807
1,0848
1,1582
1,1663
1,1372
1,1139
1,1222
1,1692
1,1702
1,2286
1,2613
1,2646
1,2262
1,1985
1,2007
1,2138
1,2266
1,2176
1,2218
1,249
1,2991
1,3408
1,3119
1,3014
1,3201
1,2938
1,2694
1,2165
1,2037
1,2292
1,2256
1,2015
1,1786
1,1856
1,2103
1,1938
1,202
1,2271
1,277
1,265
1,2684
1,2811
1,2727
1,2611
1,2881
1,3213
1,2999
1,3074
1,3242
1,3516
1,3511
1,3419
1,3716
1,3622
1,3896
1,4227
1,4684
1,457
1,4718
1,4748
1,5527
1,575
1,5557
1,5553
1,577
1,4975
1,4369

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 0 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27319&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]0 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27319&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27319&T=0

As an alternative you can also use a QR Code:  
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 time0 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-160.0817609384561544
-150.115450970769826
-140.141362088529336
-130.186161661795878
-120.222285791663636
-110.27832657695893
-100.303852833670714
-90.340747317107217
-80.3757728580923
-70.413632634567334
-60.449916369277074
-50.471921328793107
-40.471202149695161
-30.477022453652748
-20.472708218857857
-10.479835611294717
00.487311587233683
10.485673977020617
20.454734845036591
30.449656646693705
40.436838267698612
50.446724357506247
60.453380429288275
70.440780458701810
80.445626148319672
90.427649219976982
100.420882922895053
110.418486942437389
120.440083210298682
130.455271499694564
140.447263135461908
150.446023068750283
160.436809078898228

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 1 \tabularnewline
Degree of non-seasonal differencing (d) of X series & 0 \tabularnewline
Degree of seasonal differencing (D) of X series & 0 \tabularnewline
Seasonal Period (s) & 12 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & 1 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 0 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-16 & 0.0817609384561544 \tabularnewline
-15 & 0.115450970769826 \tabularnewline
-14 & 0.141362088529336 \tabularnewline
-13 & 0.186161661795878 \tabularnewline
-12 & 0.222285791663636 \tabularnewline
-11 & 0.27832657695893 \tabularnewline
-10 & 0.303852833670714 \tabularnewline
-9 & 0.340747317107217 \tabularnewline
-8 & 0.3757728580923 \tabularnewline
-7 & 0.413632634567334 \tabularnewline
-6 & 0.449916369277074 \tabularnewline
-5 & 0.471921328793107 \tabularnewline
-4 & 0.471202149695161 \tabularnewline
-3 & 0.477022453652748 \tabularnewline
-2 & 0.472708218857857 \tabularnewline
-1 & 0.479835611294717 \tabularnewline
0 & 0.487311587233683 \tabularnewline
1 & 0.485673977020617 \tabularnewline
2 & 0.454734845036591 \tabularnewline
3 & 0.449656646693705 \tabularnewline
4 & 0.436838267698612 \tabularnewline
5 & 0.446724357506247 \tabularnewline
6 & 0.453380429288275 \tabularnewline
7 & 0.440780458701810 \tabularnewline
8 & 0.445626148319672 \tabularnewline
9 & 0.427649219976982 \tabularnewline
10 & 0.420882922895053 \tabularnewline
11 & 0.418486942437389 \tabularnewline
12 & 0.440083210298682 \tabularnewline
13 & 0.455271499694564 \tabularnewline
14 & 0.447263135461908 \tabularnewline
15 & 0.446023068750283 \tabularnewline
16 & 0.436809078898228 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27319&T=1

[TABLE]
[ROW][C]Cross Correlation Function[/C][/ROW]
[ROW][C]Parameter[/C][C]Value[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda) of X series[/C][C]1[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of X series[/C][C]0[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of X series[/C][C]0[/C][/ROW]
[ROW][C]Seasonal Period (s)[/C][C]12[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda) of Y series[/C][C]1[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of Y series[/C][C]0[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of Y series[/C][C]0[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-16[/C][C]0.0817609384561544[/C][/ROW]
[ROW][C]-15[/C][C]0.115450970769826[/C][/ROW]
[ROW][C]-14[/C][C]0.141362088529336[/C][/ROW]
[ROW][C]-13[/C][C]0.186161661795878[/C][/ROW]
[ROW][C]-12[/C][C]0.222285791663636[/C][/ROW]
[ROW][C]-11[/C][C]0.27832657695893[/C][/ROW]
[ROW][C]-10[/C][C]0.303852833670714[/C][/ROW]
[ROW][C]-9[/C][C]0.340747317107217[/C][/ROW]
[ROW][C]-8[/C][C]0.3757728580923[/C][/ROW]
[ROW][C]-7[/C][C]0.413632634567334[/C][/ROW]
[ROW][C]-6[/C][C]0.449916369277074[/C][/ROW]
[ROW][C]-5[/C][C]0.471921328793107[/C][/ROW]
[ROW][C]-4[/C][C]0.471202149695161[/C][/ROW]
[ROW][C]-3[/C][C]0.477022453652748[/C][/ROW]
[ROW][C]-2[/C][C]0.472708218857857[/C][/ROW]
[ROW][C]-1[/C][C]0.479835611294717[/C][/ROW]
[ROW][C]0[/C][C]0.487311587233683[/C][/ROW]
[ROW][C]1[/C][C]0.485673977020617[/C][/ROW]
[ROW][C]2[/C][C]0.454734845036591[/C][/ROW]
[ROW][C]3[/C][C]0.449656646693705[/C][/ROW]
[ROW][C]4[/C][C]0.436838267698612[/C][/ROW]
[ROW][C]5[/C][C]0.446724357506247[/C][/ROW]
[ROW][C]6[/C][C]0.453380429288275[/C][/ROW]
[ROW][C]7[/C][C]0.440780458701810[/C][/ROW]
[ROW][C]8[/C][C]0.445626148319672[/C][/ROW]
[ROW][C]9[/C][C]0.427649219976982[/C][/ROW]
[ROW][C]10[/C][C]0.420882922895053[/C][/ROW]
[ROW][C]11[/C][C]0.418486942437389[/C][/ROW]
[ROW][C]12[/C][C]0.440083210298682[/C][/ROW]
[ROW][C]13[/C][C]0.455271499694564[/C][/ROW]
[ROW][C]14[/C][C]0.447263135461908[/C][/ROW]
[ROW][C]15[/C][C]0.446023068750283[/C][/ROW]
[ROW][C]16[/C][C]0.436809078898228[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27319&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27319&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-160.0817609384561544
-150.115450970769826
-140.141362088529336
-130.186161661795878
-120.222285791663636
-110.27832657695893
-100.303852833670714
-90.340747317107217
-80.3757728580923
-70.413632634567334
-60.449916369277074
-50.471921328793107
-40.471202149695161
-30.477022453652748
-20.472708218857857
-10.479835611294717
00.487311587233683
10.485673977020617
20.454734845036591
30.449656646693705
40.436838267698612
50.446724357506247
60.453380429288275
70.440780458701810
80.445626148319672
90.427649219976982
100.420882922895053
110.418486942437389
120.440083210298682
130.455271499694564
140.447263135461908
150.446023068750283
160.436809078898228


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 0 ; par7 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 0 ; par7 = 0 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
par3 <- as.numeric(par3)
par4 <- as.numeric(par4)
par5 <- as.numeric(par5)
par6 <- as.numeric(par6)
par7 <- as.numeric(par7)
if (par1 == 0) {
x <- log(x)
} else {
x <- (x ^ par1 - 1) / par1
}
if (par5 == 0) {
y <- log(y)
} else {
y <- (y ^ par5 - 1) / par5
}
if (par2 > 0) x <- diff(x,lag=1,difference=par2)
if (par6 > 0) y <- diff(y,lag=1,difference=par6)
if (par3 > 0) x <- diff(x,lag=par4,difference=par3)
if (par7 > 0) y <- diff(y,lag=par4,difference=par7)
x
y
bitmap(file='test1.png')
(r <- ccf(x,y,main='Cross Correlation Function',ylab='CCF',xlab='Lag (k)'))
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Cross Correlation Function',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Box-Cox transformation parameter (lambda) of X series',header=TRUE)
a<-table.element(a,par1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of non-seasonal differencing (d) of X series',header=TRUE)
a<-table.element(a,par2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of seasonal differencing (D) of X series',header=TRUE)
a<-table.element(a,par3)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Seasonal Period (s)',header=TRUE)
a<-table.element(a,par4)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Box-Cox transformation parameter (lambda) of Y series',header=TRUE)
a<-table.element(a,par5)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of non-seasonal differencing (d) of Y series',header=TRUE)
a<-table.element(a,par6)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of seasonal differencing (D) of Y series',header=TRUE)
a<-table.element(a,par7)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'k',header=TRUE)
a<-table.element(a,'rho(Y[t],X[t+k])',header=TRUE)
a<-table.row.end(a)
mylength <- length(r$acf)
myhalf <- floor((mylength-1)/2)
for (i in 1:mylength) {
a<-table.row.start(a)
a<-table.element(a,i-myhalf-1,header=TRUE)
a<-table.element(a,r$acf[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')