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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 computationSun, 30 Nov 2008 07:48:44 -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/Nov/30/t1228056592xhfxvy59a5vdxff.htm/, Retrieved Sun, 19 May 2024 09:37:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26538, Retrieved Sun, 19 May 2024 09:37:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact220

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Law of Averages] [Random Walk Simul...] [2008-11-25 18:31:28] [b98453cac15ba1066b407e146608df68]
F RMPD  [Cross Correlation Function] [Q7] [2008-11-28 08:08:59] [c5a66f1c8528a963efc2b82a8519f117]
F   P       [Cross Correlation Function] [Q7 - d0 D0] [2008-11-30 14:48:44] [5f3e73ccf1ddc75508eed47fa51813d3] [Current]
-             [Cross Correlation Function] [Q7 - d1 D0] [2008-11-30 14:53:09] [a0d819c22534897f04a2f0b92f1eb36a]
-   P           [Cross Correlation Function] [Q7 - d1 D0] [2008-11-30 16:20:05] [c5a66f1c8528a963efc2b82a8519f117]
-   P         [Cross Correlation Function] [Q7 - d1 D1] [2008-11-30 15:02:32] [a0d819c22534897f04a2f0b92f1eb36a]
F RMPD          [Variance Reduction Matrix] [Q8 - VRM -x] [2008-11-30 15:34:51] [a0d819c22534897f04a2f0b92f1eb36a]
-                 [Variance Reduction Matrix] [Q8 - VRM X] [2008-11-30 16:23:16] [c5a66f1c8528a963efc2b82a8519f117]
-   P               [Variance Reduction Matrix] [VRM bouwvergunnin...] [2008-12-05 12:57:40] [c5a66f1c8528a963efc2b82a8519f117]
F RMPD          [Variance Reduction Matrix] [Q8 - VRM - Y] [2008-11-30 15:49:59] [a0d819c22534897f04a2f0b92f1eb36a]
F                 [Variance Reduction Matrix] [Q8 - VRM Y] [2008-11-30 16:24:21] [c5a66f1c8528a963efc2b82a8519f117]
-               [Cross Correlation Function] [Q7 - d1 D1] [2008-11-30 16:21:34] [c5a66f1c8528a963efc2b82a8519f117]
Feedback Forum
2008-12-08 20:12:23 [Kim Wester] [reply] 
Juiste interpretatie van gegevens. Toevoeging: De CCF produceert een voorspelling van een datareeks aan de hand van het verleden van de reeks. In de tabel geeft 0 inderdaad de correlatie tussen Yt en Xt weer. +1 of -1 geeft de correlatie weer tussen respectievelijk Yt en Xt+1 of Yt en Xt-1. Waarbij deze + en - te interpreteren zijn als toekomst en verleden.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1515
1510
1225
1577
1417
1224
1693
1633
1639
1914
1586
1552
2081
1500
1437
1470
1849
1387
1592
1589
1798
1935
1887
2027
2080
1556
1682
1785
1869
1781
2082
2570
1862
1936
1504
1765
1607
1577
1493
1615
1700
1335
1523
1623
1540
1637
1524
1419
1821
1593
1357
1263
1750
1405
1393
1639
1679
1551
1744
1429
1784
Dataseries Y:
Download CSV
Histogram 
2718
2646
2551
2712
2606
2365
3533
3509
2912
3599
2719
2869
4085
2686
2545
3071
3388
2652
3190
2884
3295
3818
3226
3953
3810
2877
3515
3708
3450
3360
4110
4384
3729
4263
3505
3674
3911
2951
3317
3417
3498
2768
2899
3179
3011
3481
3015
2606
3530
2827
3120
2557
3645
2865
2587
2887
3429
2956
3098
2934
3269

Tables (Output of Computation)





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' @ 72.249.76.132

\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' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26538&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' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26538&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26538&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 time1 seconds
R Server'George Udny Yule' @ 72.249.76.132






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])
-140.0326945797697718
-130.0423485057483116
-120.250506353871868
-110.0925664201861248
-100.0966135904175173
-90.279951473798889
-80.266646509724180
-70.131295442303628
-60.170271199627817
-50.207974387142325
-40.283098414810301
-30.467535179649351
-20.290807054735879
-10.297152011124149
00.843223298829926
10.30600081225861
20.185608503932956
30.235655375781350
40.208786801627454
50.0071531514956832
6-0.0377955560551889
7-0.0305739771942716
8-0.00455777840855409
90.0256571519170657
10-0.00340123324653937
11-0.202327718359582
120.0314501733825509
13-0.112093431784197
14-0.200532097630683

\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
-14 & 0.0326945797697718 \tabularnewline
-13 & 0.0423485057483116 \tabularnewline
-12 & 0.250506353871868 \tabularnewline
-11 & 0.0925664201861248 \tabularnewline
-10 & 0.0966135904175173 \tabularnewline
-9 & 0.279951473798889 \tabularnewline
-8 & 0.266646509724180 \tabularnewline
-7 & 0.131295442303628 \tabularnewline
-6 & 0.170271199627817 \tabularnewline
-5 & 0.207974387142325 \tabularnewline
-4 & 0.283098414810301 \tabularnewline
-3 & 0.467535179649351 \tabularnewline
-2 & 0.290807054735879 \tabularnewline
-1 & 0.297152011124149 \tabularnewline
0 & 0.843223298829926 \tabularnewline
1 & 0.30600081225861 \tabularnewline
2 & 0.185608503932956 \tabularnewline
3 & 0.235655375781350 \tabularnewline
4 & 0.208786801627454 \tabularnewline
5 & 0.0071531514956832 \tabularnewline
6 & -0.0377955560551889 \tabularnewline
7 & -0.0305739771942716 \tabularnewline
8 & -0.00455777840855409 \tabularnewline
9 & 0.0256571519170657 \tabularnewline
10 & -0.00340123324653937 \tabularnewline
11 & -0.202327718359582 \tabularnewline
12 & 0.0314501733825509 \tabularnewline
13 & -0.112093431784197 \tabularnewline
14 & -0.200532097630683 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26538&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]-14[/C][C]0.0326945797697718[/C][/ROW]
[ROW][C]-13[/C][C]0.0423485057483116[/C][/ROW]
[ROW][C]-12[/C][C]0.250506353871868[/C][/ROW]
[ROW][C]-11[/C][C]0.0925664201861248[/C][/ROW]
[ROW][C]-10[/C][C]0.0966135904175173[/C][/ROW]
[ROW][C]-9[/C][C]0.279951473798889[/C][/ROW]
[ROW][C]-8[/C][C]0.266646509724180[/C][/ROW]
[ROW][C]-7[/C][C]0.131295442303628[/C][/ROW]
[ROW][C]-6[/C][C]0.170271199627817[/C][/ROW]
[ROW][C]-5[/C][C]0.207974387142325[/C][/ROW]
[ROW][C]-4[/C][C]0.283098414810301[/C][/ROW]
[ROW][C]-3[/C][C]0.467535179649351[/C][/ROW]
[ROW][C]-2[/C][C]0.290807054735879[/C][/ROW]
[ROW][C]-1[/C][C]0.297152011124149[/C][/ROW]
[ROW][C]0[/C][C]0.843223298829926[/C][/ROW]
[ROW][C]1[/C][C]0.30600081225861[/C][/ROW]
[ROW][C]2[/C][C]0.185608503932956[/C][/ROW]
[ROW][C]3[/C][C]0.235655375781350[/C][/ROW]
[ROW][C]4[/C][C]0.208786801627454[/C][/ROW]
[ROW][C]5[/C][C]0.0071531514956832[/C][/ROW]
[ROW][C]6[/C][C]-0.0377955560551889[/C][/ROW]
[ROW][C]7[/C][C]-0.0305739771942716[/C][/ROW]
[ROW][C]8[/C][C]-0.00455777840855409[/C][/ROW]
[ROW][C]9[/C][C]0.0256571519170657[/C][/ROW]
[ROW][C]10[/C][C]-0.00340123324653937[/C][/ROW]
[ROW][C]11[/C][C]-0.202327718359582[/C][/ROW]
[ROW][C]12[/C][C]0.0314501733825509[/C][/ROW]
[ROW][C]13[/C][C]-0.112093431784197[/C][/ROW]
[ROW][C]14[/C][C]-0.200532097630683[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26538&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26538&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])
-140.0326945797697718
-130.0423485057483116
-120.250506353871868
-110.0925664201861248
-100.0966135904175173
-90.279951473798889
-80.266646509724180
-70.131295442303628
-60.170271199627817
-50.207974387142325
-40.283098414810301
-30.467535179649351
-20.290807054735879
-10.297152011124149
00.843223298829926
10.30600081225861
20.185608503932956
30.235655375781350
40.208786801627454
50.0071531514956832
6-0.0377955560551889
7-0.0305739771942716
8-0.00455777840855409
90.0256571519170657
10-0.00340123324653937
11-0.202327718359582
120.0314501733825509
13-0.112093431784197
14-0.200532097630683


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')