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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 computationThu, 10 Dec 2009 10:49:58 -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/2009/Dec/10/t12604674572rt8vduiqnwkq9v.htm/, Retrieved Thu, 25 Apr 2024 06:43:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65647, Retrieved Thu, 25 Apr 2024 06:43:20 +0000
QR Codes:

Original text written by user:Paper: Cross correlation function met stationaire gegevens
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact145

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Granger Causality] [] [2009-12-07 09:26:51] [b98453cac15ba1066b407e146608df68]
- RMPD  [Cross Correlation Function] [Paper: Cross Corr...] [2009-12-10 17:40:17] [3c8b83428ce260cd44df892bb7619588]
-   P       [Cross Correlation Function] [Paper: Cross corr...] [2009-12-10 17:49:58] [7a39e26d7a09dd77604df90cb29f8d39] [Current]
-             [Cross Correlation Function] [Cross correlation...] [2009-12-17 17:22:43] [1433a524809eda02c3198b3ae6eebb69]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0.527
0.472
0.000
0.052
0.313
0.364
0.363
-0.155
0.052
0.568
0.668
1.378
0.252
-0.402
-0.050
0.555
0.050
0.150
0.450
0.299
0.199
0.496
0.444
-0.393
-0.444
0.198
0.494
0.133
0.388
0.484
0.278
0.369
0.165
0.155
0.087
0.414
0.360
0.975
0.270
0.359
0.169
0.381
0.154
0.486
0.925
0.728
-0.014
0.046
-0.819
-1.674
-0.788
0.279
0.396
-0.141
-0.019
0.099
0.742
0.005
0.448
Dataseries Y:
Download CSV
Histogram 
0.7461
0.7775
0.7790
0.7744
0.7905
0.7719
0.7811
0.7557
0.7637
0.7595
0.7471
0.7615
0.7487
0.7389
0.7337
0.7510
0.7382
0.7159
0.7542
0.7636
0.7433
0.7658
0.7627
0.7480
0.7692
0.7850
0.7913
0.7720
0.7880
0.8070
0.8268
0.8244
0.8487
0.8572
0.8214
0.8827
0.9216
0.8865
0.8816
0.8884
0.9466
0.9180
0.9337
0.9559
0.9626
0.9434
0.8639
0.7996
0.6680
0.6572
0.6928
0.6438
0.6454
0.6873
0.7265
0.7912
0.8114
0.8281
0.8393

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'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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65647&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' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65647&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65647&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'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 series1
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 series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-140.130338501406128
-13-0.0829687905325172
-12-0.165179091896382
-110.00255757228674377
-100.152306844754602
-9-0.132420912073383
-8-0.0594375672565655
-70.0153876564934375
-6-0.099338047690845
-5-0.0376793798276721
-40.0520492267257836
-30.18321335372656
-2-0.0363152304892941
-1-0.0131915780971010
00.284704253206283
10.278757054979955
2-0.0386534634926205
3-0.145919562107989
4-0.142065196920920
5-0.0854291463315802
6-0.0590512598472598
70.0938997382919324
8-0.0654020216382294
9-0.00972505582154386
10-0.086337180019037
110.105363828899928
12-0.0225271846158958
13-0.153382014154465
14-0.0348617743830117

\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 & 1 \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 & 1 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-14 & 0.130338501406128 \tabularnewline
-13 & -0.0829687905325172 \tabularnewline
-12 & -0.165179091896382 \tabularnewline
-11 & 0.00255757228674377 \tabularnewline
-10 & 0.152306844754602 \tabularnewline
-9 & -0.132420912073383 \tabularnewline
-8 & -0.0594375672565655 \tabularnewline
-7 & 0.0153876564934375 \tabularnewline
-6 & -0.099338047690845 \tabularnewline
-5 & -0.0376793798276721 \tabularnewline
-4 & 0.0520492267257836 \tabularnewline
-3 & 0.18321335372656 \tabularnewline
-2 & -0.0363152304892941 \tabularnewline
-1 & -0.0131915780971010 \tabularnewline
0 & 0.284704253206283 \tabularnewline
1 & 0.278757054979955 \tabularnewline
2 & -0.0386534634926205 \tabularnewline
3 & -0.145919562107989 \tabularnewline
4 & -0.142065196920920 \tabularnewline
5 & -0.0854291463315802 \tabularnewline
6 & -0.0590512598472598 \tabularnewline
7 & 0.0938997382919324 \tabularnewline
8 & -0.0654020216382294 \tabularnewline
9 & -0.00972505582154386 \tabularnewline
10 & -0.086337180019037 \tabularnewline
11 & 0.105363828899928 \tabularnewline
12 & -0.0225271846158958 \tabularnewline
13 & -0.153382014154465 \tabularnewline
14 & -0.0348617743830117 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65647&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]1[/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]1[/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]0.130338501406128[/C][/ROW]
[ROW][C]-13[/C][C]-0.0829687905325172[/C][/ROW]
[ROW][C]-12[/C][C]-0.165179091896382[/C][/ROW]
[ROW][C]-11[/C][C]0.00255757228674377[/C][/ROW]
[ROW][C]-10[/C][C]0.152306844754602[/C][/ROW]
[ROW][C]-9[/C][C]-0.132420912073383[/C][/ROW]
[ROW][C]-8[/C][C]-0.0594375672565655[/C][/ROW]
[ROW][C]-7[/C][C]0.0153876564934375[/C][/ROW]
[ROW][C]-6[/C][C]-0.099338047690845[/C][/ROW]
[ROW][C]-5[/C][C]-0.0376793798276721[/C][/ROW]
[ROW][C]-4[/C][C]0.0520492267257836[/C][/ROW]
[ROW][C]-3[/C][C]0.18321335372656[/C][/ROW]
[ROW][C]-2[/C][C]-0.0363152304892941[/C][/ROW]
[ROW][C]-1[/C][C]-0.0131915780971010[/C][/ROW]
[ROW][C]0[/C][C]0.284704253206283[/C][/ROW]
[ROW][C]1[/C][C]0.278757054979955[/C][/ROW]
[ROW][C]2[/C][C]-0.0386534634926205[/C][/ROW]
[ROW][C]3[/C][C]-0.145919562107989[/C][/ROW]
[ROW][C]4[/C][C]-0.142065196920920[/C][/ROW]
[ROW][C]5[/C][C]-0.0854291463315802[/C][/ROW]
[ROW][C]6[/C][C]-0.0590512598472598[/C][/ROW]
[ROW][C]7[/C][C]0.0938997382919324[/C][/ROW]
[ROW][C]8[/C][C]-0.0654020216382294[/C][/ROW]
[ROW][C]9[/C][C]-0.00972505582154386[/C][/ROW]
[ROW][C]10[/C][C]-0.086337180019037[/C][/ROW]
[ROW][C]11[/C][C]0.105363828899928[/C][/ROW]
[ROW][C]12[/C][C]-0.0225271846158958[/C][/ROW]
[ROW][C]13[/C][C]-0.153382014154465[/C][/ROW]
[ROW][C]14[/C][C]-0.0348617743830117[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65647&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65647&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 series1
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 series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-140.130338501406128
-13-0.0829687905325172
-12-0.165179091896382
-110.00255757228674377
-100.152306844754602
-9-0.132420912073383
-8-0.0594375672565655
-70.0153876564934375
-6-0.099338047690845
-5-0.0376793798276721
-40.0520492267257836
-30.18321335372656
-2-0.0363152304892941
-1-0.0131915780971010
00.284704253206283
10.278757054979955
2-0.0386534634926205
3-0.145919562107989
4-0.142065196920920
5-0.0854291463315802
6-0.0590512598472598
70.0938997382919324
8-0.0654020216382294
9-0.00972505582154386
10-0.086337180019037
110.105363828899928
12-0.0225271846158958
13-0.153382014154465
14-0.0348617743830117


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; par7 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; 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')