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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 computationMon, 22 Dec 2008 03:31:31 -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/22/t1229941960x768npmg75k5h41.htm/, Retrieved Sun, 12 May 2024 23:52:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=35985, Retrieved Sun, 12 May 2024 23:52:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact228

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]
- R PD  [Univariate Data Series] [Tijdreeks 2 Buite...] [2008-12-11 16:25:30] [2d4aec5ed1856c4828162be37be304d9]
- RMP     [Central Tendency] [Central tendency ...] [2008-12-11 17:41:16] [2d4aec5ed1856c4828162be37be304d9]
- RMP       [Blocked Bootstrap Plot - Central Tendency] [Blocked Bootstrap...] [2008-12-12 08:14:08] [2d4aec5ed1856c4828162be37be304d9]
- RMP         [Tukey lambda PPCC Plot] [Tukey Lambda PPCC...] [2008-12-12 08:45:26] [2d4aec5ed1856c4828162be37be304d9]
- RMP           [Univariate Explorative Data Analysis] [Lag plot + ACF Ti...] [2008-12-12 08:54:04] [2d4aec5ed1856c4828162be37be304d9]
- RMP             [Variance Reduction Matrix] [VRM tijdreeks 2] [2008-12-12 10:58:24] [2d4aec5ed1856c4828162be37be304d9]
- RMP               [(Partial) Autocorrelation Function] [P(ACF) Tijdreeks ...] [2008-12-12 12:17:09] [2d4aec5ed1856c4828162be37be304d9]
- RMP                 [ARIMA Backward Selection] [ARIMA Backward Se...] [2008-12-12 12:29:19] [2d4aec5ed1856c4828162be37be304d9]
- RMPD                  [Bivariate Kernel Density Estimation] [Bivariate Kernel ...] [2008-12-22 09:26:11] [2d4aec5ed1856c4828162be37be304d9]
- RMPD                    [Kendall tau Correlation Matrix] [Kendall Tau Corre...] [2008-12-22 09:35:25] [2d4aec5ed1856c4828162be37be304d9]
- RM D                      [Pearson Correlation] [Pearson correlati...] [2008-12-22 09:46:51] [2d4aec5ed1856c4828162be37be304d9]
- RMP                           [Cross Correlation Function] [Cross Correlation...] [2008-12-22 10:31:31] [d7f41258beeebb8716e3f5d39f3cdc01] [Current]
-   P                             [Cross Correlation Function] [Cross Correlation...] [2008-12-22 11:21:14] [2d4aec5ed1856c4828162be37be304d9]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
16283.6
16726.5
14968.9
14861
14583.3
15305.8
17903.9
16379.4
15420.3
17870.5
15912.8
13866.5
17823.2
17872
17420.4
16704.4
15991.2
16583.6
19123.5
17838.7
17209.4
18586.5
16258.1
15141.6
19202.1
17746.5
19090.1
18040.3
17515.5
17751.8
21072.4
17170
19439.5
19795.4
17574.9
16165.4
19464.6
19932.1
19961.2
17343.4
18924.2
18574.1
21350.6
18594.6
19823.1
20844.4
19640.2
17735.4
19813.6
22160
20664.3
17877.4
21211.2
21423.1
21688.7
23243.2
21490.2
22925.8
23184.8
18562.2
Dataseries Y:
Download CSV
Histogram 
2220.6
2161.5
1863.6
1955.1
1907.4
1889.4
2246.3
2213
1965
2285.6
1983.8
1872.4
2371.4
2287
2198.2
2330.4
2014.4
2066.1
2355.8
2232.5
2091.7
2376.5
1931.9
2025.7
2404.9
2316.1
2368.1
2282.5
2158.6
2174.8
2594.1
2281.4
2547.9
2606.3
2190.8
2262.3
2423.8
2520.4
2482.9
2215.9
2441.9
2333.8
2670.2
2431
2559.3
2661.4
2404.6
2378.3
2489.2
2941
2700.9
2335.6
2770
2764.2
2784.9
2898.8
2853.4
3022.6
2851.4
2630.8

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 2 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35985&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35985&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35985&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 time2 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






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])
-140.179513267661386
-130.238327098212828
-120.40537531779778
-110.279986280425176
-100.200029003207702
-90.316874913226321
-80.386774933293987
-70.394212423193832
-60.480900284109074
-50.455578099363984
-40.500217431328281
-30.59401837230566
-20.543779757471114
-10.653119989012865
00.95696143651742
10.64844760767617
20.489479571131184
30.55545081002324
40.44859227224398
50.451734939906161
60.481623300226135
70.331105354550571
80.335128139716447
90.318846316059402
100.156647425366858
110.203422736683827
120.355073633275054
130.199022628123894
140.0953757719874084

\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 & 0.179513267661386 \tabularnewline
-13 & 0.238327098212828 \tabularnewline
-12 & 0.40537531779778 \tabularnewline
-11 & 0.279986280425176 \tabularnewline
-10 & 0.200029003207702 \tabularnewline
-9 & 0.316874913226321 \tabularnewline
-8 & 0.386774933293987 \tabularnewline
-7 & 0.394212423193832 \tabularnewline
-6 & 0.480900284109074 \tabularnewline
-5 & 0.455578099363984 \tabularnewline
-4 & 0.500217431328281 \tabularnewline
-3 & 0.59401837230566 \tabularnewline
-2 & 0.543779757471114 \tabularnewline
-1 & 0.653119989012865 \tabularnewline
0 & 0.95696143651742 \tabularnewline
1 & 0.64844760767617 \tabularnewline
2 & 0.489479571131184 \tabularnewline
3 & 0.55545081002324 \tabularnewline
4 & 0.44859227224398 \tabularnewline
5 & 0.451734939906161 \tabularnewline
6 & 0.481623300226135 \tabularnewline
7 & 0.331105354550571 \tabularnewline
8 & 0.335128139716447 \tabularnewline
9 & 0.318846316059402 \tabularnewline
10 & 0.156647425366858 \tabularnewline
11 & 0.203422736683827 \tabularnewline
12 & 0.355073633275054 \tabularnewline
13 & 0.199022628123894 \tabularnewline
14 & 0.0953757719874084 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35985&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]0.179513267661386[/C][/ROW]
[ROW][C]-13[/C][C]0.238327098212828[/C][/ROW]
[ROW][C]-12[/C][C]0.40537531779778[/C][/ROW]
[ROW][C]-11[/C][C]0.279986280425176[/C][/ROW]
[ROW][C]-10[/C][C]0.200029003207702[/C][/ROW]
[ROW][C]-9[/C][C]0.316874913226321[/C][/ROW]
[ROW][C]-8[/C][C]0.386774933293987[/C][/ROW]
[ROW][C]-7[/C][C]0.394212423193832[/C][/ROW]
[ROW][C]-6[/C][C]0.480900284109074[/C][/ROW]
[ROW][C]-5[/C][C]0.455578099363984[/C][/ROW]
[ROW][C]-4[/C][C]0.500217431328281[/C][/ROW]
[ROW][C]-3[/C][C]0.59401837230566[/C][/ROW]
[ROW][C]-2[/C][C]0.543779757471114[/C][/ROW]
[ROW][C]-1[/C][C]0.653119989012865[/C][/ROW]
[ROW][C]0[/C][C]0.95696143651742[/C][/ROW]
[ROW][C]1[/C][C]0.64844760767617[/C][/ROW]
[ROW][C]2[/C][C]0.489479571131184[/C][/ROW]
[ROW][C]3[/C][C]0.55545081002324[/C][/ROW]
[ROW][C]4[/C][C]0.44859227224398[/C][/ROW]
[ROW][C]5[/C][C]0.451734939906161[/C][/ROW]
[ROW][C]6[/C][C]0.481623300226135[/C][/ROW]
[ROW][C]7[/C][C]0.331105354550571[/C][/ROW]
[ROW][C]8[/C][C]0.335128139716447[/C][/ROW]
[ROW][C]9[/C][C]0.318846316059402[/C][/ROW]
[ROW][C]10[/C][C]0.156647425366858[/C][/ROW]
[ROW][C]11[/C][C]0.203422736683827[/C][/ROW]
[ROW][C]12[/C][C]0.355073633275054[/C][/ROW]
[ROW][C]13[/C][C]0.199022628123894[/C][/ROW]
[ROW][C]14[/C][C]0.0953757719874084[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35985&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35985&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])
-140.179513267661386
-130.238327098212828
-120.40537531779778
-110.279986280425176
-100.200029003207702
-90.316874913226321
-80.386774933293987
-70.394212423193832
-60.480900284109074
-50.455578099363984
-40.500217431328281
-30.59401837230566
-20.543779757471114
-10.653119989012865
00.95696143651742
10.64844760767617
20.489479571131184
30.55545081002324
40.44859227224398
50.451734939906161
60.481623300226135
70.331105354550571
80.335128139716447
90.318846316059402
100.156647425366858
110.203422736683827
120.355073633275054
130.199022628123894
140.0953757719874084


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')