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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, 13 Dec 2010 14:14:54 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/13/t1292249645j96au3pf4lzloxr.htm/, Retrieved Mon, 06 May 2024 12:51:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108940, Retrieved Mon, 06 May 2024 12:51:54 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsPaper DMA
Estimated Impact121

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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- R PD    [Variance Reduction Matrix] [] [2010-12-06 17:10:58] [d39e5c40c631ed6c22677d2e41dbfc7d]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
3030,29
2803,47
2767,63
2882,6
2863,36
2897,06
3012,61
3142,95
3032,93
3045,78
3110,52
3013,24
2987,1
2995,55
2833,18
2848,96
2794,83
2845,26
2915,03
2892,63
2604,42
2641,65
2659,81
2638,53
2720,25
2745,88
2735,7
2811,7
2799,43
2555,28
2304,98
2214,95
2065,81
1940,49
2042
1995,37
1946,81
1765,9
1635,25
1833,42
1910,43
1959,67
1969,6
2061,41
2093,48
2120,88
2174,56
2196,72
2350,44
2440,25
2408,64
2472,81
2407,6
2454,62
2448,05
2497,84
2645,64
2756,76
2849,27
2921,44
2981,85
3080,58
3106,22
3119,31
3061,26
3097,31
3161,69
3257,16
3277,01
3295,32
3363,99
3494,17
3667,03
3813,06
3917,96
3895,51
3801,06
3570,12
3701,61
3862,27
3970,1
4138,52
4199,75
4290,89
4443,91
4502,64
4356,98
4591,27
4696,96
4621,4
4562,84
4202,52
4296,49
4435,23
4105,18
4116,68
3844,49
3720,98
3674,4
3857,62
3801,06
3504,37
3032,6
3047,03
2962,34
2197,82
2014,45
1862,83
1905,41
Dataseries Y:
Download CSV
Histogram 
25,64
27,97
27,62
23,31
29,07
29,58
28,63
29,92
32,68
31,54
32,43
26,54
25,85
27,6
25,71
25,38
28,57
27,64
25,36
25,9
26,29
21,74
19,2
19,32
19,82
20,36
24,31
25,97
25,61
24,67
25,59
26,09
28,37
27,34
24,46
27,46
30,23
32,33
29,87
24,87
25,48
27,28
28,24
29,58
26,95
29,08
28,76
29,59
30,7
30,52
32,67
33,19
37,13
35,54
37,75
41,84
42,94
49,14
44,61
40,22
44,23
45,85
53,38
53,26
51,8
55,3
57,81
63,96
63,77
59,15
56,12
57,42
63,52
61,71
63,01
68,18
72,03
69,75
74,41
74,33
64,24
60,03
59,44
62,5
55,04
58,34
61,92
67,65
67,68
70,3
75,26
71,44
76,36
81,71
92,6
90,6
92,23
94,09
102,79
109,65
124,05
132,69
135,81
116,07
101,42
75,73
55,48
43,8
45,29

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'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108940&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108940&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108940&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'RServer@AstonUniversity' @ vre.aston.ac.uk






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 series-0.5
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-170.00361360429011221
-160.0588423623491971
-15-0.0251501903655216
-140.075314165205609
-130.0934185068446954
-12-0.0414648377580233
-110.0704994813617618
-100.102252164193133
-90.190170346836752
-80.0214315646339718
-70.0433197849213246
-60.0353585733091636
-50.0946650661375738
-40.107351164590993
-30.0359178313103387
-20.256255137932666
-10.323470146819947
00.0753812941094248
10.0386905939999355
20.0556568133422579
3-0.0370567144235857
4-0.155331342694253
5-0.0111353550485505
6-0.00697927856120171
7-0.0577974521184888
80.0893418851607167
90.0763908933545461
10-0.0068438116661197
110.0619737670488234
12-0.0136026967741669
130.00062036320470797
140.069505415547718
15-0.026554825130283
160.0329360256176207
170.110730226580131

\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 & -0.5 \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
-17 & 0.00361360429011221 \tabularnewline
-16 & 0.0588423623491971 \tabularnewline
-15 & -0.0251501903655216 \tabularnewline
-14 & 0.075314165205609 \tabularnewline
-13 & 0.0934185068446954 \tabularnewline
-12 & -0.0414648377580233 \tabularnewline
-11 & 0.0704994813617618 \tabularnewline
-10 & 0.102252164193133 \tabularnewline
-9 & 0.190170346836752 \tabularnewline
-8 & 0.0214315646339718 \tabularnewline
-7 & 0.0433197849213246 \tabularnewline
-6 & 0.0353585733091636 \tabularnewline
-5 & 0.0946650661375738 \tabularnewline
-4 & 0.107351164590993 \tabularnewline
-3 & 0.0359178313103387 \tabularnewline
-2 & 0.256255137932666 \tabularnewline
-1 & 0.323470146819947 \tabularnewline
0 & 0.0753812941094248 \tabularnewline
1 & 0.0386905939999355 \tabularnewline
2 & 0.0556568133422579 \tabularnewline
3 & -0.0370567144235857 \tabularnewline
4 & -0.155331342694253 \tabularnewline
5 & -0.0111353550485505 \tabularnewline
6 & -0.00697927856120171 \tabularnewline
7 & -0.0577974521184888 \tabularnewline
8 & 0.0893418851607167 \tabularnewline
9 & 0.0763908933545461 \tabularnewline
10 & -0.0068438116661197 \tabularnewline
11 & 0.0619737670488234 \tabularnewline
12 & -0.0136026967741669 \tabularnewline
13 & 0.00062036320470797 \tabularnewline
14 & 0.069505415547718 \tabularnewline
15 & -0.026554825130283 \tabularnewline
16 & 0.0329360256176207 \tabularnewline
17 & 0.110730226580131 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108940&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]-0.5[/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]-17[/C][C]0.00361360429011221[/C][/ROW]
[ROW][C]-16[/C][C]0.0588423623491971[/C][/ROW]
[ROW][C]-15[/C][C]-0.0251501903655216[/C][/ROW]
[ROW][C]-14[/C][C]0.075314165205609[/C][/ROW]
[ROW][C]-13[/C][C]0.0934185068446954[/C][/ROW]
[ROW][C]-12[/C][C]-0.0414648377580233[/C][/ROW]
[ROW][C]-11[/C][C]0.0704994813617618[/C][/ROW]
[ROW][C]-10[/C][C]0.102252164193133[/C][/ROW]
[ROW][C]-9[/C][C]0.190170346836752[/C][/ROW]
[ROW][C]-8[/C][C]0.0214315646339718[/C][/ROW]
[ROW][C]-7[/C][C]0.0433197849213246[/C][/ROW]
[ROW][C]-6[/C][C]0.0353585733091636[/C][/ROW]
[ROW][C]-5[/C][C]0.0946650661375738[/C][/ROW]
[ROW][C]-4[/C][C]0.107351164590993[/C][/ROW]
[ROW][C]-3[/C][C]0.0359178313103387[/C][/ROW]
[ROW][C]-2[/C][C]0.256255137932666[/C][/ROW]
[ROW][C]-1[/C][C]0.323470146819947[/C][/ROW]
[ROW][C]0[/C][C]0.0753812941094248[/C][/ROW]
[ROW][C]1[/C][C]0.0386905939999355[/C][/ROW]
[ROW][C]2[/C][C]0.0556568133422579[/C][/ROW]
[ROW][C]3[/C][C]-0.0370567144235857[/C][/ROW]
[ROW][C]4[/C][C]-0.155331342694253[/C][/ROW]
[ROW][C]5[/C][C]-0.0111353550485505[/C][/ROW]
[ROW][C]6[/C][C]-0.00697927856120171[/C][/ROW]
[ROW][C]7[/C][C]-0.0577974521184888[/C][/ROW]
[ROW][C]8[/C][C]0.0893418851607167[/C][/ROW]
[ROW][C]9[/C][C]0.0763908933545461[/C][/ROW]
[ROW][C]10[/C][C]-0.0068438116661197[/C][/ROW]
[ROW][C]11[/C][C]0.0619737670488234[/C][/ROW]
[ROW][C]12[/C][C]-0.0136026967741669[/C][/ROW]
[ROW][C]13[/C][C]0.00062036320470797[/C][/ROW]
[ROW][C]14[/C][C]0.069505415547718[/C][/ROW]
[ROW][C]15[/C][C]-0.026554825130283[/C][/ROW]
[ROW][C]16[/C][C]0.0329360256176207[/C][/ROW]
[ROW][C]17[/C][C]0.110730226580131[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108940&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108940&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 series-0.5
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-170.00361360429011221
-160.0588423623491971
-15-0.0251501903655216
-140.075314165205609
-130.0934185068446954
-12-0.0414648377580233
-110.0704994813617618
-100.102252164193133
-90.190170346836752
-80.0214315646339718
-70.0433197849213246
-60.0353585733091636
-50.0946650661375738
-40.107351164590993
-30.0359178313103387
-20.256255137932666
-10.323470146819947
00.0753812941094248
10.0386905939999355
20.0556568133422579
3-0.0370567144235857
4-0.155331342694253
5-0.0111353550485505
6-0.00697927856120171
7-0.0577974521184888
80.0893418851607167
90.0763908933545461
10-0.0068438116661197
110.0619737670488234
12-0.0136026967741669
130.00062036320470797
140.069505415547718
15-0.026554825130283
160.0329360256176207
170.110730226580131


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = -0.5 ; par6 = 1 ; par7 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = -0.5 ; 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')