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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 computationWed, 22 Dec 2010 19:38:49 +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/22/t1293046784s12w7hjfw76wros.htm/, Retrieved Mon, 06 May 2024 08:52:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=114535, Retrieved Mon, 06 May 2024 08:52:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact156

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Backward Selection] [Arima backwards s...] [2010-12-21 15:15:51] [ca50229b6b451ac8f5a30a9e3154d674]
- RMPD    [Cross Correlation Function] [cross correlation...] [2010-12-22 19:38:49] [65e95fe5923d75db266bc83cb8a34c47] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
10570
10297
10635
10872
10296
10383
10431
10574
10653
10805
10872
10625
10407
10463
10556
10646
10702
11353
11346
11451
11964
12574
13031
13812
14544
14931
14886
16005
17064
15168
16050
15839
15137
14954
15648
15305
15579
16348
15928
16171
15937
15713
15594
15683
16438
17032
17696
17745
19394
20148
20108
18584
18441
18391
19178
18079
18483
19644
19195
19650
20830
23595
22937
21814
21928
21777
21383
21467
22052
22680
24320
24977
25204
Dataseries Y:
Download CSV
Histogram 
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
1810.99
1670.07
1864.44
2052.02
2029.6
2070.83
2293.41
2443.27
2513.17
2466.92
2502.66
2539.91

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'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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114535&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114535&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114535&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'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 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])
-150.0597031934563637
-140.108655899030501
-13-0.130517503932411
-12-0.0427555084558588
-110.0262203067160433
-10-0.0141329737839878
-9-0.181711749543932
-8-0.0235274692092164
-70.127863420597057
-60.163429423965921
-5-0.0836419905122728
-4-0.109785592583130
-30.218421972506824
-20.132899672910235
-1-0.193003318107813
0-0.184074446467164
10.105131319873136
2-0.0660527128672377
3-0.100813088709685
4-0.119691941383425
50.0979929703525188
60.0661951384982912
7-0.153937179988492
8-0.0252576886946397
90.261689975150021
100.108612358522370
11-0.174592067401708
12-0.0158405742181062
13-0.0947915969108642
14-0.0575504425769805
15-0.0609233259671313

\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
-15 & 0.0597031934563637 \tabularnewline
-14 & 0.108655899030501 \tabularnewline
-13 & -0.130517503932411 \tabularnewline
-12 & -0.0427555084558588 \tabularnewline
-11 & 0.0262203067160433 \tabularnewline
-10 & -0.0141329737839878 \tabularnewline
-9 & -0.181711749543932 \tabularnewline
-8 & -0.0235274692092164 \tabularnewline
-7 & 0.127863420597057 \tabularnewline
-6 & 0.163429423965921 \tabularnewline
-5 & -0.0836419905122728 \tabularnewline
-4 & -0.109785592583130 \tabularnewline
-3 & 0.218421972506824 \tabularnewline
-2 & 0.132899672910235 \tabularnewline
-1 & -0.193003318107813 \tabularnewline
0 & -0.184074446467164 \tabularnewline
1 & 0.105131319873136 \tabularnewline
2 & -0.0660527128672377 \tabularnewline
3 & -0.100813088709685 \tabularnewline
4 & -0.119691941383425 \tabularnewline
5 & 0.0979929703525188 \tabularnewline
6 & 0.0661951384982912 \tabularnewline
7 & -0.153937179988492 \tabularnewline
8 & -0.0252576886946397 \tabularnewline
9 & 0.261689975150021 \tabularnewline
10 & 0.108612358522370 \tabularnewline
11 & -0.174592067401708 \tabularnewline
12 & -0.0158405742181062 \tabularnewline
13 & -0.0947915969108642 \tabularnewline
14 & -0.0575504425769805 \tabularnewline
15 & -0.0609233259671313 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=114535&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]-15[/C][C]0.0597031934563637[/C][/ROW]
[ROW][C]-14[/C][C]0.108655899030501[/C][/ROW]
[ROW][C]-13[/C][C]-0.130517503932411[/C][/ROW]
[ROW][C]-12[/C][C]-0.0427555084558588[/C][/ROW]
[ROW][C]-11[/C][C]0.0262203067160433[/C][/ROW]
[ROW][C]-10[/C][C]-0.0141329737839878[/C][/ROW]
[ROW][C]-9[/C][C]-0.181711749543932[/C][/ROW]
[ROW][C]-8[/C][C]-0.0235274692092164[/C][/ROW]
[ROW][C]-7[/C][C]0.127863420597057[/C][/ROW]
[ROW][C]-6[/C][C]0.163429423965921[/C][/ROW]
[ROW][C]-5[/C][C]-0.0836419905122728[/C][/ROW]
[ROW][C]-4[/C][C]-0.109785592583130[/C][/ROW]
[ROW][C]-3[/C][C]0.218421972506824[/C][/ROW]
[ROW][C]-2[/C][C]0.132899672910235[/C][/ROW]
[ROW][C]-1[/C][C]-0.193003318107813[/C][/ROW]
[ROW][C]0[/C][C]-0.184074446467164[/C][/ROW]
[ROW][C]1[/C][C]0.105131319873136[/C][/ROW]
[ROW][C]2[/C][C]-0.0660527128672377[/C][/ROW]
[ROW][C]3[/C][C]-0.100813088709685[/C][/ROW]
[ROW][C]4[/C][C]-0.119691941383425[/C][/ROW]
[ROW][C]5[/C][C]0.0979929703525188[/C][/ROW]
[ROW][C]6[/C][C]0.0661951384982912[/C][/ROW]
[ROW][C]7[/C][C]-0.153937179988492[/C][/ROW]
[ROW][C]8[/C][C]-0.0252576886946397[/C][/ROW]
[ROW][C]9[/C][C]0.261689975150021[/C][/ROW]
[ROW][C]10[/C][C]0.108612358522370[/C][/ROW]
[ROW][C]11[/C][C]-0.174592067401708[/C][/ROW]
[ROW][C]12[/C][C]-0.0158405742181062[/C][/ROW]
[ROW][C]13[/C][C]-0.0947915969108642[/C][/ROW]
[ROW][C]14[/C][C]-0.0575504425769805[/C][/ROW]
[ROW][C]15[/C][C]-0.0609233259671313[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=114535&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=114535&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])
-150.0597031934563637
-140.108655899030501
-13-0.130517503932411
-12-0.0427555084558588
-110.0262203067160433
-10-0.0141329737839878
-9-0.181711749543932
-8-0.0235274692092164
-70.127863420597057
-60.163429423965921
-5-0.0836419905122728
-4-0.109785592583130
-30.218421972506824
-20.132899672910235
-1-0.193003318107813
0-0.184074446467164
10.105131319873136
2-0.0660527128672377
3-0.100813088709685
4-0.119691941383425
50.0979929703525188
60.0661951384982912
7-0.153937179988492
8-0.0252576886946397
90.261689975150021
100.108612358522370
11-0.174592067401708
12-0.0158405742181062
13-0.0947915969108642
14-0.0575504425769805
15-0.0609233259671313


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')