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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationWed, 03 Dec 2008 05:53:14 -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/03/t1228308830rhcqwxtx7fbylqj.htm/, Retrieved Fri, 17 May 2024 19:00:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28672, Retrieved Fri, 17 May 2024 19:00:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact189

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Cross Correlation Function] [NonStationaryTime...] [2008-12-03 12:53:14] [a413cf7744efd6bb212437a3916e2f23] [Current]
Feedback Forum
2008-12-08 18:20:23 [An Knapen] [reply] 
De student heeft hier een differentiatie toegepast waarbij d en D gelijk zijn aan 1. We zien duidelijk dat de differentiatie wel geholpen heeft. In vergelijking tot de grafiek bij q7, liggen er nu meer waarden binnen het betrouwbaarheidsinterval. Toch kunnen we zien echter dat niet alle waarden binnen het betrouwbaarheidsinterval liggen. Het model kan dus mogelijk nog verbeterd worden.
2008-12-08 18:59:06 [Sofie Sergoynne] [reply] 
Correct antwoord. Toch is dit model nog voor verbetering vatbaar, omdat nog niet alle lijntjes in het 95% betrouwbaarheidsinterval liggen.
2008-12-08 19:29:15 [Ellen Van den Broeck] [reply] 
goed

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1846,5
2796,3
2895,6
2472,2
2584,4
2630,4
2663,1
3176,2
2856,7
2551,4
3088,7
2628,3
2226,2
3023,6
3077,9
3084,1
2990,3
2949,6
3014,7
3517,7
3121,2
3067,4
3174,6
2676,3
2424
3195,1
3146,6
3506,7
3528,5
3365,1
3153
3843,3
3123,2
3361,1
3481,9
2970,5
2537
3257,6
3301,3
3391,6
2933,6
3283,2
3139,7
3486,4
3202,2
3294,4
3550,3
3279,3
2678,6
3451,4
3977,1
3814,8
3310,5
3971,8
4051,9
4057,6
4391,4
3628,9
4092,2
3822,5
Dataseries Y:
Download CSV
Histogram 
1530,9
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
2959
2713,5
2341,3
2833,2
2849,7
2871,7
3058,3
2855,1
3083,6
2828,3

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'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28672&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28672&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28672&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series0.1
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series0.7
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-130.228597246023128
-12-0.192577628892785
-110.09235732768528
-100.22675490408866
-90.0151623372514743
-80.037012598749865
-70.315650979385445
-6-0.114556476302305
-50.0838781147812305
-40.12032988318799
-3-0.0850522980403927
-2-0.0540657259804292
-10.455956016800722
0-0.49979934444174
10.0852066917592345
20.136132655340321
3-0.261288590072289
4-0.0366227867487118
50.0902111280636788
6-0.324589295078131
70.139658691661683
80.120298319594206
9-0.223598095717297
100.161160141176547
110.164869352245549
12-0.0845761692991327
130.00833931137614707

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 0.1 \tabularnewline
Degree of non-seasonal differencing (d) of X series & 1 \tabularnewline
Degree of seasonal differencing (D) of X series & 1 \tabularnewline
Seasonal Period (s) & 12 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & 0.7 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 0 \tabularnewline
Degree of seasonal differencing (D) of Y series & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-13 & 0.228597246023128 \tabularnewline
-12 & -0.192577628892785 \tabularnewline
-11 & 0.09235732768528 \tabularnewline
-10 & 0.22675490408866 \tabularnewline
-9 & 0.0151623372514743 \tabularnewline
-8 & 0.037012598749865 \tabularnewline
-7 & 0.315650979385445 \tabularnewline
-6 & -0.114556476302305 \tabularnewline
-5 & 0.0838781147812305 \tabularnewline
-4 & 0.12032988318799 \tabularnewline
-3 & -0.0850522980403927 \tabularnewline
-2 & -0.0540657259804292 \tabularnewline
-1 & 0.455956016800722 \tabularnewline
0 & -0.49979934444174 \tabularnewline
1 & 0.0852066917592345 \tabularnewline
2 & 0.136132655340321 \tabularnewline
3 & -0.261288590072289 \tabularnewline
4 & -0.0366227867487118 \tabularnewline
5 & 0.0902111280636788 \tabularnewline
6 & -0.324589295078131 \tabularnewline
7 & 0.139658691661683 \tabularnewline
8 & 0.120298319594206 \tabularnewline
9 & -0.223598095717297 \tabularnewline
10 & 0.161160141176547 \tabularnewline
11 & 0.164869352245549 \tabularnewline
12 & -0.0845761692991327 \tabularnewline
13 & 0.00833931137614707 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28672&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]0.1[/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]1[/C][/ROW]
[ROW][C]Seasonal Period (s)[/C][C]12[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda) of Y series[/C][C]0.7[/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]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-13[/C][C]0.228597246023128[/C][/ROW]
[ROW][C]-12[/C][C]-0.192577628892785[/C][/ROW]
[ROW][C]-11[/C][C]0.09235732768528[/C][/ROW]
[ROW][C]-10[/C][C]0.22675490408866[/C][/ROW]
[ROW][C]-9[/C][C]0.0151623372514743[/C][/ROW]
[ROW][C]-8[/C][C]0.037012598749865[/C][/ROW]
[ROW][C]-7[/C][C]0.315650979385445[/C][/ROW]
[ROW][C]-6[/C][C]-0.114556476302305[/C][/ROW]
[ROW][C]-5[/C][C]0.0838781147812305[/C][/ROW]
[ROW][C]-4[/C][C]0.12032988318799[/C][/ROW]
[ROW][C]-3[/C][C]-0.0850522980403927[/C][/ROW]
[ROW][C]-2[/C][C]-0.0540657259804292[/C][/ROW]
[ROW][C]-1[/C][C]0.455956016800722[/C][/ROW]
[ROW][C]0[/C][C]-0.49979934444174[/C][/ROW]
[ROW][C]1[/C][C]0.0852066917592345[/C][/ROW]
[ROW][C]2[/C][C]0.136132655340321[/C][/ROW]
[ROW][C]3[/C][C]-0.261288590072289[/C][/ROW]
[ROW][C]4[/C][C]-0.0366227867487118[/C][/ROW]
[ROW][C]5[/C][C]0.0902111280636788[/C][/ROW]
[ROW][C]6[/C][C]-0.324589295078131[/C][/ROW]
[ROW][C]7[/C][C]0.139658691661683[/C][/ROW]
[ROW][C]8[/C][C]0.120298319594206[/C][/ROW]
[ROW][C]9[/C][C]-0.223598095717297[/C][/ROW]
[ROW][C]10[/C][C]0.161160141176547[/C][/ROW]
[ROW][C]11[/C][C]0.164869352245549[/C][/ROW]
[ROW][C]12[/C][C]-0.0845761692991327[/C][/ROW]
[ROW][C]13[/C][C]0.00833931137614707[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28672&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28672&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 series0.1
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series0.7
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-130.228597246023128
-12-0.192577628892785
-110.09235732768528
-100.22675490408866
-90.0151623372514743
-80.037012598749865
-70.315650979385445
-6-0.114556476302305
-50.0838781147812305
-40.12032988318799
-3-0.0850522980403927
-2-0.0540657259804292
-10.455956016800722
0-0.49979934444174
10.0852066917592345
20.136132655340321
3-0.261288590072289
4-0.0366227867487118
50.0902111280636788
6-0.324589295078131
70.139658691661683
80.120298319594206
9-0.223598095717297
100.161160141176547
110.164869352245549
12-0.0845761692991327
130.00833931137614707


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0.1 ; par2 = 1 ; par3 = 1 ; par4 = 12 ; par5 = 0.7 ; par6 = 0 ; par7 = 1 ;
Parameters (R input):
par1 = 0.1 ; par2 = 1 ; par3 = 1 ; par4 = 12 ; par5 = 0.7 ; par6 = 0 ; par7 = 1 ;
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')