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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 computationTue, 02 Dec 2008 08:33:27 -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/02/t1228232039um0o7kyra7t4tsf.htm/, Retrieved Fri, 17 May 2024 02:02:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27959, Retrieved Fri, 17 May 2024 02:02:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact187

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]
F RMPD    [Cross Correlation Function] [W7Q7] [2008-12-02 15:33:27] [823d674fbf3a4e0ec71bbbd5140f82c6] [Current]
F RMPD      [] [W7Q9] [-0001-11-30 00:00:00] [a65f6f9c2f5304f20966be510d230930]
Feedback Forum
2008-12-08 16:00:38 [Kevin Vermeiren] [reply] 
: De student geeft hier een correct antwoord. Hier had nog vermeld mogen worden dat deze methode de correlatie berekend tussen 2 tijdreeksen. We gebruiken deze methode om na te gaan of het verleden van Xt ons kan helpen om Yt te voorspellen. We spreken hier echter niet over het vertragen van reeksen. Beter is hier te spreken over het verschuiven in de tijd. Als K negatief is vb: K=-9 wil zeggen correlatie tussen Yt en Xt-9, k=-8 is correlatie tussen Yt en Xt-8. Negatieve waarden duiden aan in welke mate kan ik Yt verklaren op basis van het verleden van Xt. Positieve k-waarden gebruiken we om een antwoord te vinden op de vraag heeft het verleden van Yt een invloed op Xt. Er is inderdaad sprake van een leading indicator, daar deze op voorhand informatie geeft omtrent verloop van de andere variabele. Verder is correct dat alle verticale lijnen buiten het betrouwbaarheidsinterval liggen. We kunnen dus niet zeggen dat dit te wijten is aan het toeval, de correlatie is bijgevolg significant verschillend van 0.
2008-12-08 21:45:08 [] [reply] 
De gebruikte methode berekent de correlatie berekend tussen 2 tijdreeksen. De reden waarom we deze methode gebruiken is om na te gaan of het verleden van Xt ons kan helpen om Yt te voorspellen. We kunnen dit fenomeen beschrijven als het verschuiven in de tijd.
2008-12-09 11:37:58 [Yannick Van Schil] [reply] 
correct geantwoord, de methode berekent de correlatie tussen 2 tijdreeksen. Deze methode voorspeld Yt aan de hand van het verleden van Xt

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
115.6
120.3
121.9
121.7
118.9
113.4
114
117.5
120.9
125.1
124.7
128.2
149.7
163.6
173.9
164.5
154.2
147.9
159.3
170.3
170
174.2
190.8
179.9
240.8
241.9
241.1
239.6
220.8
209.3
209.9
228.3
242.1
226.4
231.5
229.7
257.6
260
264.4
268.8
271.4
273.8
277.4
268.2
264.6
266.6
266
267.4
289.8
294
310.3
311.7
302.1
298.2
299.2
296.2
299
300
299.4
300.2
470.2
472.1
484.8
513.4
547.2
548.1
544.7
521.1
459
413.2
Dataseries Y:
Download CSV
Histogram 
94,6
97,9
101,7
102,3
103,5
103,6
102,3
101,6
104,3
110,8
112,1
111,1
114,4
115
115,3
114,1
114,8
114,5
114,1
112,3
113
112,2
113,7
113,6
115,8
117,9
120,1
118,8
114,7
110,9
112,9
113,3
114,3
116,5
114,3
115,9
120,1
122,6
122,4
123,1
127,9
130,9
135
134,9
130,2
130,8
132,6
138,6
146,2
149,3
149,9
156,8
158,8
156,7
159,9
158,2
157,5
159,1
160,6
161,6
161,3
159,7
162,4
161,4
161,7
164,3
165,4
156,6
151,5
131,3

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=27959&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=27959&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27959&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 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])
-150.266686171503725
-140.281505257723181
-130.295834075388848
-120.310170474399703
-110.324260796610293
-100.334711719170006
-90.348974708369368
-80.387027946420528
-70.433212547609619
-60.490796116986623
-50.548572323323544
-40.610454387831522
-30.675578090664841
-20.742472757868459
-10.804006973631297
00.856542140887881
10.866514893060285
20.852205646130973
30.829149535376378
40.798634072415196
50.770046254958193
60.743852442745806
70.714494781254792
80.687882270527416
90.661392896979915
100.626750753901257
110.589343132011251
120.549800052619305
130.504520977671316
140.455301624094565
150.404083194912802

\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
-15 & 0.266686171503725 \tabularnewline
-14 & 0.281505257723181 \tabularnewline
-13 & 0.295834075388848 \tabularnewline
-12 & 0.310170474399703 \tabularnewline
-11 & 0.324260796610293 \tabularnewline
-10 & 0.334711719170006 \tabularnewline
-9 & 0.348974708369368 \tabularnewline
-8 & 0.387027946420528 \tabularnewline
-7 & 0.433212547609619 \tabularnewline
-6 & 0.490796116986623 \tabularnewline
-5 & 0.548572323323544 \tabularnewline
-4 & 0.610454387831522 \tabularnewline
-3 & 0.675578090664841 \tabularnewline
-2 & 0.742472757868459 \tabularnewline
-1 & 0.804006973631297 \tabularnewline
0 & 0.856542140887881 \tabularnewline
1 & 0.866514893060285 \tabularnewline
2 & 0.852205646130973 \tabularnewline
3 & 0.829149535376378 \tabularnewline
4 & 0.798634072415196 \tabularnewline
5 & 0.770046254958193 \tabularnewline
6 & 0.743852442745806 \tabularnewline
7 & 0.714494781254792 \tabularnewline
8 & 0.687882270527416 \tabularnewline
9 & 0.661392896979915 \tabularnewline
10 & 0.626750753901257 \tabularnewline
11 & 0.589343132011251 \tabularnewline
12 & 0.549800052619305 \tabularnewline
13 & 0.504520977671316 \tabularnewline
14 & 0.455301624094565 \tabularnewline
15 & 0.404083194912802 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27959&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]-15[/C][C]0.266686171503725[/C][/ROW]
[ROW][C]-14[/C][C]0.281505257723181[/C][/ROW]
[ROW][C]-13[/C][C]0.295834075388848[/C][/ROW]
[ROW][C]-12[/C][C]0.310170474399703[/C][/ROW]
[ROW][C]-11[/C][C]0.324260796610293[/C][/ROW]
[ROW][C]-10[/C][C]0.334711719170006[/C][/ROW]
[ROW][C]-9[/C][C]0.348974708369368[/C][/ROW]
[ROW][C]-8[/C][C]0.387027946420528[/C][/ROW]
[ROW][C]-7[/C][C]0.433212547609619[/C][/ROW]
[ROW][C]-6[/C][C]0.490796116986623[/C][/ROW]
[ROW][C]-5[/C][C]0.548572323323544[/C][/ROW]
[ROW][C]-4[/C][C]0.610454387831522[/C][/ROW]
[ROW][C]-3[/C][C]0.675578090664841[/C][/ROW]
[ROW][C]-2[/C][C]0.742472757868459[/C][/ROW]
[ROW][C]-1[/C][C]0.804006973631297[/C][/ROW]
[ROW][C]0[/C][C]0.856542140887881[/C][/ROW]
[ROW][C]1[/C][C]0.866514893060285[/C][/ROW]
[ROW][C]2[/C][C]0.852205646130973[/C][/ROW]
[ROW][C]3[/C][C]0.829149535376378[/C][/ROW]
[ROW][C]4[/C][C]0.798634072415196[/C][/ROW]
[ROW][C]5[/C][C]0.770046254958193[/C][/ROW]
[ROW][C]6[/C][C]0.743852442745806[/C][/ROW]
[ROW][C]7[/C][C]0.714494781254792[/C][/ROW]
[ROW][C]8[/C][C]0.687882270527416[/C][/ROW]
[ROW][C]9[/C][C]0.661392896979915[/C][/ROW]
[ROW][C]10[/C][C]0.626750753901257[/C][/ROW]
[ROW][C]11[/C][C]0.589343132011251[/C][/ROW]
[ROW][C]12[/C][C]0.549800052619305[/C][/ROW]
[ROW][C]13[/C][C]0.504520977671316[/C][/ROW]
[ROW][C]14[/C][C]0.455301624094565[/C][/ROW]
[ROW][C]15[/C][C]0.404083194912802[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27959&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27959&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])
-150.266686171503725
-140.281505257723181
-130.295834075388848
-120.310170474399703
-110.324260796610293
-100.334711719170006
-90.348974708369368
-80.387027946420528
-70.433212547609619
-60.490796116986623
-50.548572323323544
-40.610454387831522
-30.675578090664841
-20.742472757868459
-10.804006973631297
00.856542140887881
10.866514893060285
20.852205646130973
30.829149535376378
40.798634072415196
50.770046254958193
60.743852442745806
70.714494781254792
80.687882270527416
90.661392896979915
100.626750753901257
110.589343132011251
120.549800052619305
130.504520977671316
140.455301624094565
150.404083194912802


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')