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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 05:55:39 -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/t1228308986c3flra29u4uxjtq.htm/, Retrieved Sun, 19 May 2024 07:10:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28675, Retrieved Sun, 19 May 2024 07:10:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact178

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Cross Correlation Function] [NonStationaryTime...] [2008-12-03 12:29:30] [bd6e9fd01b4fddda83ee6fb85abada8c]
F   PD    [Cross Correlation Function] [NonStationaryTime...] [2008-12-03 12:55:39] [a413cf7744efd6bb212437a3916e2f23] [Current]
Feedback Forum
2008-12-08 14:34:06 [An Knapen] [reply] 
2008-12-08 14:42:59 [An Knapen] [reply] 
In q7 wordt er gebruik gemaakt van de cross correlation. Deze methode wordt gebruikt om het verband te onderzoeken tussen twee verschillende variabelen. Welke variabelen de student gebruikt heeft, is echter niet duidelijk. Wel kunnen we opmerken dat de grafiek enkel een positieve cross correlatie toont. Bovendien steken de meeste staafje ook boven het betrouwbaarheidsinterval uit.
Dit wijst erop dat we met behulp van de waarden van x(zowel de huidige al de verleden waarden) de toekomstige waarden van y kunnen voorspellen.
2008-12-08 18:53:42 [Sofie Sergoynne] [reply] 
De Cross Correlation is een methode die gebruikt wordt om het verband te onderzoeken tussen twee variabelen. De figuur toont een positieve cross correlatie. Dus de waarden van x kunnen helpen om de waarden van y te voorspellen.
2008-12-08 19:24:17 [Ellen Van den Broeck] [reply] 
De cross correlation functie berekent de correlatie tussen Yt en Yt+k
Bij getallen < 0 : correlatie tussen het heden van Yt en het verleden van Xt
Bij getallen >0: correlatie tussen het heden van Yt en de toekomst van Xt

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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 time0 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 & 0 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28675&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]0 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=28675&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28675&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 time0 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 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])
-140.091062451832251
-130.114164432712352
-120.338642178930999
-110.190778653024895
-100.0680365207060066
-90.235781056049751
-80.25386827374947
-70.257011497834459
-60.426515412788489
-50.437375881346418
-40.473528245159161
-30.578335238424379
-20.475078589518529
-10.540497438190923
00.918930870190701
10.585218804254892
20.422717487607173
30.514327423185198
40.388374617205521
50.383528251140422
60.419864855092944
70.258131707568095
80.290507590248964
90.275363068978822
100.110373764596797
110.171811692862883
120.339518858492246
130.166076079496808
140.0406233028212314

\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 & 0.091062451832251 \tabularnewline
-13 & 0.114164432712352 \tabularnewline
-12 & 0.338642178930999 \tabularnewline
-11 & 0.190778653024895 \tabularnewline
-10 & 0.0680365207060066 \tabularnewline
-9 & 0.235781056049751 \tabularnewline
-8 & 0.25386827374947 \tabularnewline
-7 & 0.257011497834459 \tabularnewline
-6 & 0.426515412788489 \tabularnewline
-5 & 0.437375881346418 \tabularnewline
-4 & 0.473528245159161 \tabularnewline
-3 & 0.578335238424379 \tabularnewline
-2 & 0.475078589518529 \tabularnewline
-1 & 0.540497438190923 \tabularnewline
0 & 0.918930870190701 \tabularnewline
1 & 0.585218804254892 \tabularnewline
2 & 0.422717487607173 \tabularnewline
3 & 0.514327423185198 \tabularnewline
4 & 0.388374617205521 \tabularnewline
5 & 0.383528251140422 \tabularnewline
6 & 0.419864855092944 \tabularnewline
7 & 0.258131707568095 \tabularnewline
8 & 0.290507590248964 \tabularnewline
9 & 0.275363068978822 \tabularnewline
10 & 0.110373764596797 \tabularnewline
11 & 0.171811692862883 \tabularnewline
12 & 0.339518858492246 \tabularnewline
13 & 0.166076079496808 \tabularnewline
14 & 0.0406233028212314 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28675&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]0.091062451832251[/C][/ROW]
[ROW][C]-13[/C][C]0.114164432712352[/C][/ROW]
[ROW][C]-12[/C][C]0.338642178930999[/C][/ROW]
[ROW][C]-11[/C][C]0.190778653024895[/C][/ROW]
[ROW][C]-10[/C][C]0.0680365207060066[/C][/ROW]
[ROW][C]-9[/C][C]0.235781056049751[/C][/ROW]
[ROW][C]-8[/C][C]0.25386827374947[/C][/ROW]
[ROW][C]-7[/C][C]0.257011497834459[/C][/ROW]
[ROW][C]-6[/C][C]0.426515412788489[/C][/ROW]
[ROW][C]-5[/C][C]0.437375881346418[/C][/ROW]
[ROW][C]-4[/C][C]0.473528245159161[/C][/ROW]
[ROW][C]-3[/C][C]0.578335238424379[/C][/ROW]
[ROW][C]-2[/C][C]0.475078589518529[/C][/ROW]
[ROW][C]-1[/C][C]0.540497438190923[/C][/ROW]
[ROW][C]0[/C][C]0.918930870190701[/C][/ROW]
[ROW][C]1[/C][C]0.585218804254892[/C][/ROW]
[ROW][C]2[/C][C]0.422717487607173[/C][/ROW]
[ROW][C]3[/C][C]0.514327423185198[/C][/ROW]
[ROW][C]4[/C][C]0.388374617205521[/C][/ROW]
[ROW][C]5[/C][C]0.383528251140422[/C][/ROW]
[ROW][C]6[/C][C]0.419864855092944[/C][/ROW]
[ROW][C]7[/C][C]0.258131707568095[/C][/ROW]
[ROW][C]8[/C][C]0.290507590248964[/C][/ROW]
[ROW][C]9[/C][C]0.275363068978822[/C][/ROW]
[ROW][C]10[/C][C]0.110373764596797[/C][/ROW]
[ROW][C]11[/C][C]0.171811692862883[/C][/ROW]
[ROW][C]12[/C][C]0.339518858492246[/C][/ROW]
[ROW][C]13[/C][C]0.166076079496808[/C][/ROW]
[ROW][C]14[/C][C]0.0406233028212314[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28675&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28675&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])
-140.091062451832251
-130.114164432712352
-120.338642178930999
-110.190778653024895
-100.0680365207060066
-90.235781056049751
-80.25386827374947
-70.257011497834459
-60.426515412788489
-50.437375881346418
-40.473528245159161
-30.578335238424379
-20.475078589518529
-10.540497438190923
00.918930870190701
10.585218804254892
20.422717487607173
30.514327423185198
40.388374617205521
50.383528251140422
60.419864855092944
70.258131707568095
80.290507590248964
90.275363068978822
100.110373764596797
110.171811692862883
120.339518858492246
130.166076079496808
140.0406233028212314


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')