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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 computationTue, 02 Dec 2008 11:20:14 -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/02/t1228242071q0jf6ubl85gnqe6.htm/, Retrieved Fri, 17 May 2024 03:41:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28211, Retrieved Fri, 17 May 2024 03:41:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact172

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [(Partial) Autocorrelation Function] [nsts Q8 (1)] [2008-12-02 16:54:05] [b1bd16d1f47bfe13feacf1c27a0abba5]
F   PD  [(Partial) Autocorrelation Function] [nsts Q8 (2)] [2008-12-02 16:58:46] [b1bd16d1f47bfe13feacf1c27a0abba5]
F   PD    [(Partial) Autocorrelation Function] [nsts Q8 (5)] [2008-12-02 17:09:15] [b1bd16d1f47bfe13feacf1c27a0abba5]
-   PD      [(Partial) Autocorrelation Function] [nsts Q8 (6)] [2008-12-02 17:12:08] [b1bd16d1f47bfe13feacf1c27a0abba5]
-   P         [(Partial) Autocorrelation Function] [nsts Q8 (7)] [2008-12-02 17:14:26] [b1bd16d1f47bfe13feacf1c27a0abba5]
F RMPD            [Cross Correlation Function] [nsts Q9] [2008-12-02 18:20:14] [e7b1048c2c3a353441b9143db4404b91] [Current]
F   P               [Cross Correlation Function] [NonStationaryTime...] [2008-12-02 20:22:16] [9c2d53170eb755e9ae5fcf19d2174a32]
Feedback Forum
2008-12-08 19:36:30 [Jasmine Hendrikx] [reply] 
Eigen evaluatie:
De berekening is goed uitgevoerd en de conclusie is juist, alleen moet er wel opgemerkt worden dat de gebruikte lambda’s die berekend zijn in Q8, niet echt correct zijn, aangezien er geen significant verband was tussen de standaardafwijking en het gemiddelde. Bij de conclusie zou er ook nog vermeld kunnen worden dat er eerst veel significante verschillen waren (voor de transformatie). Zowel links als rechts was dit het geval (simultaan effect). Nu (na transformatie) zijn deze significante verschillen in grote mate verdwenen. Dit is een typisch fenomeen. Na transformatie zie je ofwel niets meer of véél minder significante verschillen. De verklaring hiervoor is terug te vinden in partiële correlatie. De correlatie tussen Xt enYt kan vertekend worden indien er een andere variabele is die zowel Yt als Xt sterk beïnvloedt. Zt is dus duidelijk aanwezig in de 2 tijdreeksen. De 2 reeksen vertonen dezelfde trend. Er is autocorrelatie bij de twee reeksen aanwezig. Als we de trend uit X en Y halen, zullen we dus een veel zuiverder beeld zien. Q9 geeft dus een veel betrouwbaarder beeld dan Q7. We kunnen dus eigenlijk spreken van een nonsenscorrelatie tussen Xt en Yt.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
78.4
114.6
113.3
117.0
99.6
99.4
101.9
115.2
108.5
113.8
121.0
92.2
90.2
101.5
126.6
93.9
89.8
93.4
101.5
110.4
105.9
108.4
113.9
86.1
69.4
101.2
100.5
98.0
106.6
90.1
96.9
125.9
112.0
100.0
123.9
79.8
83.4
113.6
112.9
104.0
109.9
99.0
106.3
128.9
111.1
102.9
130.0
87.0
87.5
117.6
103.4
110.8
112.6
102.5
112.4
135.6
105.1
127.7
137.0
91.0
90.5
122.4
123.3
124.3
120.0
118.1
119.0
142.7
123.6
129.6
151.6
110.4
99.2
130.5
136.2
129.7
128.0
121.6
135.8
143.8
147.5
136.2
156.6
123.3
100.4
Dataseries Y:
Download CSV
Histogram 
97.8
107.4
117.5
105.6
97.4
99.5
98.0
104.3
100.6
101.1
103.9
96.9
95.5
108.4
117.0
103.8
100.8
110.6
104.0
112.6
107.3
98.9
109.8
104.9
102.2
123.9
124.9
112.7
121.9
100.6
104.3
120.4
107.5
102.9
125.6
107.5
108.8
128.4
121.1
119.5
128.7
108.7
105.5
119.8
111.3
110.6
120.1
97.5
107.7
127.3
117.2
119.8
116.2
111.0
112.4
130.6
109.1
118.8
123.9
101.6
112.8
128.0
129.6
125.8
119.5
115.7
113.6
129.7
112.0
116.8
127.0
112.1
114.2
121.1
131.6
125.0
120.4
117.7
117.5
120.6
127.5
112.3
124.5
115.2
105.4

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28211&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28211&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28211&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series0.7
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series-0.1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-15-0.234665258317703
-140.0450668264040533
-13-0.150990784941998
-12-0.181560644421177
-11-0.269976449745632
-10-0.379060314595545
-9-0.156562743116524
-8-0.160065901784753
-7-0.208639884581659
-6-0.0640643727475325
-5-0.117923216905254
-4-0.362823143366015
-3-0.00565070442571559
-2-0.261504531118573
-1-0.433199663823933
00.162579847259802
1-0.191548244036693
2-0.0993781779010656
30.188473924709413
4-0.0194745031440052
5-0.133967722949912
60.0244758078056301
7-0.179377810279355
80.0440227844477406
90.0690441709646122
10-0.0610356393983486
110.0452773652094639
120.105156030015724
13-7.25202823352786e-05
140.0815866812890361
15-0.0607030661686997

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 0.7 \tabularnewline
Degree of non-seasonal differencing (d) of X series & 0 \tabularnewline
Degree of seasonal differencing (D) of X series & 1 \tabularnewline
Seasonal Period (s) & 12 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & -0.1 \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
-15 & -0.234665258317703 \tabularnewline
-14 & 0.0450668264040533 \tabularnewline
-13 & -0.150990784941998 \tabularnewline
-12 & -0.181560644421177 \tabularnewline
-11 & -0.269976449745632 \tabularnewline
-10 & -0.379060314595545 \tabularnewline
-9 & -0.156562743116524 \tabularnewline
-8 & -0.160065901784753 \tabularnewline
-7 & -0.208639884581659 \tabularnewline
-6 & -0.0640643727475325 \tabularnewline
-5 & -0.117923216905254 \tabularnewline
-4 & -0.362823143366015 \tabularnewline
-3 & -0.00565070442571559 \tabularnewline
-2 & -0.261504531118573 \tabularnewline
-1 & -0.433199663823933 \tabularnewline
0 & 0.162579847259802 \tabularnewline
1 & -0.191548244036693 \tabularnewline
2 & -0.0993781779010656 \tabularnewline
3 & 0.188473924709413 \tabularnewline
4 & -0.0194745031440052 \tabularnewline
5 & -0.133967722949912 \tabularnewline
6 & 0.0244758078056301 \tabularnewline
7 & -0.179377810279355 \tabularnewline
8 & 0.0440227844477406 \tabularnewline
9 & 0.0690441709646122 \tabularnewline
10 & -0.0610356393983486 \tabularnewline
11 & 0.0452773652094639 \tabularnewline
12 & 0.105156030015724 \tabularnewline
13 & -7.25202823352786e-05 \tabularnewline
14 & 0.0815866812890361 \tabularnewline
15 & -0.0607030661686997 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28211&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]0.7[/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]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]-0.1[/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]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-15[/C][C]-0.234665258317703[/C][/ROW]
[ROW][C]-14[/C][C]0.0450668264040533[/C][/ROW]
[ROW][C]-13[/C][C]-0.150990784941998[/C][/ROW]
[ROW][C]-12[/C][C]-0.181560644421177[/C][/ROW]
[ROW][C]-11[/C][C]-0.269976449745632[/C][/ROW]
[ROW][C]-10[/C][C]-0.379060314595545[/C][/ROW]
[ROW][C]-9[/C][C]-0.156562743116524[/C][/ROW]
[ROW][C]-8[/C][C]-0.160065901784753[/C][/ROW]
[ROW][C]-7[/C][C]-0.208639884581659[/C][/ROW]
[ROW][C]-6[/C][C]-0.0640643727475325[/C][/ROW]
[ROW][C]-5[/C][C]-0.117923216905254[/C][/ROW]
[ROW][C]-4[/C][C]-0.362823143366015[/C][/ROW]
[ROW][C]-3[/C][C]-0.00565070442571559[/C][/ROW]
[ROW][C]-2[/C][C]-0.261504531118573[/C][/ROW]
[ROW][C]-1[/C][C]-0.433199663823933[/C][/ROW]
[ROW][C]0[/C][C]0.162579847259802[/C][/ROW]
[ROW][C]1[/C][C]-0.191548244036693[/C][/ROW]
[ROW][C]2[/C][C]-0.0993781779010656[/C][/ROW]
[ROW][C]3[/C][C]0.188473924709413[/C][/ROW]
[ROW][C]4[/C][C]-0.0194745031440052[/C][/ROW]
[ROW][C]5[/C][C]-0.133967722949912[/C][/ROW]
[ROW][C]6[/C][C]0.0244758078056301[/C][/ROW]
[ROW][C]7[/C][C]-0.179377810279355[/C][/ROW]
[ROW][C]8[/C][C]0.0440227844477406[/C][/ROW]
[ROW][C]9[/C][C]0.0690441709646122[/C][/ROW]
[ROW][C]10[/C][C]-0.0610356393983486[/C][/ROW]
[ROW][C]11[/C][C]0.0452773652094639[/C][/ROW]
[ROW][C]12[/C][C]0.105156030015724[/C][/ROW]
[ROW][C]13[/C][C]-7.25202823352786e-05[/C][/ROW]
[ROW][C]14[/C][C]0.0815866812890361[/C][/ROW]
[ROW][C]15[/C][C]-0.0607030661686997[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28211&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28211&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 series0.7
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series-0.1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-15-0.234665258317703
-140.0450668264040533
-13-0.150990784941998
-12-0.181560644421177
-11-0.269976449745632
-10-0.379060314595545
-9-0.156562743116524
-8-0.160065901784753
-7-0.208639884581659
-6-0.0640643727475325
-5-0.117923216905254
-4-0.362823143366015
-3-0.00565070442571559
-2-0.261504531118573
-1-0.433199663823933
00.162579847259802
1-0.191548244036693
2-0.0993781779010656
30.188473924709413
4-0.0194745031440052
5-0.133967722949912
60.0244758078056301
7-0.179377810279355
80.0440227844477406
90.0690441709646122
10-0.0610356393983486
110.0452773652094639
120.105156030015724
13-7.25202823352786e-05
140.0815866812890361
15-0.0607030661686997


Figures (Output of Computation)

Input Parameters & R Code

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