FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 13:22:16 -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/t1228249386r4qz09r6tum8v6w.htm/, Retrieved Sun, 19 May 2024 11:34:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28371, Retrieved Sun, 19 May 2024 11:34:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact159

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] [b1bd16d1f47bfe13feacf1c27a0abba5]
F   P               [Cross Correlation Function] [NonStationaryTime...] [2008-12-02 20:22:16] [ff1f39dba9ec26bf89aa666d9dcb6cc1] [Current]
Feedback Forum
2008-12-06 09:50:17 [Angelique Van de Vijver] [reply] 
Foute berekening van de student.
De student heeft Q8 niet uitgevoerd en kan dus ook niet weten welke waarden zij moet gebruiken voor lambda, d en D. In Q8 moest je op zoek gaan naar de parameters die leiden tot een stationaire tijdreeks en die je dus in deze opgave moest invullen in de berekening. Ze heeft dus eigenlijk gegokt naar de waarden van de parameters denk ik dan.
Wel goede uitleg van de student van wat juist de partiële correlatie doet. Je gaat dus inderdaad de 3e variabele elimineren om dan het zuiver verband te berekenen tussen X en Y.
Normaal gezien zal de kruiscorrelatie na de correcte differentiatie een beter beeld geven dan de kruiscorrelatie zonder differentiatie(Q7) omdat nonsens-correlaties worden geëlimineerd. Het elimineert de nonsens-verbanden. Dit geeft dus een betrouwbaarder beeld dan in Q7.
De student heeft ook niks vermeld over de k-waarde. Als deze k-waarde negatief is dan kan je het verleden gebruiken van X om Y te voorspellen. Als deze k-waarde positief is dan kan je Y voorspellen m.b.v. de toekomst van X.
Een leading indicator is een voorspellende waarde, iets dat vooroploopt. Het is een indicator die je op voorhand zegt wat het verloop van een andere variabele is.
2008-12-08 19:47:03 [Stef Vermeiren] [reply] 
De student heeft wel de juiste grafiek gebruikt, maar kan deze echter niet oplossen aangezien de student Q8 niet heeft gemaakt. Voor deze vraag is lambda, d en D nodig.

Post a new message

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=28371&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=28371&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28371&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=28371&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=28371&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28371&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 = 0.7 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; par5 = -0.1 ; par6 = 0 ; par7 = 1 ;
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')