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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 09:41:08 -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/t1228236118ysi14whtyfys749.htm/, Retrieved Sun, 19 May 2024 09:23:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28060, Retrieved Sun, 19 May 2024 09:23:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact140

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] [] [2008-12-02 16:41:08] [76e580e81b2082744334eb1f6d9ccc3e] [Current]
Feedback Forum
2008-12-06 15:00:09 [Maarten Van Gucht] [reply] 
Door de invloed van een mogelijke derde variabele weg te werken, ontstaat er een zuiverder verband tussen de variabele Xt en Yt. De lags die op de correlatieplot van voor de differentiatie voornamelijk uitstaken boven het betrouwbaarheidsinterval, liggen nu meestal mooi binnen dit 95% betrouwbaarheidsinterval.
we hebben het patroon en de seizoenaliteit weggewerkt.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
105.3
103
103.8
103.4
105.8
101.4
97
94.3
96.6
97.1
95.7
96.9
97.4
95.3
93.6
91.5
93.1
91.7
94.3
93.9
90.9
88.3
91.3
91.7
92.4
92
95.6
95.8
96.4
99
107
109.7
116.2
115.9
113.8
112.6
113.7
115.9
110.3
111.3
113.4
108.2
104.8
106
110.9
115
118.4
121.4
128.8
131.7
141.7
142.9
139.4
134.7
125
113.6
111.5
108.5
112.3
116.6
115.5
120.1
132.9
128.1
129.3
132.5
131
124.9
120.8
122
122.1
127.4
135.2
137.3
135
136
138.4
134.7
138.4
133.9
133.6
141.2
151.8
155.4
156.6
161.6
160.7
156
159.5
168.7
169.9
169.9
185.9
190.8
195.8
211.9
Dataseries Y:
Download CSV
Histogram 
103.1
102.5
101.3
99.5
99.4
98.8
99.9
99.9
101.2
97.7
97
99.5
100.3
98.5
95.1
93.1
92.2
89
86.4
84.5
82.7
80.8
81.8
81.8
82.9
83.8
86.2
86.1
86.2
88.8
89.6
87.8
88.3
88.6
91
91.5
95.4
98.7
99.9
98.6
100.3
100.2
100.4
101.4
103
109.1
111.4
114.1
121.8
127.6
129.9
128
123.5
124
127.4
127.6
128.4
131.4
135.1
134
144.5
147.3
150.9
148.7
141.4
138.9
139.8
145.6
147.9
148.5
151.1
157.5
167.5
172.3
173.5
187.5
205.5
195.1
204.5
204.5
201.7
207
206.6
210.6
211.1
215
223.9
238.2
238.9
229.6
232.2
222.1
221.6
227.3
221
213.6

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'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 & 0 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28060&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28060&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28060&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'Gwilym Jenkins' @ 72.249.127.135






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1.5
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series-0.3
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-160.182255210342458
-150.113182430500285
-14-0.0402570701644827
-130.0853590061414691
-12-0.0430945260790037
-11-0.154186429341644
-10-0.236552037432637
-9-0.163476648408766
-8-0.170521388254632
-7-0.121501290511442
-6-0.0113811250078510
-50.0970340476481396
-4-0.0453663054950068
-3-0.0501956796387408
-20.219993262104859
-1-0.0447821877702039
0-0.171794807367093
10.0498264721402481
2-0.0117911740810074
3-0.130539919551636
4-0.171497255963380
5-0.133545358415814
6-0.0150878771195381
70.106605687556967
80.151010049184689
90.148325582743142
10-0.0717431246498159
11-0.0431312640284508
120.198384362206355
130.0372767981824058
140.0920080728112015
150.072137669664533
160.0259870226977488

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 1.5 \tabularnewline
Degree of non-seasonal differencing (d) of X series & 1 \tabularnewline
Degree of seasonal differencing (D) of X series & 0 \tabularnewline
Seasonal Period (s) & 12 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & -0.3 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 1 \tabularnewline
Degree of seasonal differencing (D) of Y series & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-16 & 0.182255210342458 \tabularnewline
-15 & 0.113182430500285 \tabularnewline
-14 & -0.0402570701644827 \tabularnewline
-13 & 0.0853590061414691 \tabularnewline
-12 & -0.0430945260790037 \tabularnewline
-11 & -0.154186429341644 \tabularnewline
-10 & -0.236552037432637 \tabularnewline
-9 & -0.163476648408766 \tabularnewline
-8 & -0.170521388254632 \tabularnewline
-7 & -0.121501290511442 \tabularnewline
-6 & -0.0113811250078510 \tabularnewline
-5 & 0.0970340476481396 \tabularnewline
-4 & -0.0453663054950068 \tabularnewline
-3 & -0.0501956796387408 \tabularnewline
-2 & 0.219993262104859 \tabularnewline
-1 & -0.0447821877702039 \tabularnewline
0 & -0.171794807367093 \tabularnewline
1 & 0.0498264721402481 \tabularnewline
2 & -0.0117911740810074 \tabularnewline
3 & -0.130539919551636 \tabularnewline
4 & -0.171497255963380 \tabularnewline
5 & -0.133545358415814 \tabularnewline
6 & -0.0150878771195381 \tabularnewline
7 & 0.106605687556967 \tabularnewline
8 & 0.151010049184689 \tabularnewline
9 & 0.148325582743142 \tabularnewline
10 & -0.0717431246498159 \tabularnewline
11 & -0.0431312640284508 \tabularnewline
12 & 0.198384362206355 \tabularnewline
13 & 0.0372767981824058 \tabularnewline
14 & 0.0920080728112015 \tabularnewline
15 & 0.072137669664533 \tabularnewline
16 & 0.0259870226977488 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28060&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]1.5[/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]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]-0.3[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of Y series[/C][C]1[/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]-16[/C][C]0.182255210342458[/C][/ROW]
[ROW][C]-15[/C][C]0.113182430500285[/C][/ROW]
[ROW][C]-14[/C][C]-0.0402570701644827[/C][/ROW]
[ROW][C]-13[/C][C]0.0853590061414691[/C][/ROW]
[ROW][C]-12[/C][C]-0.0430945260790037[/C][/ROW]
[ROW][C]-11[/C][C]-0.154186429341644[/C][/ROW]
[ROW][C]-10[/C][C]-0.236552037432637[/C][/ROW]
[ROW][C]-9[/C][C]-0.163476648408766[/C][/ROW]
[ROW][C]-8[/C][C]-0.170521388254632[/C][/ROW]
[ROW][C]-7[/C][C]-0.121501290511442[/C][/ROW]
[ROW][C]-6[/C][C]-0.0113811250078510[/C][/ROW]
[ROW][C]-5[/C][C]0.0970340476481396[/C][/ROW]
[ROW][C]-4[/C][C]-0.0453663054950068[/C][/ROW]
[ROW][C]-3[/C][C]-0.0501956796387408[/C][/ROW]
[ROW][C]-2[/C][C]0.219993262104859[/C][/ROW]
[ROW][C]-1[/C][C]-0.0447821877702039[/C][/ROW]
[ROW][C]0[/C][C]-0.171794807367093[/C][/ROW]
[ROW][C]1[/C][C]0.0498264721402481[/C][/ROW]
[ROW][C]2[/C][C]-0.0117911740810074[/C][/ROW]
[ROW][C]3[/C][C]-0.130539919551636[/C][/ROW]
[ROW][C]4[/C][C]-0.171497255963380[/C][/ROW]
[ROW][C]5[/C][C]-0.133545358415814[/C][/ROW]
[ROW][C]6[/C][C]-0.0150878771195381[/C][/ROW]
[ROW][C]7[/C][C]0.106605687556967[/C][/ROW]
[ROW][C]8[/C][C]0.151010049184689[/C][/ROW]
[ROW][C]9[/C][C]0.148325582743142[/C][/ROW]
[ROW][C]10[/C][C]-0.0717431246498159[/C][/ROW]
[ROW][C]11[/C][C]-0.0431312640284508[/C][/ROW]
[ROW][C]12[/C][C]0.198384362206355[/C][/ROW]
[ROW][C]13[/C][C]0.0372767981824058[/C][/ROW]
[ROW][C]14[/C][C]0.0920080728112015[/C][/ROW]
[ROW][C]15[/C][C]0.072137669664533[/C][/ROW]
[ROW][C]16[/C][C]0.0259870226977488[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28060&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28060&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 series1.5
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series-0.3
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-160.182255210342458
-150.113182430500285
-14-0.0402570701644827
-130.0853590061414691
-12-0.0430945260790037
-11-0.154186429341644
-10-0.236552037432637
-9-0.163476648408766
-8-0.170521388254632
-7-0.121501290511442
-6-0.0113811250078510
-50.0970340476481396
-4-0.0453663054950068
-3-0.0501956796387408
-20.219993262104859
-1-0.0447821877702039
0-0.171794807367093
10.0498264721402481
2-0.0117911740810074
3-0.130539919551636
4-0.171497255963380
5-0.133545358415814
6-0.0150878771195381
70.106605687556967
80.151010049184689
90.148325582743142
10-0.0717431246498159
11-0.0431312640284508
120.198384362206355
130.0372767981824058
140.0920080728112015
150.072137669664533
160.0259870226977488


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1.5 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = -0.3 ; par6 = 1 ; par7 = 1 ;
Parameters (R input):
par1 = 1.5 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = -0.3 ; par6 = 1 ; 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')