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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 12:52:09 -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/t1228247599k664pth7wbj22if.htm/, Retrieved Sat, 25 May 2024 09:59:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28293, Retrieved Sat, 25 May 2024 09:59:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact166

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  [(Partial) Autocorrelation Function] [nonstationaryques...] [2008-12-01 19:09:36] [922d8ae7bd2fd460a62d9020ccd4931a]
F RMPD    [Cross Correlation Function] [nonstationaryques...] [2008-12-02 17:59:36] [922d8ae7bd2fd460a62d9020ccd4931a]
F   P       [Cross Correlation Function] [nonstationaryques...] [2008-12-02 19:43:19] [922d8ae7bd2fd460a62d9020ccd4931a]
F   PD          [Cross Correlation Function] [nonstationaryques...] [2008-12-02 19:52:09] [89a49ebb3ece8e9a225c7f9f53a14c57] [Current]
Feedback Forum
2008-12-07 10:37:02 [6066575aa30c0611e452e930b1dff53d] [reply] 
Hier werd de cross correlation function weergegeven voor d=0 en D=1. Uit de tabel kunnen we afleiden dat voor k=0 de correlatie tussen Y[t] en X[t] zonder verschuiving in de tijd 0.186398467884646 bedraagt. Dit is toch een groot verschil met Q7 waar de correlatie 0.68 bedroeg. Bij de grafiek van de cross correlation function werd vermeld dat er nog enkele verticale lijnen zijn die buiten het 95% betrouwbaarheidsinterval liggen. Deze zijn significant verschillend van nul. Deze grafiek geeft een veel betrouwbaarder beeld dan Q7. Bovendien waren bij Q7 het merendeel van de verticale lijnen positief en bij Q9 zijn het merendeel van de verticale lijnen negatief.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
78.4
114.6
113.3
117
99.6
99.4
101.9
115.2
108.5
113.8
121
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
106.6
90.1
96.9
125.9
112
100
123.9
79.8
83.4
113.6
112.9
104
109.9
99
106.3
128.9
111.1
102.9
130
87
87.5
117.6
103.4
110.8
112.6
102.5
112.4
135.6
105.1
127.7
137
91
90.5
122.4
123.3
124.3
120
118.1
119
142.7
123.6
129.6
151.6
110.4
99.2
130.5
136.2
129.7
128
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
104.3
100.6
101.1
103.9
96.9
95.5
108.4
117
103.8
100.8
110.6
104
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
112.4
130.6
109.1
118.8
123.9
101.6
112.8
128
129.6
125.8
119.5
115.7
113.6
129.7
112
116.8
127
112.1
114.2
121.1
131.6
125
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 time2 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28293&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]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28293&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28293&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'Herman Ole Andreas Wold' @ 193.190.124.10:1001






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 series1
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 series1
krho(Y[t],X[t+k])
-15-0.232076030090473
-140.0221056358634002
-13-0.156213644968388
-12-0.179833897337554
-11-0.271798219564566
-10-0.3712211748386
-9-0.147607558375362
-8-0.166371857394835
-7-0.202565687582196
-6-0.0639265469010754
-5-0.131312154695956
-4-0.349397965820122
-30.00298988527910823
-2-0.244477268313234
-1-0.413680274429516
00.186398467884646
1-0.196350016247539
2-0.0759163140046513
30.189339916758316
4-0.0129380515890599
5-0.135448653898829
60.0438618681622579
7-0.157854268060480
80.0484750466643336
90.0685716335285924
10-0.0547416249029467
110.0340785559529833
120.102232748404375
13-0.00189966329516644
140.0652213700657713
15-0.0508450174726143

\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 & 1 \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 & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-15 & -0.232076030090473 \tabularnewline
-14 & 0.0221056358634002 \tabularnewline
-13 & -0.156213644968388 \tabularnewline
-12 & -0.179833897337554 \tabularnewline
-11 & -0.271798219564566 \tabularnewline
-10 & -0.3712211748386 \tabularnewline
-9 & -0.147607558375362 \tabularnewline
-8 & -0.166371857394835 \tabularnewline
-7 & -0.202565687582196 \tabularnewline
-6 & -0.0639265469010754 \tabularnewline
-5 & -0.131312154695956 \tabularnewline
-4 & -0.349397965820122 \tabularnewline
-3 & 0.00298988527910823 \tabularnewline
-2 & -0.244477268313234 \tabularnewline
-1 & -0.413680274429516 \tabularnewline
0 & 0.186398467884646 \tabularnewline
1 & -0.196350016247539 \tabularnewline
2 & -0.0759163140046513 \tabularnewline
3 & 0.189339916758316 \tabularnewline
4 & -0.0129380515890599 \tabularnewline
5 & -0.135448653898829 \tabularnewline
6 & 0.0438618681622579 \tabularnewline
7 & -0.157854268060480 \tabularnewline
8 & 0.0484750466643336 \tabularnewline
9 & 0.0685716335285924 \tabularnewline
10 & -0.0547416249029467 \tabularnewline
11 & 0.0340785559529833 \tabularnewline
12 & 0.102232748404375 \tabularnewline
13 & -0.00189966329516644 \tabularnewline
14 & 0.0652213700657713 \tabularnewline
15 & -0.0508450174726143 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28293&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]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]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]-0.232076030090473[/C][/ROW]
[ROW][C]-14[/C][C]0.0221056358634002[/C][/ROW]
[ROW][C]-13[/C][C]-0.156213644968388[/C][/ROW]
[ROW][C]-12[/C][C]-0.179833897337554[/C][/ROW]
[ROW][C]-11[/C][C]-0.271798219564566[/C][/ROW]
[ROW][C]-10[/C][C]-0.3712211748386[/C][/ROW]
[ROW][C]-9[/C][C]-0.147607558375362[/C][/ROW]
[ROW][C]-8[/C][C]-0.166371857394835[/C][/ROW]
[ROW][C]-7[/C][C]-0.202565687582196[/C][/ROW]
[ROW][C]-6[/C][C]-0.0639265469010754[/C][/ROW]
[ROW][C]-5[/C][C]-0.131312154695956[/C][/ROW]
[ROW][C]-4[/C][C]-0.349397965820122[/C][/ROW]
[ROW][C]-3[/C][C]0.00298988527910823[/C][/ROW]
[ROW][C]-2[/C][C]-0.244477268313234[/C][/ROW]
[ROW][C]-1[/C][C]-0.413680274429516[/C][/ROW]
[ROW][C]0[/C][C]0.186398467884646[/C][/ROW]
[ROW][C]1[/C][C]-0.196350016247539[/C][/ROW]
[ROW][C]2[/C][C]-0.0759163140046513[/C][/ROW]
[ROW][C]3[/C][C]0.189339916758316[/C][/ROW]
[ROW][C]4[/C][C]-0.0129380515890599[/C][/ROW]
[ROW][C]5[/C][C]-0.135448653898829[/C][/ROW]
[ROW][C]6[/C][C]0.0438618681622579[/C][/ROW]
[ROW][C]7[/C][C]-0.157854268060480[/C][/ROW]
[ROW][C]8[/C][C]0.0484750466643336[/C][/ROW]
[ROW][C]9[/C][C]0.0685716335285924[/C][/ROW]
[ROW][C]10[/C][C]-0.0547416249029467[/C][/ROW]
[ROW][C]11[/C][C]0.0340785559529833[/C][/ROW]
[ROW][C]12[/C][C]0.102232748404375[/C][/ROW]
[ROW][C]13[/C][C]-0.00189966329516644[/C][/ROW]
[ROW][C]14[/C][C]0.0652213700657713[/C][/ROW]
[ROW][C]15[/C][C]-0.0508450174726143[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28293&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28293&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 series1
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 series1
krho(Y[t],X[t+k])
-15-0.232076030090473
-140.0221056358634002
-13-0.156213644968388
-12-0.179833897337554
-11-0.271798219564566
-10-0.3712211748386
-9-0.147607558375362
-8-0.166371857394835
-7-0.202565687582196
-6-0.0639265469010754
-5-0.131312154695956
-4-0.349397965820122
-30.00298988527910823
-2-0.244477268313234
-1-0.413680274429516
00.186398467884646
1-0.196350016247539
2-0.0759163140046513
30.189339916758316
4-0.0129380515890599
5-0.135448653898829
60.0438618681622579
7-0.157854268060480
80.0484750466643336
90.0685716335285924
10-0.0547416249029467
110.0340785559529833
120.102232748404375
13-0.00189966329516644
140.0652213700657713
15-0.0508450174726143


Figures (Output of Computation)

Input Parameters & R Code

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