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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 06:16:38 -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/t1228223835kmv014uvwds10zx.htm/, Retrieved Sun, 19 May 2024 11:39:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27727, Retrieved Sun, 19 May 2024 11:39:49 +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       [Cross Correlation Function] [question 7] [2008-12-02 13:16:38] [490fee4f334e2e025c95681783e3fd0b] [Current]
Feedback Forum
2008-12-08 16:03:50 [Alexander Hendrickx] [reply] 
De opdracht werd goed opgelost

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1.3322
1.4369
1.4975
1.577
1.5553
1.5557
1.575
1.5527
1.4748
1.4718
1.457
1.4684
1.4227
1.3896
1.3622
1.3716
1.3419
1.3511
1.3516
1.3242
1.3074
1.2999
1.3213
1.2881
1.2611
1.2727
1.2811
1.2684
1.265
1.277
1.2271
1.202
1.1938
1.2103
1.1856
1.1786
1.2015
1.2256
1.2292
1.2037
1.2165
1.2694
1.2938
1.3201
1.3014
1.3119
1.3408
1.2991
1.249
1.2218
1.2176
1.2266
1.2138
1.2007
1.1985
1.2262
1.2646
1.2613
1.2286
1.1702
1.1692
1.1222
1.1139
1.1372
1.1663
1.1582
1.0848
1.0807
1.0773
1.0622
1.0183
1.0014
0.9811
0.9808
Dataseries Y:
Download CSV
Histogram 
133.52
153.2
163.63
168.45
166.26
162.31
161.56
156.59
157.97
158.68
163.55
162.89
164.95
159.82
159.05
166.76
164.55
163.22
160.68
155.24
157.6
156.56
154.82
151.11
149.65
148.99
148.53
146.7
145.11
142.7
143.59
140.96
140.77
139.81
140.58
139.59
138.05
136.06
135.98
134.75
132.22
135.37
138.84
138.83
136.55
135.63
139.14
136.09
135.97
134.51
134.54
134.08
132.86
134.48
129.08
133.13
134.78
134.13
132.43
127.84
128.12
128.94
132.38
134.99
138.05
135.83
130.12
128.16
128.6
126.12
124.2
121.65
121.57
118.38

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=27727&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=27727&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27727&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 series1
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 series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-140.154626521444962
-130.0688653281957272
-12-0.107081344609825
-11-0.052677815968016
-100.127350420490514
-90.16559340597751
-80.0379974390111244
-7-0.0442542944945546
-60.157916211075150
-50.100034653230309
-4-0.00304828483493349
-3-0.112069057035788
-2-0.177856623295996
-10.162721271672286
00.521262047108371
10.362663068494834
20.0614374405897717
30.0139463685094387
4-0.0545031048928374
50.076324667721236
6-0.230803549326502
7-0.0755259808137603
80.0467646984584104
9-0.0383755908219416
100.0593857172834214
11-0.0710791466774877
12-0.075252322247026
13-0.119291755765396
14-0.0513612110218373

\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 & 1 \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 & 1 \tabularnewline
Degree of seasonal differencing (D) of Y series & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-14 & 0.154626521444962 \tabularnewline
-13 & 0.0688653281957272 \tabularnewline
-12 & -0.107081344609825 \tabularnewline
-11 & -0.052677815968016 \tabularnewline
-10 & 0.127350420490514 \tabularnewline
-9 & 0.16559340597751 \tabularnewline
-8 & 0.0379974390111244 \tabularnewline
-7 & -0.0442542944945546 \tabularnewline
-6 & 0.157916211075150 \tabularnewline
-5 & 0.100034653230309 \tabularnewline
-4 & -0.00304828483493349 \tabularnewline
-3 & -0.112069057035788 \tabularnewline
-2 & -0.177856623295996 \tabularnewline
-1 & 0.162721271672286 \tabularnewline
0 & 0.521262047108371 \tabularnewline
1 & 0.362663068494834 \tabularnewline
2 & 0.0614374405897717 \tabularnewline
3 & 0.0139463685094387 \tabularnewline
4 & -0.0545031048928374 \tabularnewline
5 & 0.076324667721236 \tabularnewline
6 & -0.230803549326502 \tabularnewline
7 & -0.0755259808137603 \tabularnewline
8 & 0.0467646984584104 \tabularnewline
9 & -0.0383755908219416 \tabularnewline
10 & 0.0593857172834214 \tabularnewline
11 & -0.0710791466774877 \tabularnewline
12 & -0.075252322247026 \tabularnewline
13 & -0.119291755765396 \tabularnewline
14 & -0.0513612110218373 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27727&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]1[/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]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]-14[/C][C]0.154626521444962[/C][/ROW]
[ROW][C]-13[/C][C]0.0688653281957272[/C][/ROW]
[ROW][C]-12[/C][C]-0.107081344609825[/C][/ROW]
[ROW][C]-11[/C][C]-0.052677815968016[/C][/ROW]
[ROW][C]-10[/C][C]0.127350420490514[/C][/ROW]
[ROW][C]-9[/C][C]0.16559340597751[/C][/ROW]
[ROW][C]-8[/C][C]0.0379974390111244[/C][/ROW]
[ROW][C]-7[/C][C]-0.0442542944945546[/C][/ROW]
[ROW][C]-6[/C][C]0.157916211075150[/C][/ROW]
[ROW][C]-5[/C][C]0.100034653230309[/C][/ROW]
[ROW][C]-4[/C][C]-0.00304828483493349[/C][/ROW]
[ROW][C]-3[/C][C]-0.112069057035788[/C][/ROW]
[ROW][C]-2[/C][C]-0.177856623295996[/C][/ROW]
[ROW][C]-1[/C][C]0.162721271672286[/C][/ROW]
[ROW][C]0[/C][C]0.521262047108371[/C][/ROW]
[ROW][C]1[/C][C]0.362663068494834[/C][/ROW]
[ROW][C]2[/C][C]0.0614374405897717[/C][/ROW]
[ROW][C]3[/C][C]0.0139463685094387[/C][/ROW]
[ROW][C]4[/C][C]-0.0545031048928374[/C][/ROW]
[ROW][C]5[/C][C]0.076324667721236[/C][/ROW]
[ROW][C]6[/C][C]-0.230803549326502[/C][/ROW]
[ROW][C]7[/C][C]-0.0755259808137603[/C][/ROW]
[ROW][C]8[/C][C]0.0467646984584104[/C][/ROW]
[ROW][C]9[/C][C]-0.0383755908219416[/C][/ROW]
[ROW][C]10[/C][C]0.0593857172834214[/C][/ROW]
[ROW][C]11[/C][C]-0.0710791466774877[/C][/ROW]
[ROW][C]12[/C][C]-0.075252322247026[/C][/ROW]
[ROW][C]13[/C][C]-0.119291755765396[/C][/ROW]
[ROW][C]14[/C][C]-0.0513612110218373[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27727&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27727&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 series1
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 series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-140.154626521444962
-130.0688653281957272
-12-0.107081344609825
-11-0.052677815968016
-100.127350420490514
-90.16559340597751
-80.0379974390111244
-7-0.0442542944945546
-60.157916211075150
-50.100034653230309
-4-0.00304828483493349
-3-0.112069057035788
-2-0.177856623295996
-10.162721271672286
00.521262047108371
10.362663068494834
20.0614374405897717
30.0139463685094387
4-0.0545031048928374
50.076324667721236
6-0.230803549326502
7-0.0755259808137603
80.0467646984584104
9-0.0383755908219416
100.0593857172834214
11-0.0710791466774877
12-0.075252322247026
13-0.119291755765396
14-0.0513612110218373


Figures (Output of Computation)

Input Parameters & R Code

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