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 17:28:17 -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/03/t1228264172x4d85v9vsm8fq02.htm/, Retrieved Fri, 17 May 2024 16:58:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28544, Retrieved Fri, 17 May 2024 16:58:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact241

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] [Q9 non stationary...] [2008-12-03 00:28:17] [9f72e095d5529918bf5b0810c01bf6ce] [Current]
Feedback Forum
2008-12-04 11:18:47 [] [reply] 
Een zeer goed resultaat.
2008-12-06 18:00:13 [a2386b643d711541400692649981f2dc] [reply] 
Juist antwoord. Je vertelt welke waarden er buiten het interval liggen en wat dit wil zeggen. Je vergelijkt het ook met q7 en besluit dan dat het aangepaste model beter is.
2008-12-09 00:30:26 [Jessica Alves Pires] [reply] 
Ik ben het hiermee eens.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1.0622
1.0773
1.0807
1.0848
1.1582
1.1663
1.1372
1.1139
1.1222
1.1692
1.1702
1.2286
1.2613
1.2646
1.2262
1.1985
1.2007
1.2138
1.2266
1.2176
1.2218
1.249
1.2991
1.3408
1.3119
1.3014
1.3201
1.2938
1.2694
1.2165
1.2037
1.2292
1.2256
1.2015
1.1786
1.1856
1.2103
1.1938
1.202
1.2271
1.277
1.265
1.2684
1.2811
1.2727
1.2611
1.2881
1.3213
1.2999
1.3074
1.3242
1.3516
1.3511
1.3419
1.3716
1.3622
1.3896
1.4227
1.4684
1.457
Dataseries Y:
Download CSV
Histogram 
10812
10738
10171
9721
9897
9828
9924
10371
10846
10413
10709
10662
10570
10297
10635
10872
10296
10383
10431
10574
10653
10805
10872
10625
10407
10463
10556
10646
10702
11353
11346
11451
11964
12574
13031
13812
14544
14931
14886
16005
17064
15168
16050
15839
15137
14954
15648
15305
15579
16348
15928
16171
15937
15713
15594
15683
16438
17032
17696
17745

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=28544&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=28544&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28544&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 series0.6
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-0.6
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-140.0390290364213234
-130.00988053311233933
-12-0.0992884790727577
-110.0597181780350827
-100.0779666713879718
-90.0436391816867054
-8-0.096500046571198
-7-0.101701610408648
-60.0295430511154988
-5-0.354183883861125
-4-0.089185645121093
-30.027972198289652
-2-0.0929525175933268
-1-0.294164758709020
0-0.0517405522477253
10.0651460150968074
2-0.140654999467302
30.096582745928707
40.187660722167805
50.0859181716376914
6-0.157727614503572
70.0144972685993415
8-0.042142190736515
9-0.133435627806594
10-0.108356673834882
110.189983997838637
120.164885389133629
13-0.046787350114555
140.102380591648983

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 0.6 \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 & -0.6 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 1 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-14 & 0.0390290364213234 \tabularnewline
-13 & 0.00988053311233933 \tabularnewline
-12 & -0.0992884790727577 \tabularnewline
-11 & 0.0597181780350827 \tabularnewline
-10 & 0.0779666713879718 \tabularnewline
-9 & 0.0436391816867054 \tabularnewline
-8 & -0.096500046571198 \tabularnewline
-7 & -0.101701610408648 \tabularnewline
-6 & 0.0295430511154988 \tabularnewline
-5 & -0.354183883861125 \tabularnewline
-4 & -0.089185645121093 \tabularnewline
-3 & 0.027972198289652 \tabularnewline
-2 & -0.0929525175933268 \tabularnewline
-1 & -0.294164758709020 \tabularnewline
0 & -0.0517405522477253 \tabularnewline
1 & 0.0651460150968074 \tabularnewline
2 & -0.140654999467302 \tabularnewline
3 & 0.096582745928707 \tabularnewline
4 & 0.187660722167805 \tabularnewline
5 & 0.0859181716376914 \tabularnewline
6 & -0.157727614503572 \tabularnewline
7 & 0.0144972685993415 \tabularnewline
8 & -0.042142190736515 \tabularnewline
9 & -0.133435627806594 \tabularnewline
10 & -0.108356673834882 \tabularnewline
11 & 0.189983997838637 \tabularnewline
12 & 0.164885389133629 \tabularnewline
13 & -0.046787350114555 \tabularnewline
14 & 0.102380591648983 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28544&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]0.6[/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]-0.6[/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]0[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-14[/C][C]0.0390290364213234[/C][/ROW]
[ROW][C]-13[/C][C]0.00988053311233933[/C][/ROW]
[ROW][C]-12[/C][C]-0.0992884790727577[/C][/ROW]
[ROW][C]-11[/C][C]0.0597181780350827[/C][/ROW]
[ROW][C]-10[/C][C]0.0779666713879718[/C][/ROW]
[ROW][C]-9[/C][C]0.0436391816867054[/C][/ROW]
[ROW][C]-8[/C][C]-0.096500046571198[/C][/ROW]
[ROW][C]-7[/C][C]-0.101701610408648[/C][/ROW]
[ROW][C]-6[/C][C]0.0295430511154988[/C][/ROW]
[ROW][C]-5[/C][C]-0.354183883861125[/C][/ROW]
[ROW][C]-4[/C][C]-0.089185645121093[/C][/ROW]
[ROW][C]-3[/C][C]0.027972198289652[/C][/ROW]
[ROW][C]-2[/C][C]-0.0929525175933268[/C][/ROW]
[ROW][C]-1[/C][C]-0.294164758709020[/C][/ROW]
[ROW][C]0[/C][C]-0.0517405522477253[/C][/ROW]
[ROW][C]1[/C][C]0.0651460150968074[/C][/ROW]
[ROW][C]2[/C][C]-0.140654999467302[/C][/ROW]
[ROW][C]3[/C][C]0.096582745928707[/C][/ROW]
[ROW][C]4[/C][C]0.187660722167805[/C][/ROW]
[ROW][C]5[/C][C]0.0859181716376914[/C][/ROW]
[ROW][C]6[/C][C]-0.157727614503572[/C][/ROW]
[ROW][C]7[/C][C]0.0144972685993415[/C][/ROW]
[ROW][C]8[/C][C]-0.042142190736515[/C][/ROW]
[ROW][C]9[/C][C]-0.133435627806594[/C][/ROW]
[ROW][C]10[/C][C]-0.108356673834882[/C][/ROW]
[ROW][C]11[/C][C]0.189983997838637[/C][/ROW]
[ROW][C]12[/C][C]0.164885389133629[/C][/ROW]
[ROW][C]13[/C][C]-0.046787350114555[/C][/ROW]
[ROW][C]14[/C][C]0.102380591648983[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28544&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28544&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 series0.6
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-0.6
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-140.0390290364213234
-130.00988053311233933
-12-0.0992884790727577
-110.0597181780350827
-100.0779666713879718
-90.0436391816867054
-8-0.096500046571198
-7-0.101701610408648
-60.0295430511154988
-5-0.354183883861125
-4-0.089185645121093
-30.027972198289652
-2-0.0929525175933268
-1-0.294164758709020
0-0.0517405522477253
10.0651460150968074
2-0.140654999467302
30.096582745928707
40.187660722167805
50.0859181716376914
6-0.157727614503572
70.0144972685993415
8-0.042142190736515
9-0.133435627806594
10-0.108356673834882
110.189983997838637
120.164885389133629
13-0.046787350114555
140.102380591648983


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0.6 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = -0.6 ; par6 = 1 ; par7 = 0 ;
Parameters (R input):
par1 = 0.6 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = -0.6 ; par6 = 1 ; par7 = 0 ;
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')