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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 computationWed, 03 Dec 2008 06:45:45 -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/t1228311961ne6st6xmkkx3eu7.htm/, Retrieved Fri, 17 May 2024 14:37:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28708, Retrieved Fri, 17 May 2024 14:37:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsq9
Estimated Impact204

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]
- RMPD  [Standard Deviation-Mean Plot] [q5] [2008-12-03 13:15:06] [3ffd109c9e040b1ae7e5dbe576d4698c]
- RMPD    [(Partial) Autocorrelation Function] [q8] [2008-12-03 13:16:51] [3ffd109c9e040b1ae7e5dbe576d4698c]
- RMP       [Variance Reduction Matrix] [q8] [2008-12-03 13:29:02] [3ffd109c9e040b1ae7e5dbe576d4698c]
-    D        [Variance Reduction Matrix] [q8] [2008-12-03 13:37:20] [3ffd109c9e040b1ae7e5dbe576d4698c]
- RM            [(Partial) Autocorrelation Function] [q8] [2008-12-03 13:40:14] [3ffd109c9e040b1ae7e5dbe576d4698c]
- RM D              [Cross Correlation Function] [q9] [2008-12-03 13:45:45] [962e6c9020896982bc8283b8971710a9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
267037
258113
262813
267413
267366
264777
258863
254844
254868
277267
285351
286602
283042
276687
277915
277128
277103
275037
270150
267140
264993
287259
291186
292300
288186
281477
282656
280190
280408
276836
275216
274352
271311
289802
290726
292300
278506
269826
265861
269034
264176
255198
253353
246057
235372
258556
260993
254663
250643
243422
247105
248541
245039
237080
237085
225554
226839
247934
248333
246969
245098
Dataseries Y:
Download CSV
Histogram 
298427
289231
291975
294912
293488
290555
284736
281818
287854
316263
325412
326011
328282
317480
317539
313737
312276
309391
302950
300316
304035
333476
337698
335932
323931
313927
314485
313218
309664
302963
298989
298423
301631
329765
335083
327616
309119
295916
291413
291542
284678
276475
272566
264981
263290
296806
303598
286994
276427
266424
267153
268381
262522
255542
253158
243803
250741
280445
285257
270976
261076

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=28708&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=28708&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28708&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])
-13-0.123613479325402
-12-0.335911126811763
-110.115153208291357
-10-0.0139846247053626
-9-0.041885469810871
-8-0.00976013576350848
-7-0.0259829510481950
-60.105350299768076
-5-0.186476949476804
-40.0710698819183276
-30.223322453838045
-20.136393471078697
-1-0.0852502941830209
00.759012809082488
1-0.0095083465341072
2-0.0323414546505546
30.150493675306899
40.122382374993785
50.0702382419814183
60.00872824167149416
7-0.00662261820522205
80.200977302944319
9-0.0365108725543905
10-0.0963392440003061
110.16478554370059
120.0273004517060211
13-0.237331791866933

\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
-13 & -0.123613479325402 \tabularnewline
-12 & -0.335911126811763 \tabularnewline
-11 & 0.115153208291357 \tabularnewline
-10 & -0.0139846247053626 \tabularnewline
-9 & -0.041885469810871 \tabularnewline
-8 & -0.00976013576350848 \tabularnewline
-7 & -0.0259829510481950 \tabularnewline
-6 & 0.105350299768076 \tabularnewline
-5 & -0.186476949476804 \tabularnewline
-4 & 0.0710698819183276 \tabularnewline
-3 & 0.223322453838045 \tabularnewline
-2 & 0.136393471078697 \tabularnewline
-1 & -0.0852502941830209 \tabularnewline
0 & 0.759012809082488 \tabularnewline
1 & -0.0095083465341072 \tabularnewline
2 & -0.0323414546505546 \tabularnewline
3 & 0.150493675306899 \tabularnewline
4 & 0.122382374993785 \tabularnewline
5 & 0.0702382419814183 \tabularnewline
6 & 0.00872824167149416 \tabularnewline
7 & -0.00662261820522205 \tabularnewline
8 & 0.200977302944319 \tabularnewline
9 & -0.0365108725543905 \tabularnewline
10 & -0.0963392440003061 \tabularnewline
11 & 0.16478554370059 \tabularnewline
12 & 0.0273004517060211 \tabularnewline
13 & -0.237331791866933 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28708&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]-13[/C][C]-0.123613479325402[/C][/ROW]
[ROW][C]-12[/C][C]-0.335911126811763[/C][/ROW]
[ROW][C]-11[/C][C]0.115153208291357[/C][/ROW]
[ROW][C]-10[/C][C]-0.0139846247053626[/C][/ROW]
[ROW][C]-9[/C][C]-0.041885469810871[/C][/ROW]
[ROW][C]-8[/C][C]-0.00976013576350848[/C][/ROW]
[ROW][C]-7[/C][C]-0.0259829510481950[/C][/ROW]
[ROW][C]-6[/C][C]0.105350299768076[/C][/ROW]
[ROW][C]-5[/C][C]-0.186476949476804[/C][/ROW]
[ROW][C]-4[/C][C]0.0710698819183276[/C][/ROW]
[ROW][C]-3[/C][C]0.223322453838045[/C][/ROW]
[ROW][C]-2[/C][C]0.136393471078697[/C][/ROW]
[ROW][C]-1[/C][C]-0.0852502941830209[/C][/ROW]
[ROW][C]0[/C][C]0.759012809082488[/C][/ROW]
[ROW][C]1[/C][C]-0.0095083465341072[/C][/ROW]
[ROW][C]2[/C][C]-0.0323414546505546[/C][/ROW]
[ROW][C]3[/C][C]0.150493675306899[/C][/ROW]
[ROW][C]4[/C][C]0.122382374993785[/C][/ROW]
[ROW][C]5[/C][C]0.0702382419814183[/C][/ROW]
[ROW][C]6[/C][C]0.00872824167149416[/C][/ROW]
[ROW][C]7[/C][C]-0.00662261820522205[/C][/ROW]
[ROW][C]8[/C][C]0.200977302944319[/C][/ROW]
[ROW][C]9[/C][C]-0.0365108725543905[/C][/ROW]
[ROW][C]10[/C][C]-0.0963392440003061[/C][/ROW]
[ROW][C]11[/C][C]0.16478554370059[/C][/ROW]
[ROW][C]12[/C][C]0.0273004517060211[/C][/ROW]
[ROW][C]13[/C][C]-0.237331791866933[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28708&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28708&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])
-13-0.123613479325402
-12-0.335911126811763
-110.115153208291357
-10-0.0139846247053626
-9-0.041885469810871
-8-0.00976013576350848
-7-0.0259829510481950
-60.105350299768076
-5-0.186476949476804
-40.0710698819183276
-30.223322453838045
-20.136393471078697
-1-0.0852502941830209
00.759012809082488
1-0.0095083465341072
2-0.0323414546505546
30.150493675306899
40.122382374993785
50.0702382419814183
60.00872824167149416
7-0.00662261820522205
80.200977302944319
9-0.0365108725543905
10-0.0963392440003061
110.16478554370059
120.0273004517060211
13-0.237331791866933


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ;
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')