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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 computationSat, 29 Nov 2008 10:42:06 -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/Nov/29/t122798124371pv9pzi0hornxa.htm/, Retrieved Sun, 19 May 2024 08:20:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26347, Retrieved Sun, 19 May 2024 08:20:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact203

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  [Standard Deviation-Mean Plot] [vraag 5] [2008-11-29 13:37:39] [c45c87b96bbf32ffc2144fc37d767b2e]
-    D    [Standard Deviation-Mean Plot] [vraag 7] [2008-11-29 17:31:20] [c45c87b96bbf32ffc2144fc37d767b2e]
F RMPD        [Cross Correlation Function] [vraag 7] [2008-11-29 17:42:06] [3dc594a6c62226e1e98766c4d385bfaa] [Current]
F   P           [Cross Correlation Function] [vraag 7] [2008-11-29 17:57:22] [c45c87b96bbf32ffc2144fc37d767b2e]
Feedback Forum
2008-12-08 19:35:31 [Michaël De Kuyer] [reply] 
Deze vraag heb ik correct beantwoord.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
507
569
580
578
565
547
555
562
561
555
544
537
543
594
611
613
611
594
595
591
589
584
573
567
569
621
629
628
612
595
597
593
590
580
574
573
573
620
626
620
588
566
557
561
549
532
526
511
499
555
565
542
527
510
514
517
508
493
490
469
478
Dataseries Y:
Download CSV
Histogram 
2.282
2.266
1.878
2.267
2.069
1.746
2.299
2.36
2.214
2.825
2.355
2.333
3.016
2.155
2.172
2.15
2.533
2.058
2.16
2.259
2.498
2.695
2.799
2.945
2.93
2.318
2.54
2.57
2.669
2.45
2.842
3.439
2.677
2.979
2.257
2.842
2.546
2.455
2.293
2.379
2.478
2.054
2.272
2.351
2.271
2.542
2.304
2.194
2.722
2.395
2.146
1.894
2.548
2.087
2.063
2.481
2.476
2.212
2.834
2.148
2.598

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=26347&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=26347&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26347&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 series2
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 series-0.1
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-130.0104738551286266
-12-0.0243728974651657
-110.0617699267455265
-10-0.0433001121927841
-9-0.0345362433643631
-80.174756886404358
-70.0194600736760464
-60.031933900972105
-5-0.0334717298198724
-40.158656494425657
-3-0.068978228366227
-20.0523498253490384
-1-0.00191874881854787
0-0.0658866113329445
10.0831481004740892
2-0.067230647231489
30.214106136005345
4-0.170082036929972
50.0571163583801727
6-0.134836746085606
70.0399722520398705
8-0.213065324646911
9-0.0927546977624243
10-0.00460998584563288
110.167888380671440
12-0.0875430739762968
130.0121816482137597

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 2 \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 & -0.1 \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
-13 & 0.0104738551286266 \tabularnewline
-12 & -0.0243728974651657 \tabularnewline
-11 & 0.0617699267455265 \tabularnewline
-10 & -0.0433001121927841 \tabularnewline
-9 & -0.0345362433643631 \tabularnewline
-8 & 0.174756886404358 \tabularnewline
-7 & 0.0194600736760464 \tabularnewline
-6 & 0.031933900972105 \tabularnewline
-5 & -0.0334717298198724 \tabularnewline
-4 & 0.158656494425657 \tabularnewline
-3 & -0.068978228366227 \tabularnewline
-2 & 0.0523498253490384 \tabularnewline
-1 & -0.00191874881854787 \tabularnewline
0 & -0.0658866113329445 \tabularnewline
1 & 0.0831481004740892 \tabularnewline
2 & -0.067230647231489 \tabularnewline
3 & 0.214106136005345 \tabularnewline
4 & -0.170082036929972 \tabularnewline
5 & 0.0571163583801727 \tabularnewline
6 & -0.134836746085606 \tabularnewline
7 & 0.0399722520398705 \tabularnewline
8 & -0.213065324646911 \tabularnewline
9 & -0.0927546977624243 \tabularnewline
10 & -0.00460998584563288 \tabularnewline
11 & 0.167888380671440 \tabularnewline
12 & -0.0875430739762968 \tabularnewline
13 & 0.0121816482137597 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26347&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]2[/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]-0.1[/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]0[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-13[/C][C]0.0104738551286266[/C][/ROW]
[ROW][C]-12[/C][C]-0.0243728974651657[/C][/ROW]
[ROW][C]-11[/C][C]0.0617699267455265[/C][/ROW]
[ROW][C]-10[/C][C]-0.0433001121927841[/C][/ROW]
[ROW][C]-9[/C][C]-0.0345362433643631[/C][/ROW]
[ROW][C]-8[/C][C]0.174756886404358[/C][/ROW]
[ROW][C]-7[/C][C]0.0194600736760464[/C][/ROW]
[ROW][C]-6[/C][C]0.031933900972105[/C][/ROW]
[ROW][C]-5[/C][C]-0.0334717298198724[/C][/ROW]
[ROW][C]-4[/C][C]0.158656494425657[/C][/ROW]
[ROW][C]-3[/C][C]-0.068978228366227[/C][/ROW]
[ROW][C]-2[/C][C]0.0523498253490384[/C][/ROW]
[ROW][C]-1[/C][C]-0.00191874881854787[/C][/ROW]
[ROW][C]0[/C][C]-0.0658866113329445[/C][/ROW]
[ROW][C]1[/C][C]0.0831481004740892[/C][/ROW]
[ROW][C]2[/C][C]-0.067230647231489[/C][/ROW]
[ROW][C]3[/C][C]0.214106136005345[/C][/ROW]
[ROW][C]4[/C][C]-0.170082036929972[/C][/ROW]
[ROW][C]5[/C][C]0.0571163583801727[/C][/ROW]
[ROW][C]6[/C][C]-0.134836746085606[/C][/ROW]
[ROW][C]7[/C][C]0.0399722520398705[/C][/ROW]
[ROW][C]8[/C][C]-0.213065324646911[/C][/ROW]
[ROW][C]9[/C][C]-0.0927546977624243[/C][/ROW]
[ROW][C]10[/C][C]-0.00460998584563288[/C][/ROW]
[ROW][C]11[/C][C]0.167888380671440[/C][/ROW]
[ROW][C]12[/C][C]-0.0875430739762968[/C][/ROW]
[ROW][C]13[/C][C]0.0121816482137597[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26347&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26347&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 series2
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 series-0.1
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-130.0104738551286266
-12-0.0243728974651657
-110.0617699267455265
-10-0.0433001121927841
-9-0.0345362433643631
-80.174756886404358
-70.0194600736760464
-60.031933900972105
-5-0.0334717298198724
-40.158656494425657
-3-0.068978228366227
-20.0523498253490384
-1-0.00191874881854787
0-0.0658866113329445
10.0831481004740892
2-0.067230647231489
30.214106136005345
4-0.170082036929972
50.0571163583801727
6-0.134836746085606
70.0399722520398705
8-0.213065324646911
9-0.0927546977624243
10-0.00460998584563288
110.167888380671440
12-0.0875430739762968
130.0121816482137597


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 2.0 ; par2 = 1 ; par3 = 1 ; par4 = 12 ; par5 = -0.1 ; par6 = 1 ; par7 = 0 ;
Parameters (R input):
par1 = 2.0 ; par2 = 1 ; par3 = 1 ; par4 = 12 ; par5 = -0.1 ; 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')