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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 computationSun, 30 Nov 2008 04:28:23 -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/30/t1228044689con5uiuhto9n2n1.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=26453, 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 Impact200

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] [2008-11-29 20:10:39] [57fa5e3679c393aa19449b2f1be9928b]
-   P     [Standard Deviation-Mean Plot] [Q5] [2008-11-29 20:18:39] [57fa5e3679c393aa19449b2f1be9928b]
- RM        [Variance Reduction Matrix] [Q6 Variance] [2008-11-29 20:25:29] [57fa5e3679c393aa19449b2f1be9928b]
- RM          [(Partial) Autocorrelation Function] [Q6 ACF] [2008-11-29 20:35:57] [57fa5e3679c393aa19449b2f1be9928b]
-               [(Partial) Autocorrelation Function] [Q6 aangepaste ACF] [2008-11-29 20:44:03] [57fa5e3679c393aa19449b2f1be9928b]
- RM D            [Cross Correlation Function] [Q7] [2008-11-29 20:55:14] [57fa5e3679c393aa19449b2f1be9928b]
-                   [Cross Correlation Function] [Q9] [2008-11-29 21:16:34] [57fa5e3679c393aa19449b2f1be9928b]
F   P                   [Cross Correlation Function] [] [2008-11-30 11:28:23] [3762bf489501725951ad2579179cae2a] [Current]
Feedback Forum
2008-12-04 17:04:29 [339a57d8a4d5d113e4804fc423e4a59e] [reply] 
De student heeft de parameters uit Q8 ingevuld en bekomt zo een juist resultaat. Hij analyseert de output echter niet.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
22780
17351
21382
24561
17409
11514
31514
27071
29462
26105
22397
23843
21705
18089
20764
25316
17704
15548
28029
29383
36438
32034
22679
24319
18004
17537
20366
22782
19169
13807
29743
25591
29096
26482
22405
27044
17970
18730
19684
19785
18479
10698
31956
29506
34506
27165
26736
23691
18157
17328
18205
20995
17382
9367
31124
26551
30651
25859
25100
25778
20418
18688
20424
24776
19814
12738
31566
30111
30019
31934
25826
26835
Dataseries Y:
Download CSV
Histogram 
2218
1855
2187
1852
1570
1851
1954
1828
2251
2277
2085
2282
2266
1878
2267
2069
1746
2299
2360
2214
2825
2355
2333
3016
2155
2172
2150
2533
2058
2160
2259
2498
2695
2799
2945
2930
2318
2540
2570
2669
2450
2842
3439
2677
2979
2257
2842
2546
2455
2293
2379
2478
2054
2272
2351
2271
2542
2304
2194
2722
2395
2146
1894
2548
2087
2063
2481
2476
2212
2834
2148
2598

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=26453&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=26453&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26453&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 series0.4
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 series-0.1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-14-0.0883458776653282
-13-0.131237834861766
-120.0341192641088812
-11-0.0312022252701611
-100.0963823173566998
-90.0841513124400516
-8-0.0902848736815729
-7-0.050152454051876
-6-0.165815497948262
-5-0.142355414100787
-4-0.0255845645707528
-3-0.0818987119438374
-2-0.095485555485211
-1-0.00681134898370687
0-0.132347386050576
1-0.164360975355446
2-0.179559336448984
3-0.146800144062525
4-0.155350181064650
50.0215526260758293
6-0.02543086717846
70.105716749054889
80.130644088297107
90.200088188559129
100.175373530255718
110.373879733178617
120.183855915818171
130.361886113315476
140.348465021823887

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 0.4 \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 & -0.1 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 0 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-14 & -0.0883458776653282 \tabularnewline
-13 & -0.131237834861766 \tabularnewline
-12 & 0.0341192641088812 \tabularnewline
-11 & -0.0312022252701611 \tabularnewline
-10 & 0.0963823173566998 \tabularnewline
-9 & 0.0841513124400516 \tabularnewline
-8 & -0.0902848736815729 \tabularnewline
-7 & -0.050152454051876 \tabularnewline
-6 & -0.165815497948262 \tabularnewline
-5 & -0.142355414100787 \tabularnewline
-4 & -0.0255845645707528 \tabularnewline
-3 & -0.0818987119438374 \tabularnewline
-2 & -0.095485555485211 \tabularnewline
-1 & -0.00681134898370687 \tabularnewline
0 & -0.132347386050576 \tabularnewline
1 & -0.164360975355446 \tabularnewline
2 & -0.179559336448984 \tabularnewline
3 & -0.146800144062525 \tabularnewline
4 & -0.155350181064650 \tabularnewline
5 & 0.0215526260758293 \tabularnewline
6 & -0.02543086717846 \tabularnewline
7 & 0.105716749054889 \tabularnewline
8 & 0.130644088297107 \tabularnewline
9 & 0.200088188559129 \tabularnewline
10 & 0.175373530255718 \tabularnewline
11 & 0.373879733178617 \tabularnewline
12 & 0.183855915818171 \tabularnewline
13 & 0.361886113315476 \tabularnewline
14 & 0.348465021823887 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26453&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]0.4[/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]-0.1[/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]0[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-14[/C][C]-0.0883458776653282[/C][/ROW]
[ROW][C]-13[/C][C]-0.131237834861766[/C][/ROW]
[ROW][C]-12[/C][C]0.0341192641088812[/C][/ROW]
[ROW][C]-11[/C][C]-0.0312022252701611[/C][/ROW]
[ROW][C]-10[/C][C]0.0963823173566998[/C][/ROW]
[ROW][C]-9[/C][C]0.0841513124400516[/C][/ROW]
[ROW][C]-8[/C][C]-0.0902848736815729[/C][/ROW]
[ROW][C]-7[/C][C]-0.050152454051876[/C][/ROW]
[ROW][C]-6[/C][C]-0.165815497948262[/C][/ROW]
[ROW][C]-5[/C][C]-0.142355414100787[/C][/ROW]
[ROW][C]-4[/C][C]-0.0255845645707528[/C][/ROW]
[ROW][C]-3[/C][C]-0.0818987119438374[/C][/ROW]
[ROW][C]-2[/C][C]-0.095485555485211[/C][/ROW]
[ROW][C]-1[/C][C]-0.00681134898370687[/C][/ROW]
[ROW][C]0[/C][C]-0.132347386050576[/C][/ROW]
[ROW][C]1[/C][C]-0.164360975355446[/C][/ROW]
[ROW][C]2[/C][C]-0.179559336448984[/C][/ROW]
[ROW][C]3[/C][C]-0.146800144062525[/C][/ROW]
[ROW][C]4[/C][C]-0.155350181064650[/C][/ROW]
[ROW][C]5[/C][C]0.0215526260758293[/C][/ROW]
[ROW][C]6[/C][C]-0.02543086717846[/C][/ROW]
[ROW][C]7[/C][C]0.105716749054889[/C][/ROW]
[ROW][C]8[/C][C]0.130644088297107[/C][/ROW]
[ROW][C]9[/C][C]0.200088188559129[/C][/ROW]
[ROW][C]10[/C][C]0.175373530255718[/C][/ROW]
[ROW][C]11[/C][C]0.373879733178617[/C][/ROW]
[ROW][C]12[/C][C]0.183855915818171[/C][/ROW]
[ROW][C]13[/C][C]0.361886113315476[/C][/ROW]
[ROW][C]14[/C][C]0.348465021823887[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26453&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26453&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 series0.4
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 series-0.1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-14-0.0883458776653282
-13-0.131237834861766
-120.0341192641088812
-11-0.0312022252701611
-100.0963823173566998
-90.0841513124400516
-8-0.0902848736815729
-7-0.050152454051876
-6-0.165815497948262
-5-0.142355414100787
-4-0.0255845645707528
-3-0.0818987119438374
-2-0.095485555485211
-1-0.00681134898370687
0-0.132347386050576
1-0.164360975355446
2-0.179559336448984
3-0.146800144062525
4-0.155350181064650
50.0215526260758293
6-0.02543086717846
70.105716749054889
80.130644088297107
90.200088188559129
100.175373530255718
110.373879733178617
120.183855915818171
130.361886113315476
140.348465021823887


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0.4 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; par5 = -0.1 ; par6 = 0 ; par7 = 0 ;
Parameters (R input):
par1 = 0.4 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; par5 = -0.1 ; par6 = 0 ; par7 = 0 ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')