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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 computationMon, 01 Dec 2008 12:06:52 -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/01/t12281584874b9y6aih3kfznie.htm/, Retrieved Sun, 05 May 2024 17:36:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27179, Retrieved Sun, 05 May 2024 17:36:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact219

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  [Cross Correlation Function] [Q7 CCF d=0 D0] [2008-11-29 13:48:28] [6743688719638b0cb1c0a6e0bf433315]
-   PD      [Cross Correlation Function] [q8b] [2008-12-01 19:06:52] [5d823194959040fa9b19b8c8302177e6] [Current]
-             [Cross Correlation Function] [q9] [2008-12-01 19:10:41] [988ab43f527fc78aae41c84649095267]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2236
2084.9
2409.5
2199.3
2203.5
2254.1
1975.8
1742.2
2520.6
2438.1
2126.3
2267.5
2201.1
2128.5
2596
2458.2
2210.5
2621.2
2231.4
2103.6
2685.8
2539.3
2462.4
2693.3
2307.7
2385.9
2737.6
2653.9
2545.4
2848.8
2359.5
2488.3
2861.1
2717.9
2844
2749
2652.9
2660.2
3187.1
2774.1
3158.2
3244.6
2665.5
2820.8
2983.4
3077.4
3024.8
2731.8
3046.2
2834.8
3292.8
2946.1
3196.9
3284.2
3003
2979
3137.4
3647.7
3283
2947.3
Dataseries Y:
Download CSV
Histogram 
3258.1
3140.1
3627.4
3279.4
3204
3515.6
3146.6
2271.7
3627.9
3553.4
3018.3
3355.4
3242
3311.1
4125.2
3423
3120.3
3863
3240.8
2837.4
3945
3684.1
3659.6
3769.6
3592.7
3754
4507.8
3853.2
3817.2
3958.4
3428.9
3125.7
3977
3983.3
4299.6
4306.9
4259.5
3986
4755.6
3925.6
4206.5
4323.4
3816.1
3410.7
4227.4
4296.9
4351.7
3800
4277
4100.2
4672.5
4189.9
4231.9
4654.9
4298.5
3635.9
4505.1
4910.1
4908.7
4101.4

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'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27179&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27179&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27179&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






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])
-130.0887509340112492
-12-0.079771929594811
-110.153760504350789
-10-0.221517099856955
-90.037993886879144
-80.239411393510538
-7-0.30788747263513
-60.0104312591825685
-50.191038396160522
-4-0.187753609628341
-30.196762782647151
-2-0.0324707741059487
-1-0.390902515694304
00.653319481767677
1-0.25783927392788
2-0.262532578818333
30.307387784318921
4-0.0545312725945992
5-0.0870764149369282
60.327502661651018
7-0.357029639285267
80.0412650201720649
90.230847647338431
10-0.184991209744652
11-0.174221290775535
120.293142303477896
13-0.251416829740450

\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 & 0.0887509340112492 \tabularnewline
-12 & -0.079771929594811 \tabularnewline
-11 & 0.153760504350789 \tabularnewline
-10 & -0.221517099856955 \tabularnewline
-9 & 0.037993886879144 \tabularnewline
-8 & 0.239411393510538 \tabularnewline
-7 & -0.30788747263513 \tabularnewline
-6 & 0.0104312591825685 \tabularnewline
-5 & 0.191038396160522 \tabularnewline
-4 & -0.187753609628341 \tabularnewline
-3 & 0.196762782647151 \tabularnewline
-2 & -0.0324707741059487 \tabularnewline
-1 & -0.390902515694304 \tabularnewline
0 & 0.653319481767677 \tabularnewline
1 & -0.25783927392788 \tabularnewline
2 & -0.262532578818333 \tabularnewline
3 & 0.307387784318921 \tabularnewline
4 & -0.0545312725945992 \tabularnewline
5 & -0.0870764149369282 \tabularnewline
6 & 0.327502661651018 \tabularnewline
7 & -0.357029639285267 \tabularnewline
8 & 0.0412650201720649 \tabularnewline
9 & 0.230847647338431 \tabularnewline
10 & -0.184991209744652 \tabularnewline
11 & -0.174221290775535 \tabularnewline
12 & 0.293142303477896 \tabularnewline
13 & -0.251416829740450 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27179&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]0.0887509340112492[/C][/ROW]
[ROW][C]-12[/C][C]-0.079771929594811[/C][/ROW]
[ROW][C]-11[/C][C]0.153760504350789[/C][/ROW]
[ROW][C]-10[/C][C]-0.221517099856955[/C][/ROW]
[ROW][C]-9[/C][C]0.037993886879144[/C][/ROW]
[ROW][C]-8[/C][C]0.239411393510538[/C][/ROW]
[ROW][C]-7[/C][C]-0.30788747263513[/C][/ROW]
[ROW][C]-6[/C][C]0.0104312591825685[/C][/ROW]
[ROW][C]-5[/C][C]0.191038396160522[/C][/ROW]
[ROW][C]-4[/C][C]-0.187753609628341[/C][/ROW]
[ROW][C]-3[/C][C]0.196762782647151[/C][/ROW]
[ROW][C]-2[/C][C]-0.0324707741059487[/C][/ROW]
[ROW][C]-1[/C][C]-0.390902515694304[/C][/ROW]
[ROW][C]0[/C][C]0.653319481767677[/C][/ROW]
[ROW][C]1[/C][C]-0.25783927392788[/C][/ROW]
[ROW][C]2[/C][C]-0.262532578818333[/C][/ROW]
[ROW][C]3[/C][C]0.307387784318921[/C][/ROW]
[ROW][C]4[/C][C]-0.0545312725945992[/C][/ROW]
[ROW][C]5[/C][C]-0.0870764149369282[/C][/ROW]
[ROW][C]6[/C][C]0.327502661651018[/C][/ROW]
[ROW][C]7[/C][C]-0.357029639285267[/C][/ROW]
[ROW][C]8[/C][C]0.0412650201720649[/C][/ROW]
[ROW][C]9[/C][C]0.230847647338431[/C][/ROW]
[ROW][C]10[/C][C]-0.184991209744652[/C][/ROW]
[ROW][C]11[/C][C]-0.174221290775535[/C][/ROW]
[ROW][C]12[/C][C]0.293142303477896[/C][/ROW]
[ROW][C]13[/C][C]-0.251416829740450[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27179&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27179&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])
-130.0887509340112492
-12-0.079771929594811
-110.153760504350789
-10-0.221517099856955
-90.037993886879144
-80.239411393510538
-7-0.30788747263513
-60.0104312591825685
-50.191038396160522
-4-0.187753609628341
-30.196762782647151
-2-0.0324707741059487
-1-0.390902515694304
00.653319481767677
1-0.25783927392788
2-0.262532578818333
30.307387784318921
4-0.0545312725945992
5-0.0870764149369282
60.327502661651018
7-0.357029639285267
80.0412650201720649
90.230847647338431
10-0.184991209744652
11-0.174221290775535
120.293142303477896
13-0.251416829740450


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')