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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationTue, 09 Dec 2008 11:02:40 -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/09/t122884580009cd1zhsbtv3lso.htm/, Retrieved Sat, 25 May 2024 14:46:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=31643, Retrieved Sat, 25 May 2024 14:46:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact186

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 Randow walk task] [2008-11-29 13:16:45] [6743688719638b0cb1c0a6e0bf433315]
F         [Standard Deviation-Mean Plot] [Q5 ] [2008-11-29 15:18:13] [de72ca3f4fcfd0997c84e1ac92aea119]
F    D      [Standard Deviation-Mean Plot] [Q8 Workshop 4] [2008-12-02 17:47:16] [de72ca3f4fcfd0997c84e1ac92aea119]
F RMPD        [Cross Correlation Function] [Q9 Workshop 4] [2008-12-02 18:02:55] [de72ca3f4fcfd0997c84e1ac92aea119]
-   P             [Cross Correlation Function] [verbeterd] [2008-12-09 18:02:40] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0.9059
0.8883
0.8924
0.8833
0.87
0.8758
0.8858
0.917
0.9554
0.9922
0.9778
0.9808
0.9811
1.0014
1.0183
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
Dataseries Y:
Download CSV
Histogram 
109.86
108.68
113.38
117.12
116.23
114.75
115.81
115.86
117.80
117.11
116.31
118.38
121.57
121.65
124.20
126.12
128.60
128.16
130.12
135.83
138.05
134.99
132.38
128.94
128.12
127.84
132.43
134.13
134.78
133.13
129.08
134.48
132.86
134.08
134.54
134.51
135.97
136.09
139.14
135.63
136.55
138.83
138.84
135.37
132.22
134.75
135.98
136.06
138.05
139.59
140.58
139.81
140.77
140.96
143.59
142.70
145.11
146.70
148.53
148.99
149.65
151.11
154.82
156.56
157.60
155.24
160.68
163.22
164.55
166.76
159.05
159.82
164.95
162.89

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31643&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]2 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=31643&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31643&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1.8
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-0.2
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-15-0.0380914118349967
-14-0.0461973548476320
-13-0.0510525628857364
-12-0.0443786498355363
-11-0.0274635658777301
-10-0.0507500550678788
-9-0.0337310830135578
-8-0.0140625194567218
-7-0.00934222031785851
-60.0190827182261853
-50.00340491263773141
-40.0420970578276988
-30.0241001511239355
-20.0619028825240981
-10.0811191684756355
00.0550261582095674
10.0129957720668473
20.0474829402458858
30.0682896380894607
40.0507833776998749
50.00126495760416011
6-0.0202883355521363
7-0.0253798824163578
80.00158551963624148
90.0225242842743455
10-0.00872224942862457
11-0.00383251645729932
12-0.0343506628745747
13-0.0352134327270595
14-0.009140844911918
150.0345117118138224

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 1.8 \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 & -0.2 \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
-15 & -0.0380914118349967 \tabularnewline
-14 & -0.0461973548476320 \tabularnewline
-13 & -0.0510525628857364 \tabularnewline
-12 & -0.0443786498355363 \tabularnewline
-11 & -0.0274635658777301 \tabularnewline
-10 & -0.0507500550678788 \tabularnewline
-9 & -0.0337310830135578 \tabularnewline
-8 & -0.0140625194567218 \tabularnewline
-7 & -0.00934222031785851 \tabularnewline
-6 & 0.0190827182261853 \tabularnewline
-5 & 0.00340491263773141 \tabularnewline
-4 & 0.0420970578276988 \tabularnewline
-3 & 0.0241001511239355 \tabularnewline
-2 & 0.0619028825240981 \tabularnewline
-1 & 0.0811191684756355 \tabularnewline
0 & 0.0550261582095674 \tabularnewline
1 & 0.0129957720668473 \tabularnewline
2 & 0.0474829402458858 \tabularnewline
3 & 0.0682896380894607 \tabularnewline
4 & 0.0507833776998749 \tabularnewline
5 & 0.00126495760416011 \tabularnewline
6 & -0.0202883355521363 \tabularnewline
7 & -0.0253798824163578 \tabularnewline
8 & 0.00158551963624148 \tabularnewline
9 & 0.0225242842743455 \tabularnewline
10 & -0.00872224942862457 \tabularnewline
11 & -0.00383251645729932 \tabularnewline
12 & -0.0343506628745747 \tabularnewline
13 & -0.0352134327270595 \tabularnewline
14 & -0.009140844911918 \tabularnewline
15 & 0.0345117118138224 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31643&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]1.8[/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]-0.2[/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]-15[/C][C]-0.0380914118349967[/C][/ROW]
[ROW][C]-14[/C][C]-0.0461973548476320[/C][/ROW]
[ROW][C]-13[/C][C]-0.0510525628857364[/C][/ROW]
[ROW][C]-12[/C][C]-0.0443786498355363[/C][/ROW]
[ROW][C]-11[/C][C]-0.0274635658777301[/C][/ROW]
[ROW][C]-10[/C][C]-0.0507500550678788[/C][/ROW]
[ROW][C]-9[/C][C]-0.0337310830135578[/C][/ROW]
[ROW][C]-8[/C][C]-0.0140625194567218[/C][/ROW]
[ROW][C]-7[/C][C]-0.00934222031785851[/C][/ROW]
[ROW][C]-6[/C][C]0.0190827182261853[/C][/ROW]
[ROW][C]-5[/C][C]0.00340491263773141[/C][/ROW]
[ROW][C]-4[/C][C]0.0420970578276988[/C][/ROW]
[ROW][C]-3[/C][C]0.0241001511239355[/C][/ROW]
[ROW][C]-2[/C][C]0.0619028825240981[/C][/ROW]
[ROW][C]-1[/C][C]0.0811191684756355[/C][/ROW]
[ROW][C]0[/C][C]0.0550261582095674[/C][/ROW]
[ROW][C]1[/C][C]0.0129957720668473[/C][/ROW]
[ROW][C]2[/C][C]0.0474829402458858[/C][/ROW]
[ROW][C]3[/C][C]0.0682896380894607[/C][/ROW]
[ROW][C]4[/C][C]0.0507833776998749[/C][/ROW]
[ROW][C]5[/C][C]0.00126495760416011[/C][/ROW]
[ROW][C]6[/C][C]-0.0202883355521363[/C][/ROW]
[ROW][C]7[/C][C]-0.0253798824163578[/C][/ROW]
[ROW][C]8[/C][C]0.00158551963624148[/C][/ROW]
[ROW][C]9[/C][C]0.0225242842743455[/C][/ROW]
[ROW][C]10[/C][C]-0.00872224942862457[/C][/ROW]
[ROW][C]11[/C][C]-0.00383251645729932[/C][/ROW]
[ROW][C]12[/C][C]-0.0343506628745747[/C][/ROW]
[ROW][C]13[/C][C]-0.0352134327270595[/C][/ROW]
[ROW][C]14[/C][C]-0.009140844911918[/C][/ROW]
[ROW][C]15[/C][C]0.0345117118138224[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31643&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31643&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 series1.8
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-0.2
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-15-0.0380914118349967
-14-0.0461973548476320
-13-0.0510525628857364
-12-0.0443786498355363
-11-0.0274635658777301
-10-0.0507500550678788
-9-0.0337310830135578
-8-0.0140625194567218
-7-0.00934222031785851
-60.0190827182261853
-50.00340491263773141
-40.0420970578276988
-30.0241001511239355
-20.0619028825240981
-10.0811191684756355
00.0550261582095674
10.0129957720668473
20.0474829402458858
30.0682896380894607
40.0507833776998749
50.00126495760416011
6-0.0202883355521363
7-0.0253798824163578
80.00158551963624148
90.0225242842743455
10-0.00872224942862457
11-0.00383251645729932
12-0.0343506628745747
13-0.0352134327270595
14-0.009140844911918
150.0345117118138224


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1.8 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = -0.2 ; par6 = 0 ; par7 = 0 ;
Parameters (R input):
par1 = 1.8 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = -0.2 ; par6 = 0 ; 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')