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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, 02 Dec 2008 07:56:23 -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/Dec/02/t12282298598cm0l63mx8vj6wb.htm/, Retrieved Fri, 17 May 2024 05:45:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27909, Retrieved Fri, 17 May 2024 05:45:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact236

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  [Variance Reduction Matrix] [Vincent Dolhain T...] [2008-12-02 13:29:42] [45145a9a0edd4898119f1bcc11d60cc0]
F RM      [Spectral Analysis] [vincent Dolhain T...] [2008-12-02 13:41:51] [45145a9a0edd4898119f1bcc11d60cc0]
F RM D      [Cross Correlation Function] [Vincent Dolhain T...] [2008-12-02 14:17:58] [45145a9a0edd4898119f1bcc11d60cc0]
F               [Cross Correlation Function] [Vincent Dolhain T...] [2008-12-02 14:56:23] [b7b5ae6cd293e6a1a87dd48715549281] [Current]
Feedback Forum
2008-12-08 16:56:04 [Jessica Alves Pires] [reply] 
Hoe heb je Q9 kunnen berekenen zonder Q8 opgelost te hebben?

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0.9059
0.8883
0.8924
0.8833
0.8700
0.8758
0.8858
0.9170
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
1.2490
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
1.2020
1.2271
1.2770
1.2650
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 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=27909&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=27909&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27909&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 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])
-14-0.104464675354515
-13-0.143758899780193
-120.0287633685685997
-11-0.0165133196106402
-10-0.00738484057274178
-9-0.0505316104014827
-80.0259538805096431
-70.0465290541051726
-6-0.272738276932281
-5-0.040255602507959
-4-0.0154906354969193
-30.164367810084432
-2-0.0711002416608856
-10.223804701351708
00.349421640015357
10.0296528177363326
2-0.158620840222523
3-0.206838024519017
4-0.041336304697993
50.155673493419409
60.293501742257881
70.0838570004448753
8-0.0254261825146424
90.0246644801372083
100.115237736640415
11-0.186159200177912
12-0.250284328370368
13-0.0103778347973826
140.0260140306600202

\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
-14 & -0.104464675354515 \tabularnewline
-13 & -0.143758899780193 \tabularnewline
-12 & 0.0287633685685997 \tabularnewline
-11 & -0.0165133196106402 \tabularnewline
-10 & -0.00738484057274178 \tabularnewline
-9 & -0.0505316104014827 \tabularnewline
-8 & 0.0259538805096431 \tabularnewline
-7 & 0.0465290541051726 \tabularnewline
-6 & -0.272738276932281 \tabularnewline
-5 & -0.040255602507959 \tabularnewline
-4 & -0.0154906354969193 \tabularnewline
-3 & 0.164367810084432 \tabularnewline
-2 & -0.0711002416608856 \tabularnewline
-1 & 0.223804701351708 \tabularnewline
0 & 0.349421640015357 \tabularnewline
1 & 0.0296528177363326 \tabularnewline
2 & -0.158620840222523 \tabularnewline
3 & -0.206838024519017 \tabularnewline
4 & -0.041336304697993 \tabularnewline
5 & 0.155673493419409 \tabularnewline
6 & 0.293501742257881 \tabularnewline
7 & 0.0838570004448753 \tabularnewline
8 & -0.0254261825146424 \tabularnewline
9 & 0.0246644801372083 \tabularnewline
10 & 0.115237736640415 \tabularnewline
11 & -0.186159200177912 \tabularnewline
12 & -0.250284328370368 \tabularnewline
13 & -0.0103778347973826 \tabularnewline
14 & 0.0260140306600202 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27909&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]-14[/C][C]-0.104464675354515[/C][/ROW]
[ROW][C]-13[/C][C]-0.143758899780193[/C][/ROW]
[ROW][C]-12[/C][C]0.0287633685685997[/C][/ROW]
[ROW][C]-11[/C][C]-0.0165133196106402[/C][/ROW]
[ROW][C]-10[/C][C]-0.00738484057274178[/C][/ROW]
[ROW][C]-9[/C][C]-0.0505316104014827[/C][/ROW]
[ROW][C]-8[/C][C]0.0259538805096431[/C][/ROW]
[ROW][C]-7[/C][C]0.0465290541051726[/C][/ROW]
[ROW][C]-6[/C][C]-0.272738276932281[/C][/ROW]
[ROW][C]-5[/C][C]-0.040255602507959[/C][/ROW]
[ROW][C]-4[/C][C]-0.0154906354969193[/C][/ROW]
[ROW][C]-3[/C][C]0.164367810084432[/C][/ROW]
[ROW][C]-2[/C][C]-0.0711002416608856[/C][/ROW]
[ROW][C]-1[/C][C]0.223804701351708[/C][/ROW]
[ROW][C]0[/C][C]0.349421640015357[/C][/ROW]
[ROW][C]1[/C][C]0.0296528177363326[/C][/ROW]
[ROW][C]2[/C][C]-0.158620840222523[/C][/ROW]
[ROW][C]3[/C][C]-0.206838024519017[/C][/ROW]
[ROW][C]4[/C][C]-0.041336304697993[/C][/ROW]
[ROW][C]5[/C][C]0.155673493419409[/C][/ROW]
[ROW][C]6[/C][C]0.293501742257881[/C][/ROW]
[ROW][C]7[/C][C]0.0838570004448753[/C][/ROW]
[ROW][C]8[/C][C]-0.0254261825146424[/C][/ROW]
[ROW][C]9[/C][C]0.0246644801372083[/C][/ROW]
[ROW][C]10[/C][C]0.115237736640415[/C][/ROW]
[ROW][C]11[/C][C]-0.186159200177912[/C][/ROW]
[ROW][C]12[/C][C]-0.250284328370368[/C][/ROW]
[ROW][C]13[/C][C]-0.0103778347973826[/C][/ROW]
[ROW][C]14[/C][C]0.0260140306600202[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27909&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27909&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])
-14-0.104464675354515
-13-0.143758899780193
-120.0287633685685997
-11-0.0165133196106402
-10-0.00738484057274178
-9-0.0505316104014827
-80.0259538805096431
-70.0465290541051726
-6-0.272738276932281
-5-0.040255602507959
-4-0.0154906354969193
-30.164367810084432
-2-0.0711002416608856
-10.223804701351708
00.349421640015357
10.0296528177363326
2-0.158620840222523
3-0.206838024519017
4-0.041336304697993
50.155673493419409
60.293501742257881
70.0838570004448753
8-0.0254261825146424
90.0246644801372083
100.115237736640415
11-0.186159200177912
12-0.250284328370368
13-0.0103778347973826
140.0260140306600202


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 60 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 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')