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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, 14 Dec 2008 07:31:56 -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/14/t1229265170wguaesdfkptvvce.htm/, Retrieved Sat, 18 May 2024 13:47:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=33395, Retrieved Sat, 18 May 2024 13:47:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact215

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [(Partial) Autocorrelation Function] [VAC (Partiële) au...] [2008-12-14 13:47:12] [379d6c32f73e3218fd773d79e4063d07]
-    D  [(Partial) Autocorrelation Function] [VAC (Partiële) au...] [2008-12-14 14:21:15] [379d6c32f73e3218fd773d79e4063d07]
- RM D      [Cross Correlation Function] [VAC cross correla...] [2008-12-14 14:31:56] [490fee4f334e2e025c95681783e3fd0b] [Current]
-   PD        [Cross Correlation Function] [VAC cross correla...] [2008-12-23 15:12:44] [379d6c32f73e3218fd773d79e4063d07]
-  M            [Cross Correlation Function] [Cross Cerelation ...] [2010-01-23 19:21:52] [f1bd7399181c649098ca7b814ee0e027]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
124,1
124,4
115,7
108,3
102,3
104,6
104
103,5
96
96,6
95,4
92,1
93
90,4
93,3
97,1
111
114,1
113,3
111
107,2
118,3
134,1
139
116,7
112,5
122,8
130
125,6
123,8
135,8
136,4
135,3
149,5
159,6
161,4
175,2
199,5
245
257,8
Dataseries Y:
Download CSV
Histogram 
188,5
188,6
191,9
193,5
194,9
194,9
196,2
196,2
198
198,6
201,3
203,5
204,1
204,8
206,5
207,8
208,6
209,7
210
211,7
212,4
213,7
214,8
216,4
217,5
218,6
220,4
221,8
222,5
223,4
225,5
226,5
227,8
228,5
229,1
229,9
230,8
231,9
236
237,5

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=33395&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=33395&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33395&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
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)4
Box-Cox transformation parameter (lambda) of Y series0.3
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-120.118252325977984
-110.0315714615951283
-10-0.199914970930381
-9-0.00694395671243361
-80.124698351726257
-70.021636911768048
-6-0.0188945606774148
-5-0.134548144712469
-4-0.0542880649826198
-3-0.050421717483187
-20.078396414420375
-1-0.0775416575902796
0-0.0170817970414505
1-0.136613960563010
20.111577999470481
30.0415312928089281
40.0637577351757702
5-0.197241080662429
6-0.214878777169000
70.024437891251442
80.201433414345398
90.174178830326559
10-0.0291761445795847
11-0.110575953209002
12-0.262260467418864

\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) & 4 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & 0.3 \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
-12 & 0.118252325977984 \tabularnewline
-11 & 0.0315714615951283 \tabularnewline
-10 & -0.199914970930381 \tabularnewline
-9 & -0.00694395671243361 \tabularnewline
-8 & 0.124698351726257 \tabularnewline
-7 & 0.021636911768048 \tabularnewline
-6 & -0.0188945606774148 \tabularnewline
-5 & -0.134548144712469 \tabularnewline
-4 & -0.0542880649826198 \tabularnewline
-3 & -0.050421717483187 \tabularnewline
-2 & 0.078396414420375 \tabularnewline
-1 & -0.0775416575902796 \tabularnewline
0 & -0.0170817970414505 \tabularnewline
1 & -0.136613960563010 \tabularnewline
2 & 0.111577999470481 \tabularnewline
3 & 0.0415312928089281 \tabularnewline
4 & 0.0637577351757702 \tabularnewline
5 & -0.197241080662429 \tabularnewline
6 & -0.214878777169000 \tabularnewline
7 & 0.024437891251442 \tabularnewline
8 & 0.201433414345398 \tabularnewline
9 & 0.174178830326559 \tabularnewline
10 & -0.0291761445795847 \tabularnewline
11 & -0.110575953209002 \tabularnewline
12 & -0.262260467418864 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33395&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]4[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda) of Y series[/C][C]0.3[/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]-12[/C][C]0.118252325977984[/C][/ROW]
[ROW][C]-11[/C][C]0.0315714615951283[/C][/ROW]
[ROW][C]-10[/C][C]-0.199914970930381[/C][/ROW]
[ROW][C]-9[/C][C]-0.00694395671243361[/C][/ROW]
[ROW][C]-8[/C][C]0.124698351726257[/C][/ROW]
[ROW][C]-7[/C][C]0.021636911768048[/C][/ROW]
[ROW][C]-6[/C][C]-0.0188945606774148[/C][/ROW]
[ROW][C]-5[/C][C]-0.134548144712469[/C][/ROW]
[ROW][C]-4[/C][C]-0.0542880649826198[/C][/ROW]
[ROW][C]-3[/C][C]-0.050421717483187[/C][/ROW]
[ROW][C]-2[/C][C]0.078396414420375[/C][/ROW]
[ROW][C]-1[/C][C]-0.0775416575902796[/C][/ROW]
[ROW][C]0[/C][C]-0.0170817970414505[/C][/ROW]
[ROW][C]1[/C][C]-0.136613960563010[/C][/ROW]
[ROW][C]2[/C][C]0.111577999470481[/C][/ROW]
[ROW][C]3[/C][C]0.0415312928089281[/C][/ROW]
[ROW][C]4[/C][C]0.0637577351757702[/C][/ROW]
[ROW][C]5[/C][C]-0.197241080662429[/C][/ROW]
[ROW][C]6[/C][C]-0.214878777169000[/C][/ROW]
[ROW][C]7[/C][C]0.024437891251442[/C][/ROW]
[ROW][C]8[/C][C]0.201433414345398[/C][/ROW]
[ROW][C]9[/C][C]0.174178830326559[/C][/ROW]
[ROW][C]10[/C][C]-0.0291761445795847[/C][/ROW]
[ROW][C]11[/C][C]-0.110575953209002[/C][/ROW]
[ROW][C]12[/C][C]-0.262260467418864[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33395&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33395&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)4
Box-Cox transformation parameter (lambda) of Y series0.3
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-120.118252325977984
-110.0315714615951283
-10-0.199914970930381
-9-0.00694395671243361
-80.124698351726257
-70.021636911768048
-6-0.0188945606774148
-5-0.134548144712469
-4-0.0542880649826198
-3-0.050421717483187
-20.078396414420375
-1-0.0775416575902796
0-0.0170817970414505
1-0.136613960563010
20.111577999470481
30.0415312928089281
40.0637577351757702
5-0.197241080662429
6-0.214878777169000
70.024437891251442
80.201433414345398
90.174178830326559
10-0.0291761445795847
11-0.110575953209002
12-0.262260467418864


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = Default ; par2 = 1.1 ; par3 = 1 ; par4 = 1 ; par5 = 4 ;
Parameters (R input):
par1 = 1.0 ; par2 = 1 ; par3 = 1 ; par4 = 4 ; par5 = 0.3 ; 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')