FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 10:52:21 -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/t1228153983q5njbvbsr8phmyz.htm/, Retrieved Sat, 11 May 2024 16:38:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27064, Retrieved Sat, 11 May 2024 16:38:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact178

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Law of Averages] [Random Walk Simul...] [2008-11-25 18:05:16] [b98453cac15ba1066b407e146608df68]
F RMPD    [Cross Correlation Function] [Cross correlation ] [2008-12-01 17:52:21] [8758b22b4a10c08c31202f233362e983] [Current]
Feedback Forum
2008-12-04 16:35:34 [Matthieu Blondeau] [reply] 
We kunnen zien dat er hoge correlatiewaarden zijn, die ook over de rand van het 95%-betrouwbaarheidsinterval liggen, deze zijn dus niet aan de toeval toe te schrijven. Tevens kunnen we een bepaald patroon terugvinden.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
14211
13646,8
12224,6
15916,4
16535,9
15796
14418,6
15044,5
14944,2
16754,8
14254
15454,9
15644,8
14568,3
12520,2
14803
15873,2
14755,3
12875,1
14291,1
14205,3
15859,4
15258,9
15498,6
15106,5
15023,6
12083
15761,3
16943
15070,3
13659,6
14768,9
14725,1
15998,1
15370,6
14956,9
15469,7
15101,8
11703,7
16283,6
16726,5
14968,9
14861
14583,3
15305,8
17903,9
16379,4
15420,3
17870,5
15912,8
13866,5
17823,2
17872
17420,4
16704,4
15991,2
16583,6
19123,5
17838,7
17209,4
18586,5
16258,1
15141,6
19202,1
17746,5
19090,1
18040,3
17515,5
17751,8
21072,4
17170
19439,5
19795,4
17574,9
16165,4
19464,6
19932,1
19961,2
17343,4
18924,2
18574,1
21350,6
18594,6
19823,1
20844,4
19640,2
17735,4
19813,6
22238,5
20682,2
17818,6
21872,1
22117
21865,9
23451,3
20953,7
22497,3
Dataseries Y:
Download CSV
Histogram 
13698,3
12477,6
13139,7
14532,2
15167
16071,1
14827,5
15082
14772,7
16083
14272,5
15223,3
14897,3
13062,6
12603,8
13629,8
14421,1
13978,3
12927,9
13429,9
13470,1
14785,8
14292
14308,8
14013
13240,9
12153,4
14289,7
15669,2
14169,5
14569,8
14469,1
14264,9
15320,9
14433,5
13691,5
14194,1
13519,2
11857,9
14616
15643,4
14077,2
14887,5
14159,9
14643
17192,5
15386,1
14287,1
17526,6
14497
14398,3
16629,6
16670,7
16614,8
16869,2
15663,9
16359,9
18447,7
16889
16505
18320,9
15052,1
15699,8
18135,3
16768,7
18883
19021
18101,9
17776,1
21489,9
17065,3
18690
18953,1
16398,9
16895,7
18553
19270
19422,1
17579,4
18637,3
18076,7
20438,6
18075,2
19563
19899,2
19227,5
17789,6
19220,8
22058,6
21230,8
19504,4
23913,1
23165,7
23574,3
25002
22603,9
23408,6

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=27064&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=27064&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27064&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 series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)1
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-160.389036977249799
-150.422961489725233
-140.425152619767847
-130.474575147489699
-120.619258811009467
-110.540603135019807
-100.463747260927964
-90.540374566320081
-80.596974929322216
-70.575127643005209
-60.634850781783579
-50.621066844949778
-40.641461703535266
-30.687346665348966
-20.696183966873782
-10.75000939786357
00.961746331021194
10.766603762414564
20.66679676718188
30.685580336295596
40.644189562116262
50.587672328113053
60.590836257500571
70.519873461111146
80.489321673270798
90.456428574843099
100.410878948277435
110.445464293746756
120.53718118453928
130.416616297209442
140.34325872269613
150.373815388607842
160.348916811762301

\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 & 0 \tabularnewline
Degree of seasonal differencing (D) of X series & 0 \tabularnewline
Seasonal Period (s) & 1 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & 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
-16 & 0.389036977249799 \tabularnewline
-15 & 0.422961489725233 \tabularnewline
-14 & 0.425152619767847 \tabularnewline
-13 & 0.474575147489699 \tabularnewline
-12 & 0.619258811009467 \tabularnewline
-11 & 0.540603135019807 \tabularnewline
-10 & 0.463747260927964 \tabularnewline
-9 & 0.540374566320081 \tabularnewline
-8 & 0.596974929322216 \tabularnewline
-7 & 0.575127643005209 \tabularnewline
-6 & 0.634850781783579 \tabularnewline
-5 & 0.621066844949778 \tabularnewline
-4 & 0.641461703535266 \tabularnewline
-3 & 0.687346665348966 \tabularnewline
-2 & 0.696183966873782 \tabularnewline
-1 & 0.75000939786357 \tabularnewline
0 & 0.961746331021194 \tabularnewline
1 & 0.766603762414564 \tabularnewline
2 & 0.66679676718188 \tabularnewline
3 & 0.685580336295596 \tabularnewline
4 & 0.644189562116262 \tabularnewline
5 & 0.587672328113053 \tabularnewline
6 & 0.590836257500571 \tabularnewline
7 & 0.519873461111146 \tabularnewline
8 & 0.489321673270798 \tabularnewline
9 & 0.456428574843099 \tabularnewline
10 & 0.410878948277435 \tabularnewline
11 & 0.445464293746756 \tabularnewline
12 & 0.53718118453928 \tabularnewline
13 & 0.416616297209442 \tabularnewline
14 & 0.34325872269613 \tabularnewline
15 & 0.373815388607842 \tabularnewline
16 & 0.348916811762301 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27064&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]0[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of X series[/C][C]0[/C][/ROW]
[ROW][C]Seasonal Period (s)[/C][C]1[/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]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]-16[/C][C]0.389036977249799[/C][/ROW]
[ROW][C]-15[/C][C]0.422961489725233[/C][/ROW]
[ROW][C]-14[/C][C]0.425152619767847[/C][/ROW]
[ROW][C]-13[/C][C]0.474575147489699[/C][/ROW]
[ROW][C]-12[/C][C]0.619258811009467[/C][/ROW]
[ROW][C]-11[/C][C]0.540603135019807[/C][/ROW]
[ROW][C]-10[/C][C]0.463747260927964[/C][/ROW]
[ROW][C]-9[/C][C]0.540374566320081[/C][/ROW]
[ROW][C]-8[/C][C]0.596974929322216[/C][/ROW]
[ROW][C]-7[/C][C]0.575127643005209[/C][/ROW]
[ROW][C]-6[/C][C]0.634850781783579[/C][/ROW]
[ROW][C]-5[/C][C]0.621066844949778[/C][/ROW]
[ROW][C]-4[/C][C]0.641461703535266[/C][/ROW]
[ROW][C]-3[/C][C]0.687346665348966[/C][/ROW]
[ROW][C]-2[/C][C]0.696183966873782[/C][/ROW]
[ROW][C]-1[/C][C]0.75000939786357[/C][/ROW]
[ROW][C]0[/C][C]0.961746331021194[/C][/ROW]
[ROW][C]1[/C][C]0.766603762414564[/C][/ROW]
[ROW][C]2[/C][C]0.66679676718188[/C][/ROW]
[ROW][C]3[/C][C]0.685580336295596[/C][/ROW]
[ROW][C]4[/C][C]0.644189562116262[/C][/ROW]
[ROW][C]5[/C][C]0.587672328113053[/C][/ROW]
[ROW][C]6[/C][C]0.590836257500571[/C][/ROW]
[ROW][C]7[/C][C]0.519873461111146[/C][/ROW]
[ROW][C]8[/C][C]0.489321673270798[/C][/ROW]
[ROW][C]9[/C][C]0.456428574843099[/C][/ROW]
[ROW][C]10[/C][C]0.410878948277435[/C][/ROW]
[ROW][C]11[/C][C]0.445464293746756[/C][/ROW]
[ROW][C]12[/C][C]0.53718118453928[/C][/ROW]
[ROW][C]13[/C][C]0.416616297209442[/C][/ROW]
[ROW][C]14[/C][C]0.34325872269613[/C][/ROW]
[ROW][C]15[/C][C]0.373815388607842[/C][/ROW]
[ROW][C]16[/C][C]0.348916811762301[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27064&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27064&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 series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)1
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-160.389036977249799
-150.422961489725233
-140.425152619767847
-130.474575147489699
-120.619258811009467
-110.540603135019807
-100.463747260927964
-90.540374566320081
-80.596974929322216
-70.575127643005209
-60.634850781783579
-50.621066844949778
-40.641461703535266
-30.687346665348966
-20.696183966873782
-10.75000939786357
00.961746331021194
10.766603762414564
20.66679676718188
30.685580336295596
40.644189562116262
50.587672328113053
60.590836257500571
70.519873461111146
80.489321673270798
90.456428574843099
100.410878948277435
110.445464293746756
120.53718118453928
130.416616297209442
140.34325872269613
150.373815388607842
160.348916811762301


Figures (Output of Computation)

Input Parameters & R Code

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