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 computationSun, 30 Nov 2008 06:40:30 -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/Nov/30/t12280525282nfo0nbpjlka9z5.htm/, Retrieved Mon, 20 May 2024 07:29:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26502, Retrieved Mon, 20 May 2024 07:29:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact197

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]
-    D  [Univariate Data Series] [Non Stationary Ti...] [2008-11-30 09:45:17] [b82ef11dce0545f3fd4676ec3ebed828]
- RMP     [Standard Deviation-Mean Plot] [Non Stationary Ti...] [2008-11-30 10:32:49] [b82ef11dce0545f3fd4676ec3ebed828]
-   PD      [Standard Deviation-Mean Plot] [Non Stationary Ti...] [2008-11-30 13:22:54] [b82ef11dce0545f3fd4676ec3ebed828]
F RM D          [Cross Correlation Function] [Non Stationary Ti...] [2008-11-30 13:40:30] [4b953869c7238aca4b6e0cfb0c5cddd6] [Current]
-   P             [Cross Correlation Function] [Non Stationary Ti...] [2008-12-05 17:18:17] [b82ef11dce0545f3fd4676ec3ebed828]
Feedback Forum
2008-12-03 16:19:06 [Ken Van den Heuvel] [reply] 
Q8:
Opmerking mbt je differentiatiewaarden ! Je test deze niet !
Je neemt deze gewoon over van Q6 terwijl het hier over andere data gaat. Voor zover je weet moet je misschien wel 2x seizoenaal differentieren om de reeks stationair te maken...

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
97.4
97.0
105.4
102.7
98.1
104.5
87.4
89.9
109.8
111.7
98.6
96.9
95.1
97.0
112.7
102.9
97.4
111.4
87.4
96.8
114.1
110.3
103.9
101.6
94.6
95.9
104.7
102.8
98.1
113.9
80.9
95.7
113.2
105.9
108.8
102.3
99.0
100.7
115.5
100.7
109.9
114.6
85.4
100.5
114.8
116.5
112.9
102.0
106.0
105.3
118.8
106.1
109.3
117.2
92.5
104.2
112.5
122.4
113.3
100.0
110.7
112.8
109.8
117.3
109.1
115.9
95.7
Dataseries Y:
Download CSV
Histogram 
104.2
103.2
112.7
106.4
102.6
110.6
95.2
89.0
112.5
116.8
107.2
113.6
101.8
102.6
122.7
110.3
110.5
121.6
100.3
100.7
123.4
127.1
124.1
131.2
111.6
114.2
130.1
125.9
119.0
133.8
107.5
113.5
134.4
126.8
135.6
139.9
129.8
131.0
153.1
134.1
144.1
155.9
123.3
128.1
144.3
153.0
149.9
150.9
141.0
138.9
157.4
142.9
151.7
161.0
138.5
135.9
151.5
164.0
159.1
157.0
142.1
144.8
152.1
154.6
148.7
157.7
146.7

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=26502&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=26502&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26502&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 series-0.3
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 series0.5
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-140.0273486355074853
-130.0104987719229759
-120.0335885064910762
-11-0.0161775862417543
-10-0.251950769793217
-90.309488238247375
-8-0.087472715138862
-7-0.0487862548231087
-60.0437495892268465
-50.0486752226013565
-4-0.216815540996087
-30.284900260357756
-20.055761914037438
-1-0.544551306398926
00.823544183480936
1-0.549553006737968
20.158004798624852
30.0369676456321258
4-0.0602750398450302
5-0.0559140885528348
60.184203155082454
7-0.161495617085373
8-0.075870535125546
90.276555430985784
10-0.231940708951703
110.0403284259985564
12-0.0318674698180571
130.0599343951630418
14-0.060235579278708

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & -0.3 \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 & 0.5 \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 & 0.0273486355074853 \tabularnewline
-13 & 0.0104987719229759 \tabularnewline
-12 & 0.0335885064910762 \tabularnewline
-11 & -0.0161775862417543 \tabularnewline
-10 & -0.251950769793217 \tabularnewline
-9 & 0.309488238247375 \tabularnewline
-8 & -0.087472715138862 \tabularnewline
-7 & -0.0487862548231087 \tabularnewline
-6 & 0.0437495892268465 \tabularnewline
-5 & 0.0486752226013565 \tabularnewline
-4 & -0.216815540996087 \tabularnewline
-3 & 0.284900260357756 \tabularnewline
-2 & 0.055761914037438 \tabularnewline
-1 & -0.544551306398926 \tabularnewline
0 & 0.823544183480936 \tabularnewline
1 & -0.549553006737968 \tabularnewline
2 & 0.158004798624852 \tabularnewline
3 & 0.0369676456321258 \tabularnewline
4 & -0.0602750398450302 \tabularnewline
5 & -0.0559140885528348 \tabularnewline
6 & 0.184203155082454 \tabularnewline
7 & -0.161495617085373 \tabularnewline
8 & -0.075870535125546 \tabularnewline
9 & 0.276555430985784 \tabularnewline
10 & -0.231940708951703 \tabularnewline
11 & 0.0403284259985564 \tabularnewline
12 & -0.0318674698180571 \tabularnewline
13 & 0.0599343951630418 \tabularnewline
14 & -0.060235579278708 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26502&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]-0.3[/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]0.5[/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]0.0273486355074853[/C][/ROW]
[ROW][C]-13[/C][C]0.0104987719229759[/C][/ROW]
[ROW][C]-12[/C][C]0.0335885064910762[/C][/ROW]
[ROW][C]-11[/C][C]-0.0161775862417543[/C][/ROW]
[ROW][C]-10[/C][C]-0.251950769793217[/C][/ROW]
[ROW][C]-9[/C][C]0.309488238247375[/C][/ROW]
[ROW][C]-8[/C][C]-0.087472715138862[/C][/ROW]
[ROW][C]-7[/C][C]-0.0487862548231087[/C][/ROW]
[ROW][C]-6[/C][C]0.0437495892268465[/C][/ROW]
[ROW][C]-5[/C][C]0.0486752226013565[/C][/ROW]
[ROW][C]-4[/C][C]-0.216815540996087[/C][/ROW]
[ROW][C]-3[/C][C]0.284900260357756[/C][/ROW]
[ROW][C]-2[/C][C]0.055761914037438[/C][/ROW]
[ROW][C]-1[/C][C]-0.544551306398926[/C][/ROW]
[ROW][C]0[/C][C]0.823544183480936[/C][/ROW]
[ROW][C]1[/C][C]-0.549553006737968[/C][/ROW]
[ROW][C]2[/C][C]0.158004798624852[/C][/ROW]
[ROW][C]3[/C][C]0.0369676456321258[/C][/ROW]
[ROW][C]4[/C][C]-0.0602750398450302[/C][/ROW]
[ROW][C]5[/C][C]-0.0559140885528348[/C][/ROW]
[ROW][C]6[/C][C]0.184203155082454[/C][/ROW]
[ROW][C]7[/C][C]-0.161495617085373[/C][/ROW]
[ROW][C]8[/C][C]-0.075870535125546[/C][/ROW]
[ROW][C]9[/C][C]0.276555430985784[/C][/ROW]
[ROW][C]10[/C][C]-0.231940708951703[/C][/ROW]
[ROW][C]11[/C][C]0.0403284259985564[/C][/ROW]
[ROW][C]12[/C][C]-0.0318674698180571[/C][/ROW]
[ROW][C]13[/C][C]0.0599343951630418[/C][/ROW]
[ROW][C]14[/C][C]-0.060235579278708[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26502&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26502&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 series-0.3
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 series0.5
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-140.0273486355074853
-130.0104987719229759
-120.0335885064910762
-11-0.0161775862417543
-10-0.251950769793217
-90.309488238247375
-8-0.087472715138862
-7-0.0487862548231087
-60.0437495892268465
-50.0486752226013565
-4-0.216815540996087
-30.284900260357756
-20.055761914037438
-1-0.544551306398926
00.823544183480936
1-0.549553006737968
20.158004798624852
30.0369676456321258
4-0.0602750398450302
5-0.0559140885528348
60.184203155082454
7-0.161495617085373
8-0.075870535125546
90.276555430985784
10-0.231940708951703
110.0403284259985564
12-0.0318674698180571
130.0599343951630418
14-0.060235579278708


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 4 ;
Parameters (R input):
par1 = -0.3 ; par2 = 1 ; par3 = 1 ; par4 = 12 ; par5 = 0.5 ; 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')