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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 computationTue, 02 Dec 2008 14:57:15 -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/t1228255085oci9qreelaua3av.htm/, Retrieved Tue, 14 May 2024 04:48:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28500, Retrieved Tue, 14 May 2024 04:48:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact229

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]
- RM D  [Cross Correlation Function] [Opdracht 1 - Blok...] [2008-11-26 22:16:36] [8094ad203a218aaca2d1cea2c78c2d6e]
F    D      [Cross Correlation Function] [Opdracht 1 - Blok...] [2008-12-02 21:57:15] [1351baa662f198be3bff32f9007a9a6d] [Current]
- RM D        [Variance Reduction Matrix] [Opdracht 1 - Blok...] [2008-12-02 22:00:09] [8094ad203a218aaca2d1cea2c78c2d6e]
- RMPD          [Cross Correlation Function] [Opdracht 1 - Blok...] [2008-12-08 17:35:51] [8094ad203a218aaca2d1cea2c78c2d6e]
-   PD            [Cross Correlation Function] [Verbetering Q9 (b...] [2008-12-08 20:51:45] [8094ad203a218aaca2d1cea2c78c2d6e]
-             [Cross Correlation Function] [Opdracht 1 - Blok...] [2008-12-02 22:03:42] [8094ad203a218aaca2d1cea2c78c2d6e]
Feedback Forum
2008-12-08 21:49:29 [Nathalie Daneels] [reply] 
Evaluatie opdracht 1 - Blok 17 (Q7):

De tabel en de grafiek zijn correct geproduceerd. Ook de conclusie kan eigenlijk niet meer aangevuld worden, eventueel dit nog:
* We gaan Yt voorspellen op basis van Xt of van het verleden van Xt (of van de toekomst van Xt).
* ruwe reeks (= een tijdreeks die niet wordt getransformeerd en/of gedifferentieerd: Die niet stationair wordt gemaakt)

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
98.1
101.1
111.1
93.3
100
108
70.4
75.4
105.5
112.3
102.5
93.5
86.7
95.2
103.8
97
95.5
101
67.5
64
106.7
100.6
101.2
93.1
84.2
85.8
91.8
92.4
80.3
79.7
62.5
57.1
100.8
100.7
86.2
83.2
71.7
77.5
89.8
80.3
78.7
93.8
57.6
60.6
91
85.3
77.4
77.3
68.3
69.9
81.7
75.1
69.9
84
54.3
60
89.9
77
85.3
77.6
69.2
Dataseries Y:
Download CSV
Histogram 
13
8
7
3
3
4
4
0
-4
-14
-18
-8
-1
1
2
0
1
0
-1
-3
-3
-3
-4
-8
-9
-13
-18
-11
-9
-10
-13
-11
-5
-15
-6
-6
-3
-1
-3
-4
-6
0
-4
-2
-2
-6
-7
-6
-6
-3
-2
-5
-11
-11
-11
-10
-14
-8
-9
-5
-1

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28500&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28500&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28500&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'George Udny Yule' @ 72.249.76.132






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)12
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])
-140.0297011038653348
-13-0.0252416828196672
-120.0421315939351509
-110.0297762516624697
-10-0.00439331809434676
-9-0.0221199774041418
-8-0.043659412233
-70.000320275308513181
-60.0112327086876606
-50.0989672596220955
-40.249619596263377
-30.294824107081862
-20.24080841465483
-10.138616240856376
00.215200081578651
10.257229875896064
20.283323583191409
30.270451075651202
40.221192572296261
50.149426825858509
60.0846064044184674
70.176870906692208
80.325484120633165
90.387532250509905
100.188522594092055
110.0334965259578471
120.0668960453348482
130.0888069722163867
140.129534016180925

\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) & 12 \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
-14 & 0.0297011038653348 \tabularnewline
-13 & -0.0252416828196672 \tabularnewline
-12 & 0.0421315939351509 \tabularnewline
-11 & 0.0297762516624697 \tabularnewline
-10 & -0.00439331809434676 \tabularnewline
-9 & -0.0221199774041418 \tabularnewline
-8 & -0.043659412233 \tabularnewline
-7 & 0.000320275308513181 \tabularnewline
-6 & 0.0112327086876606 \tabularnewline
-5 & 0.0989672596220955 \tabularnewline
-4 & 0.249619596263377 \tabularnewline
-3 & 0.294824107081862 \tabularnewline
-2 & 0.24080841465483 \tabularnewline
-1 & 0.138616240856376 \tabularnewline
0 & 0.215200081578651 \tabularnewline
1 & 0.257229875896064 \tabularnewline
2 & 0.283323583191409 \tabularnewline
3 & 0.270451075651202 \tabularnewline
4 & 0.221192572296261 \tabularnewline
5 & 0.149426825858509 \tabularnewline
6 & 0.0846064044184674 \tabularnewline
7 & 0.176870906692208 \tabularnewline
8 & 0.325484120633165 \tabularnewline
9 & 0.387532250509905 \tabularnewline
10 & 0.188522594092055 \tabularnewline
11 & 0.0334965259578471 \tabularnewline
12 & 0.0668960453348482 \tabularnewline
13 & 0.0888069722163867 \tabularnewline
14 & 0.129534016180925 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28500&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]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]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]-14[/C][C]0.0297011038653348[/C][/ROW]
[ROW][C]-13[/C][C]-0.0252416828196672[/C][/ROW]
[ROW][C]-12[/C][C]0.0421315939351509[/C][/ROW]
[ROW][C]-11[/C][C]0.0297762516624697[/C][/ROW]
[ROW][C]-10[/C][C]-0.00439331809434676[/C][/ROW]
[ROW][C]-9[/C][C]-0.0221199774041418[/C][/ROW]
[ROW][C]-8[/C][C]-0.043659412233[/C][/ROW]
[ROW][C]-7[/C][C]0.000320275308513181[/C][/ROW]
[ROW][C]-6[/C][C]0.0112327086876606[/C][/ROW]
[ROW][C]-5[/C][C]0.0989672596220955[/C][/ROW]
[ROW][C]-4[/C][C]0.249619596263377[/C][/ROW]
[ROW][C]-3[/C][C]0.294824107081862[/C][/ROW]
[ROW][C]-2[/C][C]0.24080841465483[/C][/ROW]
[ROW][C]-1[/C][C]0.138616240856376[/C][/ROW]
[ROW][C]0[/C][C]0.215200081578651[/C][/ROW]
[ROW][C]1[/C][C]0.257229875896064[/C][/ROW]
[ROW][C]2[/C][C]0.283323583191409[/C][/ROW]
[ROW][C]3[/C][C]0.270451075651202[/C][/ROW]
[ROW][C]4[/C][C]0.221192572296261[/C][/ROW]
[ROW][C]5[/C][C]0.149426825858509[/C][/ROW]
[ROW][C]6[/C][C]0.0846064044184674[/C][/ROW]
[ROW][C]7[/C][C]0.176870906692208[/C][/ROW]
[ROW][C]8[/C][C]0.325484120633165[/C][/ROW]
[ROW][C]9[/C][C]0.387532250509905[/C][/ROW]
[ROW][C]10[/C][C]0.188522594092055[/C][/ROW]
[ROW][C]11[/C][C]0.0334965259578471[/C][/ROW]
[ROW][C]12[/C][C]0.0668960453348482[/C][/ROW]
[ROW][C]13[/C][C]0.0888069722163867[/C][/ROW]
[ROW][C]14[/C][C]0.129534016180925[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28500&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28500&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)12
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])
-140.0297011038653348
-13-0.0252416828196672
-120.0421315939351509
-110.0297762516624697
-10-0.00439331809434676
-9-0.0221199774041418
-8-0.043659412233
-70.000320275308513181
-60.0112327086876606
-50.0989672596220955
-40.249619596263377
-30.294824107081862
-20.24080841465483
-10.138616240856376
00.215200081578651
10.257229875896064
20.283323583191409
30.270451075651202
40.221192572296261
50.149426825858509
60.0846064044184674
70.176870906692208
80.325484120633165
90.387532250509905
100.188522594092055
110.0334965259578471
120.0668960453348482
130.0888069722163867
140.129534016180925


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = Airline ; par2 = Box-Jenkins ; par3 = Airline Passengers ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; 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')