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 computationTue, 02 Dec 2008 13:58:46 -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/t12282516443yl4psmuf64dohv.htm/, Retrieved Sun, 19 May 2024 10:46:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28454, Retrieved Sun, 19 May 2024 10:46:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact137

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Cross Correlation Function] [Cross Correlation...] [2008-12-02 20:58:46] [96839c4b6d4e03ef3851369c676780bf] [Current]
Feedback Forum
2008-12-06 14:22:11 [Ken Wright] [reply] 
juist, je bent er dus in geslaagd om jouw tijdreeks zodanig stationair te maken, zodat er niet meer gebruik kan gemaakt worden van het verleden van x om y te voorspellen.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
117,6
121,7
127,3
129,8
137,1
141,4
137,4
130,7
117,2
110,8
111,4
108,2
108,8
110,2
109,5
109,5
116
111,2
112,1
114
119,1
114,1
115,1
115,4
110,8
116
119,2
126,5
127,8
131,3
140,3
137,3
143
134,5
139,9
159,3
170,4
175
175,8
180,9
180,3
169,6
172,3
184,8
177,7
184,6
211,4
215,3
215,9
244,7
259,3
289
310,9
321
315,1
333,2
314,1
284,7
273,9
216
196,4
Dataseries Y:
Download CSV
Histogram 
147,4
148
158,1
165
187
190,3
182,4
168,8
151,2
120,1
112,5
106,2
107,1
108,5
106,5
108,3
125,6
124
127,2
136,9
135,8
124,3
115,4
113,6
114,4
118,4
117
116,5
115,4
113,6
117,4
116,9
116,4
111,1
110,2
118,9
131,8
130,6
138,3
148,4
148,7
144,3
152,5
162,9
167,2
166,5
185,6
193,2
207,8
223,4
246,4
266,3
264,3
255,8
259,5
289,1
288,5
243,8
224,8
177
171,6

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'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 & 1 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28454&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28454&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28454&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'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)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])
-140.0241678426430553
-13-0.000214253645876006
-120.0246290331923163
-110.0512901499991028
-100.0180970595355700
-90.0757533165591595
-80.0664409257102138
-70.0385113159232608
-6-0.00154695922903752
-5-0.00357254464226285
-4-0.0701563737715954
-3-0.0683988226333868
-2-0.0951054587223037
-1-0.218262567848526
0-0.114732011124124
1-0.116585767554604
2-0.0966337782694611
3-0.0552028184312031
4-0.0545599008219181
5-0.0543369381187143
6-0.0581488527265672
7-0.0227467220888765
8-0.0192595342542730
9-0.0196716505235826
10-0.0174835641222171
11-0.0256153579576687
12-0.0052197103218405
13-0.0122854415374478
14-0.0125345081645825

\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) & 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
-14 & 0.0241678426430553 \tabularnewline
-13 & -0.000214253645876006 \tabularnewline
-12 & 0.0246290331923163 \tabularnewline
-11 & 0.0512901499991028 \tabularnewline
-10 & 0.0180970595355700 \tabularnewline
-9 & 0.0757533165591595 \tabularnewline
-8 & 0.0664409257102138 \tabularnewline
-7 & 0.0385113159232608 \tabularnewline
-6 & -0.00154695922903752 \tabularnewline
-5 & -0.00357254464226285 \tabularnewline
-4 & -0.0701563737715954 \tabularnewline
-3 & -0.0683988226333868 \tabularnewline
-2 & -0.0951054587223037 \tabularnewline
-1 & -0.218262567848526 \tabularnewline
0 & -0.114732011124124 \tabularnewline
1 & -0.116585767554604 \tabularnewline
2 & -0.0966337782694611 \tabularnewline
3 & -0.0552028184312031 \tabularnewline
4 & -0.0545599008219181 \tabularnewline
5 & -0.0543369381187143 \tabularnewline
6 & -0.0581488527265672 \tabularnewline
7 & -0.0227467220888765 \tabularnewline
8 & -0.0192595342542730 \tabularnewline
9 & -0.0196716505235826 \tabularnewline
10 & -0.0174835641222171 \tabularnewline
11 & -0.0256153579576687 \tabularnewline
12 & -0.0052197103218405 \tabularnewline
13 & -0.0122854415374478 \tabularnewline
14 & -0.0125345081645825 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28454&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]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]-14[/C][C]0.0241678426430553[/C][/ROW]
[ROW][C]-13[/C][C]-0.000214253645876006[/C][/ROW]
[ROW][C]-12[/C][C]0.0246290331923163[/C][/ROW]
[ROW][C]-11[/C][C]0.0512901499991028[/C][/ROW]
[ROW][C]-10[/C][C]0.0180970595355700[/C][/ROW]
[ROW][C]-9[/C][C]0.0757533165591595[/C][/ROW]
[ROW][C]-8[/C][C]0.0664409257102138[/C][/ROW]
[ROW][C]-7[/C][C]0.0385113159232608[/C][/ROW]
[ROW][C]-6[/C][C]-0.00154695922903752[/C][/ROW]
[ROW][C]-5[/C][C]-0.00357254464226285[/C][/ROW]
[ROW][C]-4[/C][C]-0.0701563737715954[/C][/ROW]
[ROW][C]-3[/C][C]-0.0683988226333868[/C][/ROW]
[ROW][C]-2[/C][C]-0.0951054587223037[/C][/ROW]
[ROW][C]-1[/C][C]-0.218262567848526[/C][/ROW]
[ROW][C]0[/C][C]-0.114732011124124[/C][/ROW]
[ROW][C]1[/C][C]-0.116585767554604[/C][/ROW]
[ROW][C]2[/C][C]-0.0966337782694611[/C][/ROW]
[ROW][C]3[/C][C]-0.0552028184312031[/C][/ROW]
[ROW][C]4[/C][C]-0.0545599008219181[/C][/ROW]
[ROW][C]5[/C][C]-0.0543369381187143[/C][/ROW]
[ROW][C]6[/C][C]-0.0581488527265672[/C][/ROW]
[ROW][C]7[/C][C]-0.0227467220888765[/C][/ROW]
[ROW][C]8[/C][C]-0.0192595342542730[/C][/ROW]
[ROW][C]9[/C][C]-0.0196716505235826[/C][/ROW]
[ROW][C]10[/C][C]-0.0174835641222171[/C][/ROW]
[ROW][C]11[/C][C]-0.0256153579576687[/C][/ROW]
[ROW][C]12[/C][C]-0.0052197103218405[/C][/ROW]
[ROW][C]13[/C][C]-0.0122854415374478[/C][/ROW]
[ROW][C]14[/C][C]-0.0125345081645825[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28454&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28454&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)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])
-140.0241678426430553
-13-0.000214253645876006
-120.0246290331923163
-110.0512901499991028
-100.0180970595355700
-90.0757533165591595
-80.0664409257102138
-70.0385113159232608
-6-0.00154695922903752
-5-0.00357254464226285
-4-0.0701563737715954
-3-0.0683988226333868
-2-0.0951054587223037
-1-0.218262567848526
0-0.114732011124124
1-0.116585767554604
2-0.0966337782694611
3-0.0552028184312031
4-0.0545599008219181
5-0.0543369381187143
6-0.0581488527265672
7-0.0227467220888765
8-0.0192595342542730
9-0.0196716505235826
10-0.0174835641222171
11-0.0256153579576687
12-0.0052197103218405
13-0.0122854415374478
14-0.0125345081645825


Figures (Output of Computation)

Input Parameters & R Code

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