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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, 12 Dec 2010 14:24:39 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/12/t1292163754j9bz02j5bmaxsyx.htm/, Retrieved Tue, 07 May 2024 17:35:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108473, Retrieved Tue, 07 May 2024 17:35:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact156

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
- RMPD  [Bivariate Explorative Data Analysis] [Ws4 part 1.1 s090...] [2009-10-27 21:56:53] [e0fc65a5811681d807296d590d5b45de]
-  M D    [Bivariate Explorative Data Analysis] [Paper; bivariate ...] [2009-12-19 19:10:37] [e0fc65a5811681d807296d590d5b45de]
- RMPD      [Cross Correlation Function] [cross correlation...] [2010-12-08 19:50:23] [74be16979710d4c4e7c6647856088456]
-   PD        [Cross Correlation Function] [] [2010-12-09 09:22:26] [b98453cac15ba1066b407e146608df68]
-   PD            [Cross Correlation Function] [workshop 10] [2010-12-12 14:24:39] [c1f1b5e209adb4577289f490325e36f2] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1.3031
1.3241
1.2961
1.2865
1.2305
1.2101
1.2125
1.2350
1.2014
1.1992
1.1791
1.1832
1.2159
1.1922
1.2114
1.2614
1.2812
1.2786
1.2772
1.2815
1.2679
1.2765
1.3247
1.3191
1.3029
1.3234
1.3354
1.3651
1.3453
1.3534
1.3706
1.3638
1.4268
1.4485
1.4635
1.4587
1.4876
1.5189
1.5783
1.5633
1.5554
1.5757
1.5593
1.4660
1.4065
1.2759
1.2705
1.3954
1.2793
1.2694
1.3282
1.3230
1.4135
1.4042
1.4253
1.4322
1.4632
1.4713
1.5016
1.4318
Dataseries Y:
Download CSV
Histogram 
0.6923
0.6886
0.6855
0.6745
0.6769
0.6758
0.6896
0.6843
0.6818
0.6774
0.6821
0.6885
0.6829
0.6796
0.6976
0.6924
0.6849
0.6921
0.6839
0.6727
0.6776
0.6692
0.6738
0.6740
0.6635
0.6737
0.6788
0.6828
0.6795
0.6740
0.6744
0.6764
0.6987
0.6967
0.7116
0.7357
0.7455
0.7639
0.7958
0.7864
0.7853
0.7903
0.7866
0.8039
0.7916
0.7903
0.8242
0.9567
0.8850
0.8865
0.9258
0.8948
0.8762
0.8527
0.8536
0.8805
0.9155
0.8961
0.9127
0.8857

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'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108473&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108473&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108473&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'Gwilym Jenkins' @ 72.249.127.135






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series0
Degree of non-seasonal differencing (d) of X series2
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series0
Degree of non-seasonal differencing (d) of Y series2
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-13-0.110889568161045
-12-0.243668240318847
-110.257570081785849
-10-0.176589384814295
-90.236750823858184
-8-0.189283458948711
-7-0.0774862173173764
-60.0739247417003751
-50.0681910372114541
-4-0.0894848214402331
-30.107756595254641
-2-0.192757698811668
-1-0.197117662794973
00.713067981121252
1-0.15472545608258
20.081755644883193
3-0.128511390754384
4-0.091619479554054
50.179886015904060
6-0.0525957463015481
7-0.0852508892512073
80.0151613241814401
9-0.0133241844128856
10-0.0374018972208789
110.257331066030994
12-0.112532715977719
13-0.120299258920069

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 0 \tabularnewline
Degree of non-seasonal differencing (d) of X series & 2 \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 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 2 \tabularnewline
Degree of seasonal differencing (D) of Y series & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-13 & -0.110889568161045 \tabularnewline
-12 & -0.243668240318847 \tabularnewline
-11 & 0.257570081785849 \tabularnewline
-10 & -0.176589384814295 \tabularnewline
-9 & 0.236750823858184 \tabularnewline
-8 & -0.189283458948711 \tabularnewline
-7 & -0.0774862173173764 \tabularnewline
-6 & 0.0739247417003751 \tabularnewline
-5 & 0.0681910372114541 \tabularnewline
-4 & -0.0894848214402331 \tabularnewline
-3 & 0.107756595254641 \tabularnewline
-2 & -0.192757698811668 \tabularnewline
-1 & -0.197117662794973 \tabularnewline
0 & 0.713067981121252 \tabularnewline
1 & -0.15472545608258 \tabularnewline
2 & 0.081755644883193 \tabularnewline
3 & -0.128511390754384 \tabularnewline
4 & -0.091619479554054 \tabularnewline
5 & 0.179886015904060 \tabularnewline
6 & -0.0525957463015481 \tabularnewline
7 & -0.0852508892512073 \tabularnewline
8 & 0.0151613241814401 \tabularnewline
9 & -0.0133241844128856 \tabularnewline
10 & -0.0374018972208789 \tabularnewline
11 & 0.257331066030994 \tabularnewline
12 & -0.112532715977719 \tabularnewline
13 & -0.120299258920069 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108473&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[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of X series[/C][C]2[/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[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of Y series[/C][C]2[/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]-13[/C][C]-0.110889568161045[/C][/ROW]
[ROW][C]-12[/C][C]-0.243668240318847[/C][/ROW]
[ROW][C]-11[/C][C]0.257570081785849[/C][/ROW]
[ROW][C]-10[/C][C]-0.176589384814295[/C][/ROW]
[ROW][C]-9[/C][C]0.236750823858184[/C][/ROW]
[ROW][C]-8[/C][C]-0.189283458948711[/C][/ROW]
[ROW][C]-7[/C][C]-0.0774862173173764[/C][/ROW]
[ROW][C]-6[/C][C]0.0739247417003751[/C][/ROW]
[ROW][C]-5[/C][C]0.0681910372114541[/C][/ROW]
[ROW][C]-4[/C][C]-0.0894848214402331[/C][/ROW]
[ROW][C]-3[/C][C]0.107756595254641[/C][/ROW]
[ROW][C]-2[/C][C]-0.192757698811668[/C][/ROW]
[ROW][C]-1[/C][C]-0.197117662794973[/C][/ROW]
[ROW][C]0[/C][C]0.713067981121252[/C][/ROW]
[ROW][C]1[/C][C]-0.15472545608258[/C][/ROW]
[ROW][C]2[/C][C]0.081755644883193[/C][/ROW]
[ROW][C]3[/C][C]-0.128511390754384[/C][/ROW]
[ROW][C]4[/C][C]-0.091619479554054[/C][/ROW]
[ROW][C]5[/C][C]0.179886015904060[/C][/ROW]
[ROW][C]6[/C][C]-0.0525957463015481[/C][/ROW]
[ROW][C]7[/C][C]-0.0852508892512073[/C][/ROW]
[ROW][C]8[/C][C]0.0151613241814401[/C][/ROW]
[ROW][C]9[/C][C]-0.0133241844128856[/C][/ROW]
[ROW][C]10[/C][C]-0.0374018972208789[/C][/ROW]
[ROW][C]11[/C][C]0.257331066030994[/C][/ROW]
[ROW][C]12[/C][C]-0.112532715977719[/C][/ROW]
[ROW][C]13[/C][C]-0.120299258920069[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108473&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108473&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 series0
Degree of non-seasonal differencing (d) of X series2
Degree of seasonal differencing (D) of X series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series0
Degree of non-seasonal differencing (d) of Y series2
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-13-0.110889568161045
-12-0.243668240318847
-110.257570081785849
-10-0.176589384814295
-90.236750823858184
-8-0.189283458948711
-7-0.0774862173173764
-60.0739247417003751
-50.0681910372114541
-4-0.0894848214402331
-30.107756595254641
-2-0.192757698811668
-1-0.197117662794973
00.713067981121252
1-0.15472545608258
20.081755644883193
3-0.128511390754384
4-0.091619479554054
50.179886015904060
6-0.0525957463015481
7-0.0852508892512073
80.0151613241814401
9-0.0133241844128856
10-0.0374018972208789
110.257331066030994
12-0.112532715977719
13-0.120299258920069


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0.0 ; par2 = 2 ; par3 = 1 ; par4 = 12 ; par5 = 0.0 ; par6 = 2 ; par7 = 1 ;
Parameters (R input):
par1 = 0.0 ; par2 = 2 ; par3 = 1 ; par4 = 12 ; par5 = 0.0 ; par6 = 2 ; 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')