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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationSat, 24 Nov 2007 14:21:09 -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/2007/Nov/24/t1195938755v1d42hrj6dmbuip.htm/, Retrieved Fri, 03 May 2024 12:22:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=6367, Retrieved Fri, 03 May 2024 12:22:38 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact196

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Cross Correlation Function] [WS9Q6Efs-e] [2007-11-24 21:21:09] [142ab5472309a9ae9a3b52678758dc4a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
22
27
24
24
22
23
25
23
21
21
22
20
22
22
20
21
20
21
21
21
19
21
21
22
19
24
22
22
22
24
22
23
24
21
20
22
23
23
22
20
21
21
20
20
17
18
19
19
20
21
20
21
19
22
20
18
16
17
18
19
Dataseries Y:
Download CSV
Histogram 
2529
2196
3202
2718
2728
2354
2697
2651
2067
2641
2539
2294
2712
2314
3092
2677
2813
2668
2939
2617
2231
2481
2421
2408
2560
2100
3315
2801
2403
3024
2507
2980
2211
2471
2594
2452
2232
2373
3127
2802
2641
2787
2619
2806
2193
2323
2529
2412
2262
2154
3230
2295
2715
2733
2317
2730
1913
2390
2484
1960

Tables (Output of Computation)





Summary of compuational 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 compuational 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=6367&T=0

[TABLE]
[ROW][C]Summary of compuational 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=6367&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=6367&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 compuational 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 series0.8
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series2
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-13-0.054996349566877
-120.243617850338314
-11-0.0578507518504605
-100.0753476540608864
-90.113914418973981
-8-0.101269791978006
-70.107705729492445
-6-0.0126504245832711
-5-0.115964006020115
-40.0974294552818348
-3-0.0145945487331462
-2-0.095055903496419
-10.168832930724156
0-0.246136262258888
10.0956730944435323
20.0683521765576589
30.105624621433688
4-0.0503393181327711
50.0827580322824232
6-0.142205843585823
70.110359304377003
8-0.0793980356815316
9-0.0191276545382509
10-0.0368274952777185
110.0514330436964557
120.0534169073631224
13-0.119464831569363

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 0.8 \tabularnewline
Degree of non-seasonal differencing (d) of X series & 1 \tabularnewline
Degree of seasonal differencing (D) of X series & 0 \tabularnewline
Seasonal Period (s) & 12 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & 2 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 0 \tabularnewline
Degree of seasonal differencing (D) of Y series & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-13 & -0.054996349566877 \tabularnewline
-12 & 0.243617850338314 \tabularnewline
-11 & -0.0578507518504605 \tabularnewline
-10 & 0.0753476540608864 \tabularnewline
-9 & 0.113914418973981 \tabularnewline
-8 & -0.101269791978006 \tabularnewline
-7 & 0.107705729492445 \tabularnewline
-6 & -0.0126504245832711 \tabularnewline
-5 & -0.115964006020115 \tabularnewline
-4 & 0.0974294552818348 \tabularnewline
-3 & -0.0145945487331462 \tabularnewline
-2 & -0.095055903496419 \tabularnewline
-1 & 0.168832930724156 \tabularnewline
0 & -0.246136262258888 \tabularnewline
1 & 0.0956730944435323 \tabularnewline
2 & 0.0683521765576589 \tabularnewline
3 & 0.105624621433688 \tabularnewline
4 & -0.0503393181327711 \tabularnewline
5 & 0.0827580322824232 \tabularnewline
6 & -0.142205843585823 \tabularnewline
7 & 0.110359304377003 \tabularnewline
8 & -0.0793980356815316 \tabularnewline
9 & -0.0191276545382509 \tabularnewline
10 & -0.0368274952777185 \tabularnewline
11 & 0.0514330436964557 \tabularnewline
12 & 0.0534169073631224 \tabularnewline
13 & -0.119464831569363 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=6367&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]0.8[/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]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]2[/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]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-13[/C][C]-0.054996349566877[/C][/ROW]
[ROW][C]-12[/C][C]0.243617850338314[/C][/ROW]
[ROW][C]-11[/C][C]-0.0578507518504605[/C][/ROW]
[ROW][C]-10[/C][C]0.0753476540608864[/C][/ROW]
[ROW][C]-9[/C][C]0.113914418973981[/C][/ROW]
[ROW][C]-8[/C][C]-0.101269791978006[/C][/ROW]
[ROW][C]-7[/C][C]0.107705729492445[/C][/ROW]
[ROW][C]-6[/C][C]-0.0126504245832711[/C][/ROW]
[ROW][C]-5[/C][C]-0.115964006020115[/C][/ROW]
[ROW][C]-4[/C][C]0.0974294552818348[/C][/ROW]
[ROW][C]-3[/C][C]-0.0145945487331462[/C][/ROW]
[ROW][C]-2[/C][C]-0.095055903496419[/C][/ROW]
[ROW][C]-1[/C][C]0.168832930724156[/C][/ROW]
[ROW][C]0[/C][C]-0.246136262258888[/C][/ROW]
[ROW][C]1[/C][C]0.0956730944435323[/C][/ROW]
[ROW][C]2[/C][C]0.0683521765576589[/C][/ROW]
[ROW][C]3[/C][C]0.105624621433688[/C][/ROW]
[ROW][C]4[/C][C]-0.0503393181327711[/C][/ROW]
[ROW][C]5[/C][C]0.0827580322824232[/C][/ROW]
[ROW][C]6[/C][C]-0.142205843585823[/C][/ROW]
[ROW][C]7[/C][C]0.110359304377003[/C][/ROW]
[ROW][C]8[/C][C]-0.0793980356815316[/C][/ROW]
[ROW][C]9[/C][C]-0.0191276545382509[/C][/ROW]
[ROW][C]10[/C][C]-0.0368274952777185[/C][/ROW]
[ROW][C]11[/C][C]0.0514330436964557[/C][/ROW]
[ROW][C]12[/C][C]0.0534169073631224[/C][/ROW]
[ROW][C]13[/C][C]-0.119464831569363[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=6367&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=6367&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 series0.8
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series2
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-13-0.054996349566877
-120.243617850338314
-11-0.0578507518504605
-100.0753476540608864
-90.113914418973981
-8-0.101269791978006
-70.107705729492445
-6-0.0126504245832711
-5-0.115964006020115
-40.0974294552818348
-3-0.0145945487331462
-2-0.095055903496419
-10.168832930724156
0-0.246136262258888
10.0956730944435323
20.0683521765576589
30.105624621433688
4-0.0503393181327711
50.0827580322824232
6-0.142205843585823
70.110359304377003
8-0.0793980356815316
9-0.0191276545382509
10-0.0368274952777185
110.0514330436964557
120.0534169073631224
13-0.119464831569363


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0.8 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = 2.0 ; par6 = 0 ; par7 = 1 ;
Parameters (R input):
par1 = 0.8 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = 2.0 ; par6 = 0 ; 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) x <- diff(y,lag=par4,difference=par7)
x
y
bitmap(file='test1.png')
(r <- ccf(x,y,main='Cross Correlation Function',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')