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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, 16 Dec 2008 07:45:53 -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/16/t1229438837se3n1tnd03mdmdc.htm/, Retrieved Wed, 15 May 2024 17:12:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=33971, Retrieved Wed, 15 May 2024 17:12:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact175

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Forecasting] [Taak 11 ARIMA For...] [2008-12-09 19:00:10] [819b576fab25b35cfda70f80599828ec]
- RMPD  [Cross Correlation Function] [Paper hoofdstuk 4...] [2008-12-10 21:28:46] [6fea0e9a9b3b29a63badf2c274e82506]
-           [Cross Correlation Function] [Paper 4.5 cross c...] [2008-12-16 14:45:53] [3bb0537fcae9c337e49b9ce75ff3d4da] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
493.000
481.000
462.000
457.000
442.000
439.000
488.000
521.000
501.000
485.000
464.000
460.000
467.000
460.000
448.000
443.000
436.000
431.000
484.000
510.000
513.000
503.000
471.000
471.000
476.000
475.000
470.000
461.000
455.000
456.000
517.000
525.000
523.000
519.000
509.000
512.000
519.000
517.000
510.000
509.000
501.000
507.000
569.000
580.000
578.000
565.000
547.000
555.000
562.000
561.000
555.000
544.000
537.000
543.000
594.000
611.000
613.000
611.000
594.000
595.000
Dataseries Y:
Download CSV
Histogram 
58.972
59.249
63.955
53.785
52.760
44.795
37.348
32.370
32.717
40.974
33.591
21.124
58.608
46.865
51.378
46.235
47.206
45.382
41.227
33.795
31.295
42.625
33.625
21.538
56.421
53.152
53.536
52.408
41.454
38.271
35.306
26.414
31.917
38.030
27.534
18.387
50.556
43.901
48.572
43.899
37.532
40.357
35.489
29.027
34.485
42.598
30.306
26.451
47.460
50.104
61.465
53.726
39.477
43.895
31.481
29.896
33.842
39.120
33.702
25.094

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 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 & 0 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33971&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]0 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=33971&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33971&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 time0 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






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)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-13-0.149791378174226
-120.0130089555585662
-110.165152386091078
-10-0.122535712829358
-90.0399030428588217
-8-0.0900660964715655
-70.0493270651574413
-60.186197551639008
-50.0475094454945297
-4-0.100003369110744
-3-0.0428789672656942
-20.116203607881155
-10.0485479072276536
00.0229710156563915
1-0.218842327848539
20.0624969108224136
30.203726826205408
4-0.0871145773603812
5-0.080825259945709
6-0.127777078546539
7-0.101261690291195
80.00331933574157733
90.183567426336966
10-0.143643300156420
110.00949946281313931
120.0966587347800366
13-0.154012285974972

\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) & 12 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & 1 \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
-13 & -0.149791378174226 \tabularnewline
-12 & 0.0130089555585662 \tabularnewline
-11 & 0.165152386091078 \tabularnewline
-10 & -0.122535712829358 \tabularnewline
-9 & 0.0399030428588217 \tabularnewline
-8 & -0.0900660964715655 \tabularnewline
-7 & 0.0493270651574413 \tabularnewline
-6 & 0.186197551639008 \tabularnewline
-5 & 0.0475094454945297 \tabularnewline
-4 & -0.100003369110744 \tabularnewline
-3 & -0.0428789672656942 \tabularnewline
-2 & 0.116203607881155 \tabularnewline
-1 & 0.0485479072276536 \tabularnewline
0 & 0.0229710156563915 \tabularnewline
1 & -0.218842327848539 \tabularnewline
2 & 0.0624969108224136 \tabularnewline
3 & 0.203726826205408 \tabularnewline
4 & -0.0871145773603812 \tabularnewline
5 & -0.080825259945709 \tabularnewline
6 & -0.127777078546539 \tabularnewline
7 & -0.101261690291195 \tabularnewline
8 & 0.00331933574157733 \tabularnewline
9 & 0.183567426336966 \tabularnewline
10 & -0.143643300156420 \tabularnewline
11 & 0.00949946281313931 \tabularnewline
12 & 0.0966587347800366 \tabularnewline
13 & -0.154012285974972 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33971&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]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]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]-13[/C][C]-0.149791378174226[/C][/ROW]
[ROW][C]-12[/C][C]0.0130089555585662[/C][/ROW]
[ROW][C]-11[/C][C]0.165152386091078[/C][/ROW]
[ROW][C]-10[/C][C]-0.122535712829358[/C][/ROW]
[ROW][C]-9[/C][C]0.0399030428588217[/C][/ROW]
[ROW][C]-8[/C][C]-0.0900660964715655[/C][/ROW]
[ROW][C]-7[/C][C]0.0493270651574413[/C][/ROW]
[ROW][C]-6[/C][C]0.186197551639008[/C][/ROW]
[ROW][C]-5[/C][C]0.0475094454945297[/C][/ROW]
[ROW][C]-4[/C][C]-0.100003369110744[/C][/ROW]
[ROW][C]-3[/C][C]-0.0428789672656942[/C][/ROW]
[ROW][C]-2[/C][C]0.116203607881155[/C][/ROW]
[ROW][C]-1[/C][C]0.0485479072276536[/C][/ROW]
[ROW][C]0[/C][C]0.0229710156563915[/C][/ROW]
[ROW][C]1[/C][C]-0.218842327848539[/C][/ROW]
[ROW][C]2[/C][C]0.0624969108224136[/C][/ROW]
[ROW][C]3[/C][C]0.203726826205408[/C][/ROW]
[ROW][C]4[/C][C]-0.0871145773603812[/C][/ROW]
[ROW][C]5[/C][C]-0.080825259945709[/C][/ROW]
[ROW][C]6[/C][C]-0.127777078546539[/C][/ROW]
[ROW][C]7[/C][C]-0.101261690291195[/C][/ROW]
[ROW][C]8[/C][C]0.00331933574157733[/C][/ROW]
[ROW][C]9[/C][C]0.183567426336966[/C][/ROW]
[ROW][C]10[/C][C]-0.143643300156420[/C][/ROW]
[ROW][C]11[/C][C]0.00949946281313931[/C][/ROW]
[ROW][C]12[/C][C]0.0966587347800366[/C][/ROW]
[ROW][C]13[/C][C]-0.154012285974972[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33971&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33971&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)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-13-0.149791378174226
-120.0130089555585662
-110.165152386091078
-10-0.122535712829358
-90.0399030428588217
-8-0.0900660964715655
-70.0493270651574413
-60.186197551639008
-50.0475094454945297
-4-0.100003369110744
-3-0.0428789672656942
-20.116203607881155
-10.0485479072276536
00.0229710156563915
1-0.218842327848539
20.0624969108224136
30.203726826205408
4-0.0871145773603812
5-0.080825259945709
6-0.127777078546539
7-0.101261690291195
80.00331933574157733
90.183567426336966
10-0.143643300156420
110.00949946281313931
120.0966587347800366
13-0.154012285974972


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1.0 ; par2 = 1 ; par3 = 1 ; par4 = 12 ; par5 = 1.0 ; par6 = 1 ; par7 = 1 ;
Parameters (R input):
par1 = 1.0 ; par2 = 1 ; par3 = 1 ; par4 = 12 ; par5 = 1.0 ; 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')