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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 computationMon, 08 Dec 2008 08:34:52 -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/08/t1228750547sr8p5fggq72g8i4.htm/, Retrieved Thu, 16 May 2024 13:19:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=30544, Retrieved Thu, 16 May 2024 13:19:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact159

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...] [2008-12-01 18:25:17] [592f2eec6ad66b41308d6ae6d607599a]
-   P   [Cross Correlation Function] [cross correlation...] [2008-12-01 18:49:25] [592f2eec6ad66b41308d6ae6d607599a]
-   PD      [Cross Correlation Function] [Verbetering Q9] [2008-12-08 15:34:52] [2ae704d6b0222e84f58032588d68322b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0.9383
0.9217
0.9095
0.892
0.8742
0.8532
0.8607
0.9005
0.9111
0.9059
0.8883
0.8924
0.8833
0.87
0.8758
0.8858
0.917
0.9554
0.9922
0.9778
0.9808
0.9811
1.0014
1.0183
1.0622
1.0773
1.0807
1.0848
1.1582
1.1663
1.1372
1.1139
1.1222
1.1692
1.1702
1.2286
1.2613
1.2646
1.2262
1.1985
1.2007
1.2138
1.2266
1.2176
1.2218
1.249
1.2991
1.3408
1.3119
1.3014
1.3201
1.2938
1.2694
1.2165
1.2037
1.2292
1.2256
1.2015
1.1786
1.1856
1.2103
1.1938
1.202
1.2271
1.277
1.265
1.2684
1.2811
1.2727
1.2611
1.2881
1.3213
Dataseries Y:
Download CSV
Histogram 
90.8
96.4
90
92.1
97.2
95.1
88.5
91
90.5
75
66.3
66
68.4
70.6
83.9
90.1
90.6
87.1
90.8
94.1
99.8
96.8
87
96.3
107.1
115.2
106.1
89.5
91.3
97.6
100.7
104.6
94.7
101.8
102.5
105.3
110.3
109.8
117.3
118.8
131.3
125.9
133.1
147
145.8
164.4
149.8
137.7
151.7
156.8
180
180.4
170.4
191.6
199.5
218.2
217.5
205
194
199.3
219.3
211.1
215.2
240.2
242.2
240.7
255.4
253
218.2
203.7
205.6
215.6

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'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 & 0 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30544&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30544&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30544&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'George Udny Yule' @ 72.249.76.132






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1.6
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 series0.2
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-150.0947145806899992
-140.00925623749213676
-130.0130971654227199
-12-0.05729343286031
-11-0.189835926849945
-10-0.101759858607923
-90.0007542749892066
-80.2203033394042
-70.219580656155558
-6-0.0124591899461505
-5-0.0221397624621432
-4-0.0552270613100544
-30.0872353123361771
-2-0.190959320128734
-1-0.0134718090495893
00.0757409971706031
1-0.0603849189350272
2-0.0185831570672707
3-0.0781119084040206
40.21899056675645
50.0306688052609139
6-0.161611852931806
7-0.012670104084897
8-0.162928366405087
9-0.0862951159593987
100.122296616389269
110.109166449131413
120.079892342939457
13-0.108833889223028
14-0.092633320730723
15-0.173022875639938

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 1.6 \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 & 0.2 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 1 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-15 & 0.0947145806899992 \tabularnewline
-14 & 0.00925623749213676 \tabularnewline
-13 & 0.0130971654227199 \tabularnewline
-12 & -0.05729343286031 \tabularnewline
-11 & -0.189835926849945 \tabularnewline
-10 & -0.101759858607923 \tabularnewline
-9 & 0.0007542749892066 \tabularnewline
-8 & 0.2203033394042 \tabularnewline
-7 & 0.219580656155558 \tabularnewline
-6 & -0.0124591899461505 \tabularnewline
-5 & -0.0221397624621432 \tabularnewline
-4 & -0.0552270613100544 \tabularnewline
-3 & 0.0872353123361771 \tabularnewline
-2 & -0.190959320128734 \tabularnewline
-1 & -0.0134718090495893 \tabularnewline
0 & 0.0757409971706031 \tabularnewline
1 & -0.0603849189350272 \tabularnewline
2 & -0.0185831570672707 \tabularnewline
3 & -0.0781119084040206 \tabularnewline
4 & 0.21899056675645 \tabularnewline
5 & 0.0306688052609139 \tabularnewline
6 & -0.161611852931806 \tabularnewline
7 & -0.012670104084897 \tabularnewline
8 & -0.162928366405087 \tabularnewline
9 & -0.0862951159593987 \tabularnewline
10 & 0.122296616389269 \tabularnewline
11 & 0.109166449131413 \tabularnewline
12 & 0.079892342939457 \tabularnewline
13 & -0.108833889223028 \tabularnewline
14 & -0.092633320730723 \tabularnewline
15 & -0.173022875639938 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30544&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]1.6[/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]0.2[/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]0[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-15[/C][C]0.0947145806899992[/C][/ROW]
[ROW][C]-14[/C][C]0.00925623749213676[/C][/ROW]
[ROW][C]-13[/C][C]0.0130971654227199[/C][/ROW]
[ROW][C]-12[/C][C]-0.05729343286031[/C][/ROW]
[ROW][C]-11[/C][C]-0.189835926849945[/C][/ROW]
[ROW][C]-10[/C][C]-0.101759858607923[/C][/ROW]
[ROW][C]-9[/C][C]0.0007542749892066[/C][/ROW]
[ROW][C]-8[/C][C]0.2203033394042[/C][/ROW]
[ROW][C]-7[/C][C]0.219580656155558[/C][/ROW]
[ROW][C]-6[/C][C]-0.0124591899461505[/C][/ROW]
[ROW][C]-5[/C][C]-0.0221397624621432[/C][/ROW]
[ROW][C]-4[/C][C]-0.0552270613100544[/C][/ROW]
[ROW][C]-3[/C][C]0.0872353123361771[/C][/ROW]
[ROW][C]-2[/C][C]-0.190959320128734[/C][/ROW]
[ROW][C]-1[/C][C]-0.0134718090495893[/C][/ROW]
[ROW][C]0[/C][C]0.0757409971706031[/C][/ROW]
[ROW][C]1[/C][C]-0.0603849189350272[/C][/ROW]
[ROW][C]2[/C][C]-0.0185831570672707[/C][/ROW]
[ROW][C]3[/C][C]-0.0781119084040206[/C][/ROW]
[ROW][C]4[/C][C]0.21899056675645[/C][/ROW]
[ROW][C]5[/C][C]0.0306688052609139[/C][/ROW]
[ROW][C]6[/C][C]-0.161611852931806[/C][/ROW]
[ROW][C]7[/C][C]-0.012670104084897[/C][/ROW]
[ROW][C]8[/C][C]-0.162928366405087[/C][/ROW]
[ROW][C]9[/C][C]-0.0862951159593987[/C][/ROW]
[ROW][C]10[/C][C]0.122296616389269[/C][/ROW]
[ROW][C]11[/C][C]0.109166449131413[/C][/ROW]
[ROW][C]12[/C][C]0.079892342939457[/C][/ROW]
[ROW][C]13[/C][C]-0.108833889223028[/C][/ROW]
[ROW][C]14[/C][C]-0.092633320730723[/C][/ROW]
[ROW][C]15[/C][C]-0.173022875639938[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30544&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30544&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 series1.6
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 series0.2
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-150.0947145806899992
-140.00925623749213676
-130.0130971654227199
-12-0.05729343286031
-11-0.189835926849945
-10-0.101759858607923
-90.0007542749892066
-80.2203033394042
-70.219580656155558
-6-0.0124591899461505
-5-0.0221397624621432
-4-0.0552270613100544
-30.0872353123361771
-2-0.190959320128734
-1-0.0134718090495893
00.0757409971706031
1-0.0603849189350272
2-0.0185831570672707
3-0.0781119084040206
40.21899056675645
50.0306688052609139
6-0.161611852931806
7-0.012670104084897
8-0.162928366405087
9-0.0862951159593987
100.122296616389269
110.109166449131413
120.079892342939457
13-0.108833889223028
14-0.092633320730723
15-0.173022875639938


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = -0.3 ; par2 = 1 ; par3 = 1 ; par4 = 12 ;
Parameters (R input):
par1 = 1.6 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = 0.2 ; par6 = 1 ; 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')