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 computationMon, 01 Dec 2008 14:08:35 -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/01/t1228165800og9nq2trzb32y7z.htm/, Retrieved Sun, 05 May 2024 17:38:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27409, Retrieved Sun, 05 May 2024 17:38:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact210

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] [Non Stationary Ti...] [2008-12-01 18:20:52] [d32f94eec6fe2d8c421bd223368a5ced]
F   PD    [Cross Correlation Function] [Non Stationary Ti...] [2008-12-01 21:08:35] [c33ddd06d9ea3933c8ac89c0e74c9b3a] [Current]
-           [Cross Correlation Function] [NSTS Q9] [2008-12-01 22:36:39] [d32f94eec6fe2d8c421bd223368a5ced]
Feedback Forum
2008-12-08 01:54:26 [Kenny Simons] [reply] 
Correct,

Door de aanpassing van de verschillende paramters (die je gevonden hebt in Q8)bekom je hier een andere oplossing als in Q7, dit is ook logisch, je hebt de reeks stationair gemaakt door de reeks te transformeren en te differentiëren.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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
1.2999
1.3074
1.3242
1.3516
1.3511
Dataseries Y:
Download CSV
Histogram 
24.67
25.59
26.09
28.37
27.34
24.46
27.46
30.23
32.33
29.87
24.87
25.48
27.28
28.24
29.58
26.95
29.08
28.76
29.59
30.7
30.52
32.67
33.19
37.13
35.54
37.75
41.84
42.94
49.14
44.61
40.22
44.23
45.85
53.38
53.26
51.8
55.3
57.81
63.96
63.77
59.15
56.12
57.42
63.52
61.71
63.01
68.18
72.03
69.75
74.41
74.33
64.24
60.03
59.44
62.5
55.04
58.34
61.92
67.65
67.68

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 2 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27409&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27409&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27409&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 time2 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series0.1
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)1
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-140.0496870765293243
-13-0.0478296578557951
-12-0.105667439286517
-11-0.0619418603423157
-100.00562096718707283
-90.0207190476164029
-80.068420864726732
-70.085900093610027
-6-0.155867994621452
-5-0.0574877691455741
-40.00166470573083295
-30.100438218504602
-2-0.206318251781826
-1-0.127182167051743
00.192742277839940
1-0.0607214092283147
2-0.101796423951538
3-0.20742865141187
40.207363921604333
50.0420240845318401
6-0.191317585085452
7-0.00140068725182414
8-0.116726433148147
9-0.097775989254563
100.0388942234574614
110.129116371454040
120.0782811788493345
13-0.148267422546677
14-0.0904222995484178

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 0.1 \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) & 1 \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 & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-14 & 0.0496870765293243 \tabularnewline
-13 & -0.0478296578557951 \tabularnewline
-12 & -0.105667439286517 \tabularnewline
-11 & -0.0619418603423157 \tabularnewline
-10 & 0.00562096718707283 \tabularnewline
-9 & 0.0207190476164029 \tabularnewline
-8 & 0.068420864726732 \tabularnewline
-7 & 0.085900093610027 \tabularnewline
-6 & -0.155867994621452 \tabularnewline
-5 & -0.0574877691455741 \tabularnewline
-4 & 0.00166470573083295 \tabularnewline
-3 & 0.100438218504602 \tabularnewline
-2 & -0.206318251781826 \tabularnewline
-1 & -0.127182167051743 \tabularnewline
0 & 0.192742277839940 \tabularnewline
1 & -0.0607214092283147 \tabularnewline
2 & -0.101796423951538 \tabularnewline
3 & -0.20742865141187 \tabularnewline
4 & 0.207363921604333 \tabularnewline
5 & 0.0420240845318401 \tabularnewline
6 & -0.191317585085452 \tabularnewline
7 & -0.00140068725182414 \tabularnewline
8 & -0.116726433148147 \tabularnewline
9 & -0.097775989254563 \tabularnewline
10 & 0.0388942234574614 \tabularnewline
11 & 0.129116371454040 \tabularnewline
12 & 0.0782811788493345 \tabularnewline
13 & -0.148267422546677 \tabularnewline
14 & -0.0904222995484178 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27409&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]0.1[/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]0[/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]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]-14[/C][C]0.0496870765293243[/C][/ROW]
[ROW][C]-13[/C][C]-0.0478296578557951[/C][/ROW]
[ROW][C]-12[/C][C]-0.105667439286517[/C][/ROW]
[ROW][C]-11[/C][C]-0.0619418603423157[/C][/ROW]
[ROW][C]-10[/C][C]0.00562096718707283[/C][/ROW]
[ROW][C]-9[/C][C]0.0207190476164029[/C][/ROW]
[ROW][C]-8[/C][C]0.068420864726732[/C][/ROW]
[ROW][C]-7[/C][C]0.085900093610027[/C][/ROW]
[ROW][C]-6[/C][C]-0.155867994621452[/C][/ROW]
[ROW][C]-5[/C][C]-0.0574877691455741[/C][/ROW]
[ROW][C]-4[/C][C]0.00166470573083295[/C][/ROW]
[ROW][C]-3[/C][C]0.100438218504602[/C][/ROW]
[ROW][C]-2[/C][C]-0.206318251781826[/C][/ROW]
[ROW][C]-1[/C][C]-0.127182167051743[/C][/ROW]
[ROW][C]0[/C][C]0.192742277839940[/C][/ROW]
[ROW][C]1[/C][C]-0.0607214092283147[/C][/ROW]
[ROW][C]2[/C][C]-0.101796423951538[/C][/ROW]
[ROW][C]3[/C][C]-0.20742865141187[/C][/ROW]
[ROW][C]4[/C][C]0.207363921604333[/C][/ROW]
[ROW][C]5[/C][C]0.0420240845318401[/C][/ROW]
[ROW][C]6[/C][C]-0.191317585085452[/C][/ROW]
[ROW][C]7[/C][C]-0.00140068725182414[/C][/ROW]
[ROW][C]8[/C][C]-0.116726433148147[/C][/ROW]
[ROW][C]9[/C][C]-0.097775989254563[/C][/ROW]
[ROW][C]10[/C][C]0.0388942234574614[/C][/ROW]
[ROW][C]11[/C][C]0.129116371454040[/C][/ROW]
[ROW][C]12[/C][C]0.0782811788493345[/C][/ROW]
[ROW][C]13[/C][C]-0.148267422546677[/C][/ROW]
[ROW][C]14[/C][C]-0.0904222995484178[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27409&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27409&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 series0.1
Degree of non-seasonal differencing (d) of X series1
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)1
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-140.0496870765293243
-13-0.0478296578557951
-12-0.105667439286517
-11-0.0619418603423157
-100.00562096718707283
-90.0207190476164029
-80.068420864726732
-70.085900093610027
-6-0.155867994621452
-5-0.0574877691455741
-40.00166470573083295
-30.100438218504602
-2-0.206318251781826
-1-0.127182167051743
00.192742277839940
1-0.0607214092283147
2-0.101796423951538
3-0.20742865141187
40.207363921604333
50.0420240845318401
6-0.191317585085452
7-0.00140068725182414
8-0.116726433148147
9-0.097775989254563
100.0388942234574614
110.129116371454040
120.0782811788493345
13-0.148267422546677
14-0.0904222995484178


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0.1 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 1 ; par6 = 1 ; par7 = 0 ;
Parameters (R input):
par1 = 0.1 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 1 ; 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')