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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 computationTue, 02 Dec 2008 10:44:19 -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/02/t1228239930uwnaxxhhyz6rhra.htm/, Retrieved Fri, 17 May 2024 05:15:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28139, Retrieved Fri, 17 May 2024 05:15:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact153

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 stat TS q7 ] [2008-12-02 17:44:19] [441cddd6b019c6452f1399cb0038dc92] [Current]
Feedback Forum
2008-12-08 19:47:56 [94a54c888ac7f7d6874c3108eb0e1808] [reply] 
De student geeft een correcte interpretatie van de gevonden gegevens.
2008-12-08 20:11:29 [Vincent Dolhain] [reply] 
correct
2008-12-10 08:13:04 [Peter Van Doninck] [reply] 
De student merkt een lange termijntrend op. Dit is echter niet mogelijk bij de cross correlatie functie! Het klopt wel dat je gaat zoeken naar een dynamische manier om het ene te voorspellen op basis van het andere. Er is min of meer sprake van een cross correlatie.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2659,81
2638,53
2720,25
2745,88
2735,7
2811,7
2799,43
2555,28
2304,98
2214,95
2065,81
1940,49
2042
1995,37
1946,81
1765,9
1635,25
1833,42
1910,43
1959,67
1969,6
2061,41
2093,48
2120,88
2174,56
2196,72
2350,44
2440,25
2408,64
2472,81
2407,6
2454,62
2448,05
2497,84
2645,64
2756,76
2849,27
2921,44
2981,85
3080,58
3106,22
3119,31
3061,26
3097,31
3161,69
3257,16
3277,01
3295,32
3363,99
3494,17
3667,03
3813,06
3917,96
3895,51
3801,06
3570,12
3701,61
3862,27
3970,1
4138,52
4199,75
4290,89
4443,91
4502,64
4356,98
4591,27
4696,96
4621,4
4562,84
4202,52
4296,49
4435,23
4105,18
4116,68
3844,49
3720,98
3674,4
3857,62
3801,06
3504,37
3032,6
3047,03
Dataseries Y:
Download CSV
Histogram 
19,2
19,32
19,82
20,36
24,31
25,97
25,61
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
70,3
75,26
71,44
76,36
81,71
92,6
90,6
92,23
94,09
102,79
109,65
124,05
132,69
135,81
116,07

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=28139&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=28139&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28139&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 series1
Degree of non-seasonal differencing (d) of X series0
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 series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-160.549206470409354
-150.594796603616192
-140.636591399225546
-130.672679717470304
-120.694085694426821
-110.713000510299515
-100.735536388304742
-90.751222903843875
-80.762999799876684
-70.766697888714917
-60.762281537810258
-50.753940499786999
-40.750841142531797
-30.749804822144368
-20.741359504634262
-10.715146672769229
00.683924487676175
10.650192995316911
20.622386609833261
30.603557914355628
40.589290788359811
50.579862323683221
60.570693873517028
70.558640707160126
80.541988394701671
90.524828011237057
100.507193519527956
110.485817747575669
120.461086939689879
130.437315422509666
140.411759766498420
150.383944194122091
160.355655488652462

\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 & 0 \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 & 0 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-16 & 0.549206470409354 \tabularnewline
-15 & 0.594796603616192 \tabularnewline
-14 & 0.636591399225546 \tabularnewline
-13 & 0.672679717470304 \tabularnewline
-12 & 0.694085694426821 \tabularnewline
-11 & 0.713000510299515 \tabularnewline
-10 & 0.735536388304742 \tabularnewline
-9 & 0.751222903843875 \tabularnewline
-8 & 0.762999799876684 \tabularnewline
-7 & 0.766697888714917 \tabularnewline
-6 & 0.762281537810258 \tabularnewline
-5 & 0.753940499786999 \tabularnewline
-4 & 0.750841142531797 \tabularnewline
-3 & 0.749804822144368 \tabularnewline
-2 & 0.741359504634262 \tabularnewline
-1 & 0.715146672769229 \tabularnewline
0 & 0.683924487676175 \tabularnewline
1 & 0.650192995316911 \tabularnewline
2 & 0.622386609833261 \tabularnewline
3 & 0.603557914355628 \tabularnewline
4 & 0.589290788359811 \tabularnewline
5 & 0.579862323683221 \tabularnewline
6 & 0.570693873517028 \tabularnewline
7 & 0.558640707160126 \tabularnewline
8 & 0.541988394701671 \tabularnewline
9 & 0.524828011237057 \tabularnewline
10 & 0.507193519527956 \tabularnewline
11 & 0.485817747575669 \tabularnewline
12 & 0.461086939689879 \tabularnewline
13 & 0.437315422509666 \tabularnewline
14 & 0.411759766498420 \tabularnewline
15 & 0.383944194122091 \tabularnewline
16 & 0.355655488652462 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28139&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]0[/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]0[/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]-16[/C][C]0.549206470409354[/C][/ROW]
[ROW][C]-15[/C][C]0.594796603616192[/C][/ROW]
[ROW][C]-14[/C][C]0.636591399225546[/C][/ROW]
[ROW][C]-13[/C][C]0.672679717470304[/C][/ROW]
[ROW][C]-12[/C][C]0.694085694426821[/C][/ROW]
[ROW][C]-11[/C][C]0.713000510299515[/C][/ROW]
[ROW][C]-10[/C][C]0.735536388304742[/C][/ROW]
[ROW][C]-9[/C][C]0.751222903843875[/C][/ROW]
[ROW][C]-8[/C][C]0.762999799876684[/C][/ROW]
[ROW][C]-7[/C][C]0.766697888714917[/C][/ROW]
[ROW][C]-6[/C][C]0.762281537810258[/C][/ROW]
[ROW][C]-5[/C][C]0.753940499786999[/C][/ROW]
[ROW][C]-4[/C][C]0.750841142531797[/C][/ROW]
[ROW][C]-3[/C][C]0.749804822144368[/C][/ROW]
[ROW][C]-2[/C][C]0.741359504634262[/C][/ROW]
[ROW][C]-1[/C][C]0.715146672769229[/C][/ROW]
[ROW][C]0[/C][C]0.683924487676175[/C][/ROW]
[ROW][C]1[/C][C]0.650192995316911[/C][/ROW]
[ROW][C]2[/C][C]0.622386609833261[/C][/ROW]
[ROW][C]3[/C][C]0.603557914355628[/C][/ROW]
[ROW][C]4[/C][C]0.589290788359811[/C][/ROW]
[ROW][C]5[/C][C]0.579862323683221[/C][/ROW]
[ROW][C]6[/C][C]0.570693873517028[/C][/ROW]
[ROW][C]7[/C][C]0.558640707160126[/C][/ROW]
[ROW][C]8[/C][C]0.541988394701671[/C][/ROW]
[ROW][C]9[/C][C]0.524828011237057[/C][/ROW]
[ROW][C]10[/C][C]0.507193519527956[/C][/ROW]
[ROW][C]11[/C][C]0.485817747575669[/C][/ROW]
[ROW][C]12[/C][C]0.461086939689879[/C][/ROW]
[ROW][C]13[/C][C]0.437315422509666[/C][/ROW]
[ROW][C]14[/C][C]0.411759766498420[/C][/ROW]
[ROW][C]15[/C][C]0.383944194122091[/C][/ROW]
[ROW][C]16[/C][C]0.355655488652462[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28139&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28139&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 series0
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 series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-160.549206470409354
-150.594796603616192
-140.636591399225546
-130.672679717470304
-120.694085694426821
-110.713000510299515
-100.735536388304742
-90.751222903843875
-80.762999799876684
-70.766697888714917
-60.762281537810258
-50.753940499786999
-40.750841142531797
-30.749804822144368
-20.741359504634262
-10.715146672769229
00.683924487676175
10.650192995316911
20.622386609833261
30.603557914355628
40.589290788359811
50.579862323683221
60.570693873517028
70.558640707160126
80.541988394701671
90.524828011237057
100.507193519527956
110.485817747575669
120.461086939689879
130.437315422509666
140.411759766498420
150.383944194122091
160.355655488652462


Figures (Output of Computation)

Input Parameters & R Code

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