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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, 02 Dec 2008 11:02:55 -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/t1228241038znzmcld628ygwjw.htm/, Retrieved Sun, 19 May 2024 11:12:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28166, Retrieved Sun, 19 May 2024 11:12:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact167

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
- RMPD  [Standard Deviation-Mean Plot] [Q5 Randow walk task] [2008-11-29 13:16:45] [6743688719638b0cb1c0a6e0bf433315]
F         [Standard Deviation-Mean Plot] [Q5 ] [2008-11-29 15:18:13] [de72ca3f4fcfd0997c84e1ac92aea119]
F    D      [Standard Deviation-Mean Plot] [Q8 Workshop 4] [2008-12-02 17:47:16] [de72ca3f4fcfd0997c84e1ac92aea119]
F RMPD          [Cross Correlation Function] [Q9 Workshop 4] [2008-12-02 18:02:55] [56fd94b954e08a6655cb7790b21ee404] [Current]
-   P             [Cross Correlation Function] [] [2008-12-06 16:38:31] [74be16979710d4c4e7c6647856088456]
-   P             [Cross Correlation Function] [verbeterd] [2008-12-09 18:02:40] [74be16979710d4c4e7c6647856088456]
Feedback Forum
2008-12-06 16:41:30 [Ken Wright] [reply] 
Doordat de student in de vorige vraag de mist is ingegaan, is deze vraag ook fout, men zal d gelijk moeten stellen aan 1 en niet aan 0, en dan krijgt men wel een andere cross correlation fuction.juiste link:http://www.freestatistics.org/blog/index.php?v=date/2008/Dec/06/t122858154663vbd3o8wx2woxq.htm
Hier kan men uit afleiden dat alle waarde (behalve lag 0) binnen het betrouwbaarheidsinterval liggen, men kan dus besluiten dat door de tijdreeksen te differentieren er geen significante corelatie meer bestaat tussen bijvoorbeeld het verleden van x en het heden van y
2008-12-08 17:47:32 [Birgit Van Dyck] [reply] 
De student heeft en Q8 een foute berekenign gemaakt. Door d gelijk te stellen aan 1 en D gelijk aan nul bekomt men een andere grafiek.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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
1.2999
1.3074
1.3242
1.3516
1.3511
1.3419
1.3716
1.3622
1.3896
1.4227
1.4684
Dataseries Y:
Download CSV
Histogram 
109.86
108.68
113.38
117.12
116.23
114.75
115.81
115.86
117.80
117.11
116.31
118.38
121.57
121.65
124.20
126.12
128.60
128.16
130.12
135.83
138.05
134.99
132.38
128.94
128.12
127.84
132.43
134.13
134.78
133.13
129.08
134.48
132.86
134.08
134.54
134.51
135.97
136.09
139.14
135.63
136.55
138.83
138.84
135.37
132.22
134.75
135.98
136.06
138.05
139.59
140.58
139.81
140.77
140.96
143.59
142.70
145.11
146.70
148.53
148.99
149.65
151.11
154.82
156.56
157.60
155.24
160.68
163.22
164.55
166.76
159.05
159.82
164.95
162.89

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28166&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]1 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=28166&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28166&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 time1 seconds
R Server'George Udny Yule' @ 72.249.76.132






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1.8
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series-0.2
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-150.230816884823243
-140.258696557688929
-130.289009487627106
-120.324611641760056
-110.368100855170887
-100.40804753225427
-90.446445028129782
-80.486971237846538
-70.535806145203507
-60.579351386564548
-50.627316812831213
-40.675432053236077
-30.720051512435076
-20.775208418292234
-10.846996973169132
00.920349272967166
10.878587284405231
20.824860218654439
30.784363000098571
40.747600427711032
50.693715070620254
60.633548891084927
70.570576014817241
80.50979771227918
90.46203312767286
100.412810767851683
110.358970761560734
120.30805744574753
130.257706352581478
140.209183131422069
150.165699886185262

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 1.8 \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) & 12 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & -0.2 \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
-15 & 0.230816884823243 \tabularnewline
-14 & 0.258696557688929 \tabularnewline
-13 & 0.289009487627106 \tabularnewline
-12 & 0.324611641760056 \tabularnewline
-11 & 0.368100855170887 \tabularnewline
-10 & 0.40804753225427 \tabularnewline
-9 & 0.446445028129782 \tabularnewline
-8 & 0.486971237846538 \tabularnewline
-7 & 0.535806145203507 \tabularnewline
-6 & 0.579351386564548 \tabularnewline
-5 & 0.627316812831213 \tabularnewline
-4 & 0.675432053236077 \tabularnewline
-3 & 0.720051512435076 \tabularnewline
-2 & 0.775208418292234 \tabularnewline
-1 & 0.846996973169132 \tabularnewline
0 & 0.920349272967166 \tabularnewline
1 & 0.878587284405231 \tabularnewline
2 & 0.824860218654439 \tabularnewline
3 & 0.784363000098571 \tabularnewline
4 & 0.747600427711032 \tabularnewline
5 & 0.693715070620254 \tabularnewline
6 & 0.633548891084927 \tabularnewline
7 & 0.570576014817241 \tabularnewline
8 & 0.50979771227918 \tabularnewline
9 & 0.46203312767286 \tabularnewline
10 & 0.412810767851683 \tabularnewline
11 & 0.358970761560734 \tabularnewline
12 & 0.30805744574753 \tabularnewline
13 & 0.257706352581478 \tabularnewline
14 & 0.209183131422069 \tabularnewline
15 & 0.165699886185262 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28166&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]1.8[/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]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]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]-15[/C][C]0.230816884823243[/C][/ROW]
[ROW][C]-14[/C][C]0.258696557688929[/C][/ROW]
[ROW][C]-13[/C][C]0.289009487627106[/C][/ROW]
[ROW][C]-12[/C][C]0.324611641760056[/C][/ROW]
[ROW][C]-11[/C][C]0.368100855170887[/C][/ROW]
[ROW][C]-10[/C][C]0.40804753225427[/C][/ROW]
[ROW][C]-9[/C][C]0.446445028129782[/C][/ROW]
[ROW][C]-8[/C][C]0.486971237846538[/C][/ROW]
[ROW][C]-7[/C][C]0.535806145203507[/C][/ROW]
[ROW][C]-6[/C][C]0.579351386564548[/C][/ROW]
[ROW][C]-5[/C][C]0.627316812831213[/C][/ROW]
[ROW][C]-4[/C][C]0.675432053236077[/C][/ROW]
[ROW][C]-3[/C][C]0.720051512435076[/C][/ROW]
[ROW][C]-2[/C][C]0.775208418292234[/C][/ROW]
[ROW][C]-1[/C][C]0.846996973169132[/C][/ROW]
[ROW][C]0[/C][C]0.920349272967166[/C][/ROW]
[ROW][C]1[/C][C]0.878587284405231[/C][/ROW]
[ROW][C]2[/C][C]0.824860218654439[/C][/ROW]
[ROW][C]3[/C][C]0.784363000098571[/C][/ROW]
[ROW][C]4[/C][C]0.747600427711032[/C][/ROW]
[ROW][C]5[/C][C]0.693715070620254[/C][/ROW]
[ROW][C]6[/C][C]0.633548891084927[/C][/ROW]
[ROW][C]7[/C][C]0.570576014817241[/C][/ROW]
[ROW][C]8[/C][C]0.50979771227918[/C][/ROW]
[ROW][C]9[/C][C]0.46203312767286[/C][/ROW]
[ROW][C]10[/C][C]0.412810767851683[/C][/ROW]
[ROW][C]11[/C][C]0.358970761560734[/C][/ROW]
[ROW][C]12[/C][C]0.30805744574753[/C][/ROW]
[ROW][C]13[/C][C]0.257706352581478[/C][/ROW]
[ROW][C]14[/C][C]0.209183131422069[/C][/ROW]
[ROW][C]15[/C][C]0.165699886185262[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28166&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28166&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 series1.8
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series-0.2
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-150.230816884823243
-140.258696557688929
-130.289009487627106
-120.324611641760056
-110.368100855170887
-100.40804753225427
-90.446445028129782
-80.486971237846538
-70.535806145203507
-60.579351386564548
-50.627316812831213
-40.675432053236077
-30.720051512435076
-20.775208418292234
-10.846996973169132
00.920349272967166
10.878587284405231
20.824860218654439
30.784363000098571
40.747600427711032
50.693715070620254
60.633548891084927
70.570576014817241
80.50979771227918
90.46203312767286
100.412810767851683
110.358970761560734
120.30805744574753
130.257706352581478
140.209183131422069
150.165699886185262


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1.8 ; par2 = 0 ; par3 = 0 ; par4 = 1 ; par5 = 1.8 ; par6 = 0 ; par7 = 0 ;
Parameters (R input):
par1 = 1.8 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; par5 = -0.2 ; 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')