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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 computationSun, 19 Dec 2010 19:14:52 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/19/t1292785968t4ymk1xkq88b4pz.htm/, Retrieved Sun, 05 May 2024 05:49:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112680, Retrieved Sun, 05 May 2024 05:49:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact168

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
- RMPD  [Bivariate Explorative Data Analysis] [Ws4 part 1.1 s090...] [2009-10-27 21:56:53] [e0fc65a5811681d807296d590d5b45de]
-  M D    [Bivariate Explorative Data Analysis] [Paper; bivariate ...] [2009-12-19 19:10:37] [e0fc65a5811681d807296d590d5b45de]
- RMPD      [Cross Correlation Function] [cross correlation...] [2010-12-08 19:50:23] [74be16979710d4c4e7c6647856088456]
-   PD        [Cross Correlation Function] [] [2010-12-09 09:22:26] [b98453cac15ba1066b407e146608df68]
-    D            [Cross Correlation Function] [] [2010-12-19 19:14:52] [6b31f806e9ccc1f74a26091056f791cb] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
54.64
52.39
52.51
52.92
55.22
55.41
57.02
58.55
57.49
55.52
57.84
58.69
59.74
60.7
60.74
64.32
66.9
70.93
75.89
80.6
81.39
81.33
77.04
79.54
81.93
80.79
81.98
85.94
86.6
87.42
93.14
95.76
99.75
97.71
94.99
96.41
96.28
100.14
99.9
102.87
107.37
115.68
124.33
128.44
130.19
148.4
169.14
153.98
163.13
165.4
166.35
173.73
174.23
177.04
170.78
174.01
183.76
201.95
205.38
197.36
196.53
179.94
174.84
179.86
172.77
162.56
178.4
190.83
201.07
198.95
190.46
186.27
187.96
174.99
164.1
131.48
116.14
103.43
96.87
93.68
96.49
105.22
110.11
118.47
122.15
137.35
134.83
138.34
141.98
149.45
154.68
145.98
156.33
176.28
159.08
151.18
162.63
174.2
180.51
185.31
186.33
Dataseries Y:
Download CSV
Histogram 
14.36
14.62
13.51
14.95
16.72
16.33
15.21
16.69
15.81
16.02
16.7
15.99
17.68
18.89
18.72
21.14
20.97
23.75
23.05
23.45
21.74
19.37
21.1
21.2
22.67
22.24
23.78
23.27
25.74
26.1
27.49
31.41
28.79
26.76
26.41
27.05
29.43
32.1
36.84
34.22
36.53
40.99
45.97
43.6
47.84
51.47
51.31
48.47
48.28
46.56
43.83
51.17
49.59
49.11
49.97
50.07
53.3
57.08
68.54
71.62
67.64
64.79
80.97
88.42
110.22
99
95.95
107.94
97.82
111.64
114.73
117.58
99.19
90.19
59.74
44.51
23.94
21.29
20.77
25.07
32.95
40.05
44.59
40.28
41.19
38.14
41.85
43.76
50.16
52.94
47.69
51.52
58.69
50.44
45.72
43.24
51.49
50.43
58.73
65.12
64.13

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'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112680&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112680&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112680&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series0
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
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-160.0480547541613312
-150.0758597596099772
-140.157976298851292
-130.174887518468077
-120.116453762054480
-110.0870484294322357
-10-0.0539033914466972
-9-0.105338581425263
-8-0.145200356965112
-7-5.57780739309056e-05
-6-0.0220678715537212
-5-0.0873416851574331
-4-0.169823934314946
-30.0411574172321334
-20.0158608498494678
-10.309392949668878
00.389745181975033
10.554140027434825
20.289446743231196
30.193445415056710
40.247027528366593
50.0645675747941957
6-0.105466264805785
7-0.174326597980156
8-0.0895000333245358
9-0.152957688059764
10-0.0468278720772301
11-0.108069883705919
12-0.0529008421225239
13-0.179214177282809
14-0.0760210354241459
150.0285841944161094
16-0.0720180421831239

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 0 \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 \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
-16 & 0.0480547541613312 \tabularnewline
-15 & 0.0758597596099772 \tabularnewline
-14 & 0.157976298851292 \tabularnewline
-13 & 0.174887518468077 \tabularnewline
-12 & 0.116453762054480 \tabularnewline
-11 & 0.0870484294322357 \tabularnewline
-10 & -0.0539033914466972 \tabularnewline
-9 & -0.105338581425263 \tabularnewline
-8 & -0.145200356965112 \tabularnewline
-7 & -5.57780739309056e-05 \tabularnewline
-6 & -0.0220678715537212 \tabularnewline
-5 & -0.0873416851574331 \tabularnewline
-4 & -0.169823934314946 \tabularnewline
-3 & 0.0411574172321334 \tabularnewline
-2 & 0.0158608498494678 \tabularnewline
-1 & 0.309392949668878 \tabularnewline
0 & 0.389745181975033 \tabularnewline
1 & 0.554140027434825 \tabularnewline
2 & 0.289446743231196 \tabularnewline
3 & 0.193445415056710 \tabularnewline
4 & 0.247027528366593 \tabularnewline
5 & 0.0645675747941957 \tabularnewline
6 & -0.105466264805785 \tabularnewline
7 & -0.174326597980156 \tabularnewline
8 & -0.0895000333245358 \tabularnewline
9 & -0.152957688059764 \tabularnewline
10 & -0.0468278720772301 \tabularnewline
11 & -0.108069883705919 \tabularnewline
12 & -0.0529008421225239 \tabularnewline
13 & -0.179214177282809 \tabularnewline
14 & -0.0760210354241459 \tabularnewline
15 & 0.0285841944161094 \tabularnewline
16 & -0.0720180421831239 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112680&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[/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[/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]-16[/C][C]0.0480547541613312[/C][/ROW]
[ROW][C]-15[/C][C]0.0758597596099772[/C][/ROW]
[ROW][C]-14[/C][C]0.157976298851292[/C][/ROW]
[ROW][C]-13[/C][C]0.174887518468077[/C][/ROW]
[ROW][C]-12[/C][C]0.116453762054480[/C][/ROW]
[ROW][C]-11[/C][C]0.0870484294322357[/C][/ROW]
[ROW][C]-10[/C][C]-0.0539033914466972[/C][/ROW]
[ROW][C]-9[/C][C]-0.105338581425263[/C][/ROW]
[ROW][C]-8[/C][C]-0.145200356965112[/C][/ROW]
[ROW][C]-7[/C][C]-5.57780739309056e-05[/C][/ROW]
[ROW][C]-6[/C][C]-0.0220678715537212[/C][/ROW]
[ROW][C]-5[/C][C]-0.0873416851574331[/C][/ROW]
[ROW][C]-4[/C][C]-0.169823934314946[/C][/ROW]
[ROW][C]-3[/C][C]0.0411574172321334[/C][/ROW]
[ROW][C]-2[/C][C]0.0158608498494678[/C][/ROW]
[ROW][C]-1[/C][C]0.309392949668878[/C][/ROW]
[ROW][C]0[/C][C]0.389745181975033[/C][/ROW]
[ROW][C]1[/C][C]0.554140027434825[/C][/ROW]
[ROW][C]2[/C][C]0.289446743231196[/C][/ROW]
[ROW][C]3[/C][C]0.193445415056710[/C][/ROW]
[ROW][C]4[/C][C]0.247027528366593[/C][/ROW]
[ROW][C]5[/C][C]0.0645675747941957[/C][/ROW]
[ROW][C]6[/C][C]-0.105466264805785[/C][/ROW]
[ROW][C]7[/C][C]-0.174326597980156[/C][/ROW]
[ROW][C]8[/C][C]-0.0895000333245358[/C][/ROW]
[ROW][C]9[/C][C]-0.152957688059764[/C][/ROW]
[ROW][C]10[/C][C]-0.0468278720772301[/C][/ROW]
[ROW][C]11[/C][C]-0.108069883705919[/C][/ROW]
[ROW][C]12[/C][C]-0.0529008421225239[/C][/ROW]
[ROW][C]13[/C][C]-0.179214177282809[/C][/ROW]
[ROW][C]14[/C][C]-0.0760210354241459[/C][/ROW]
[ROW][C]15[/C][C]0.0285841944161094[/C][/ROW]
[ROW][C]16[/C][C]-0.0720180421831239[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112680&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112680&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
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
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-160.0480547541613312
-150.0758597596099772
-140.157976298851292
-130.174887518468077
-120.116453762054480
-110.0870484294322357
-10-0.0539033914466972
-9-0.105338581425263
-8-0.145200356965112
-7-5.57780739309056e-05
-6-0.0220678715537212
-5-0.0873416851574331
-4-0.169823934314946
-30.0411574172321334
-20.0158608498494678
-10.309392949668878
00.389745181975033
10.554140027434825
20.289446743231196
30.193445415056710
40.247027528366593
50.0645675747941957
6-0.105466264805785
7-0.174326597980156
8-0.0895000333245358
9-0.152957688059764
10-0.0468278720772301
11-0.108069883705919
12-0.0529008421225239
13-0.179214177282809
14-0.0760210354241459
150.0285841944161094
16-0.0720180421831239


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0.0 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = 0.0 ; par6 = 1 ; par7 = 0 ;
Parameters (R input):
par1 = 0.0 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = 0.0 ; 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')