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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 computationFri, 24 Dec 2010 14:40:05 +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/24/t12932020426zgznsbb9ex898z.htm/, Retrieved Tue, 30 Apr 2024 03:31:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=115032, Retrieved Tue, 30 Apr 2024 03:31:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact176

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] [] [2010-12-24 14:40:05] [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=115032&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=115032&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115032&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 series1
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 series-0.7
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-160.0366920769371048
-150.0987698782057464
-140.149836048758131
-130.162454543833981
-120.138079983494140
-110.0666233169887424
-10-0.107467661917511
-9-0.151901425192678
-8-0.155482141173839
-7-0.0365591131319498
-6-0.0321262079944661
-5-0.0940458831155122
-4-0.177069773224795
-30.0663067694210089
-20.0581072000995315
-10.302888650093075
00.323633520408322
10.426427532947196
20.188297715167111
30.0873381216592128
40.178915020157723
50.0553765287161688
6-0.0458871893516758
7-0.11985703755125
8-0.0300034605674534
9-0.0845963944738653
10-0.00631220693089579
11-0.0419393137723757
12-0.0244304731580453
13-0.0978321447579863
14-0.046949172092894
150.0685775777256058
16-0.0725755174505503

\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 & 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 & -0.7 \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 & 0.0366920769371048 \tabularnewline
-15 & 0.0987698782057464 \tabularnewline
-14 & 0.149836048758131 \tabularnewline
-13 & 0.162454543833981 \tabularnewline
-12 & 0.138079983494140 \tabularnewline
-11 & 0.0666233169887424 \tabularnewline
-10 & -0.107467661917511 \tabularnewline
-9 & -0.151901425192678 \tabularnewline
-8 & -0.155482141173839 \tabularnewline
-7 & -0.0365591131319498 \tabularnewline
-6 & -0.0321262079944661 \tabularnewline
-5 & -0.0940458831155122 \tabularnewline
-4 & -0.177069773224795 \tabularnewline
-3 & 0.0663067694210089 \tabularnewline
-2 & 0.0581072000995315 \tabularnewline
-1 & 0.302888650093075 \tabularnewline
0 & 0.323633520408322 \tabularnewline
1 & 0.426427532947196 \tabularnewline
2 & 0.188297715167111 \tabularnewline
3 & 0.0873381216592128 \tabularnewline
4 & 0.178915020157723 \tabularnewline
5 & 0.0553765287161688 \tabularnewline
6 & -0.0458871893516758 \tabularnewline
7 & -0.11985703755125 \tabularnewline
8 & -0.0300034605674534 \tabularnewline
9 & -0.0845963944738653 \tabularnewline
10 & -0.00631220693089579 \tabularnewline
11 & -0.0419393137723757 \tabularnewline
12 & -0.0244304731580453 \tabularnewline
13 & -0.0978321447579863 \tabularnewline
14 & -0.046949172092894 \tabularnewline
15 & 0.0685775777256058 \tabularnewline
16 & -0.0725755174505503 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115032&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]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]-0.7[/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]0.0366920769371048[/C][/ROW]
[ROW][C]-15[/C][C]0.0987698782057464[/C][/ROW]
[ROW][C]-14[/C][C]0.149836048758131[/C][/ROW]
[ROW][C]-13[/C][C]0.162454543833981[/C][/ROW]
[ROW][C]-12[/C][C]0.138079983494140[/C][/ROW]
[ROW][C]-11[/C][C]0.0666233169887424[/C][/ROW]
[ROW][C]-10[/C][C]-0.107467661917511[/C][/ROW]
[ROW][C]-9[/C][C]-0.151901425192678[/C][/ROW]
[ROW][C]-8[/C][C]-0.155482141173839[/C][/ROW]
[ROW][C]-7[/C][C]-0.0365591131319498[/C][/ROW]
[ROW][C]-6[/C][C]-0.0321262079944661[/C][/ROW]
[ROW][C]-5[/C][C]-0.0940458831155122[/C][/ROW]
[ROW][C]-4[/C][C]-0.177069773224795[/C][/ROW]
[ROW][C]-3[/C][C]0.0663067694210089[/C][/ROW]
[ROW][C]-2[/C][C]0.0581072000995315[/C][/ROW]
[ROW][C]-1[/C][C]0.302888650093075[/C][/ROW]
[ROW][C]0[/C][C]0.323633520408322[/C][/ROW]
[ROW][C]1[/C][C]0.426427532947196[/C][/ROW]
[ROW][C]2[/C][C]0.188297715167111[/C][/ROW]
[ROW][C]3[/C][C]0.0873381216592128[/C][/ROW]
[ROW][C]4[/C][C]0.178915020157723[/C][/ROW]
[ROW][C]5[/C][C]0.0553765287161688[/C][/ROW]
[ROW][C]6[/C][C]-0.0458871893516758[/C][/ROW]
[ROW][C]7[/C][C]-0.11985703755125[/C][/ROW]
[ROW][C]8[/C][C]-0.0300034605674534[/C][/ROW]
[ROW][C]9[/C][C]-0.0845963944738653[/C][/ROW]
[ROW][C]10[/C][C]-0.00631220693089579[/C][/ROW]
[ROW][C]11[/C][C]-0.0419393137723757[/C][/ROW]
[ROW][C]12[/C][C]-0.0244304731580453[/C][/ROW]
[ROW][C]13[/C][C]-0.0978321447579863[/C][/ROW]
[ROW][C]14[/C][C]-0.046949172092894[/C][/ROW]
[ROW][C]15[/C][C]0.0685775777256058[/C][/ROW]
[ROW][C]16[/C][C]-0.0725755174505503[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115032&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115032&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 series1
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series-0.7
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-160.0366920769371048
-150.0987698782057464
-140.149836048758131
-130.162454543833981
-120.138079983494140
-110.0666233169887424
-10-0.107467661917511
-9-0.151901425192678
-8-0.155482141173839
-7-0.0365591131319498
-6-0.0321262079944661
-5-0.0940458831155122
-4-0.177069773224795
-30.0663067694210089
-20.0581072000995315
-10.302888650093075
00.323633520408322
10.426427532947196
20.188297715167111
30.0873381216592128
40.178915020157723
50.0553765287161688
6-0.0458871893516758
7-0.11985703755125
8-0.0300034605674534
9-0.0845963944738653
10-0.00631220693089579
11-0.0419393137723757
12-0.0244304731580453
13-0.0978321447579863
14-0.046949172092894
150.0685775777256058
16-0.0725755174505503


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = -0.7 ; 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')