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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 computationThu, 29 Nov 2007 10:29:21 -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/2007/Nov/29/t1196356723wvaa8m6z1cpetzc.htm/, Retrieved Fri, 03 May 2024 12:15:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=7553, Retrieved Fri, 03 May 2024 12:15:19 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact216

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Cross Correlation Function] [opdracht 3 - Q5] [2007-11-29 17:29:21] [20b3c1d23568d98c39f9358fef47a65d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
102,3
108,8
105,9
113,2
95,7
80,9
113,9
98,1
102,8
104,7
95,9
94,6
101,6
103,9
110,3
114,1
96,8
87,4
111,4
97,4
102,9
112,7
97
95,1
96,9
98,6
111,7
109,8
89,9
87,4
104,5
98,1
102,7
105,4
97
97,4
92
101,7
112,6
106,9
92,1
86
104,7
102
103,1
106
96,1
96,2
90,7
102,3
109,4
101
94,7
81
106,2
101,9
96,4
110,4
100,5
98,8
106,9
92,1
86
104,7
102
103,1
106
96,1
96,2
90,7
102,3
109,4
101
94,7
81
106,2
101,9
96,4
110,4
100,5
98,8
Dataseries Y:
Download CSV
Histogram 
139,9
135,6
126,8
134,4
113,5
107,5
133,8
119,0
125,9
130,1
114,2
111,6
131,2
124,1
127,1
123,4
100,7
100,3
121,6
110,5
110,3
122,7
102,6
101,8
113,6
107,2
116,8
112,5
89,0
95,2
110,6
102,6
106,4
112,7
103,2
104,2
111,3
115,7
111,6
112,2
92,2
97,1
108,1
107,2
107,2
110,3
97,6
100,6
102,8
105,3
109,5
105,7
93,7
91,7
111,6
109,2
101,2
114,3
100,9
106,0
109,0
110,4
110,7
105,7
89,6
83,1
103,5
104,8
93,5
106,7
93,7
84,7
99,2
91,9
94,9
94,1
80,8
72,6
94,0
80,0
85,4
91,5
76,1
76,1
89,5
85,3
91,0
93,0
73,4
76,6
95,0
84,5
88,7
93,1
83,4
82,2
95,6
83,7
94,3
93,0
71,8
75,3
91,8
80,7
84,8
83,1
78,4
78,7
84,7
80,5
91,8
83,5
66,6
68,6
83,6
79,7
79,9
81,2
74,3
73,0
78,8
81,6
85,0
91,1
68,2
59,8
83,1
76,7
73,2
83,9
73,0
70,9

Tables (Output of Computation)





Summary of compuational 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 compuational 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=7553&T=0

[TABLE]
[ROW][C]Summary of compuational 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=7553&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=7553&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 compuational 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 series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-17-0.102613066588082
-160.154957923479744
-15-0.0291690912772091
-14-0.0626466600703377
-130.00589310759793503
-120.148775503457454
-11-0.181087380912304
-100.174151894647307
-9-0.0472880511674319
-8-0.117736887679213
-70.169921621375792
-6-0.0851725018878482
-5-0.0973660130565397
-40.22212209731198
-3-0.0987814621230894
-2-0.109580714700707
-10.232325598869151
0-0.332941705590635
10.162850172010864
20.0449915161201081
3-0.0924278515528298
40.0296508653152467
50.0615073508418464
6-0.127478017764473
70.0411322911233523
80.0845079848218404
9-0.117329934774557
100.0589173006452322
110.0726235609071012
12-0.186869605950445
130.155049406738064
140.0118821230821056
15-0.147553867663309
160.14977680833976
17-0.0159451174170074

\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 & 1 \tabularnewline
Seasonal Period (s) & 12 \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 & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-17 & -0.102613066588082 \tabularnewline
-16 & 0.154957923479744 \tabularnewline
-15 & -0.0291690912772091 \tabularnewline
-14 & -0.0626466600703377 \tabularnewline
-13 & 0.00589310759793503 \tabularnewline
-12 & 0.148775503457454 \tabularnewline
-11 & -0.181087380912304 \tabularnewline
-10 & 0.174151894647307 \tabularnewline
-9 & -0.0472880511674319 \tabularnewline
-8 & -0.117736887679213 \tabularnewline
-7 & 0.169921621375792 \tabularnewline
-6 & -0.0851725018878482 \tabularnewline
-5 & -0.0973660130565397 \tabularnewline
-4 & 0.22212209731198 \tabularnewline
-3 & -0.0987814621230894 \tabularnewline
-2 & -0.109580714700707 \tabularnewline
-1 & 0.232325598869151 \tabularnewline
0 & -0.332941705590635 \tabularnewline
1 & 0.162850172010864 \tabularnewline
2 & 0.0449915161201081 \tabularnewline
3 & -0.0924278515528298 \tabularnewline
4 & 0.0296508653152467 \tabularnewline
5 & 0.0615073508418464 \tabularnewline
6 & -0.127478017764473 \tabularnewline
7 & 0.0411322911233523 \tabularnewline
8 & 0.0845079848218404 \tabularnewline
9 & -0.117329934774557 \tabularnewline
10 & 0.0589173006452322 \tabularnewline
11 & 0.0726235609071012 \tabularnewline
12 & -0.186869605950445 \tabularnewline
13 & 0.155049406738064 \tabularnewline
14 & 0.0118821230821056 \tabularnewline
15 & -0.147553867663309 \tabularnewline
16 & 0.14977680833976 \tabularnewline
17 & -0.0159451174170074 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=7553&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]1[/C][/ROW]
[ROW][C]Seasonal Period (s)[/C][C]12[/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]1[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-17[/C][C]-0.102613066588082[/C][/ROW]
[ROW][C]-16[/C][C]0.154957923479744[/C][/ROW]
[ROW][C]-15[/C][C]-0.0291690912772091[/C][/ROW]
[ROW][C]-14[/C][C]-0.0626466600703377[/C][/ROW]
[ROW][C]-13[/C][C]0.00589310759793503[/C][/ROW]
[ROW][C]-12[/C][C]0.148775503457454[/C][/ROW]
[ROW][C]-11[/C][C]-0.181087380912304[/C][/ROW]
[ROW][C]-10[/C][C]0.174151894647307[/C][/ROW]
[ROW][C]-9[/C][C]-0.0472880511674319[/C][/ROW]
[ROW][C]-8[/C][C]-0.117736887679213[/C][/ROW]
[ROW][C]-7[/C][C]0.169921621375792[/C][/ROW]
[ROW][C]-6[/C][C]-0.0851725018878482[/C][/ROW]
[ROW][C]-5[/C][C]-0.0973660130565397[/C][/ROW]
[ROW][C]-4[/C][C]0.22212209731198[/C][/ROW]
[ROW][C]-3[/C][C]-0.0987814621230894[/C][/ROW]
[ROW][C]-2[/C][C]-0.109580714700707[/C][/ROW]
[ROW][C]-1[/C][C]0.232325598869151[/C][/ROW]
[ROW][C]0[/C][C]-0.332941705590635[/C][/ROW]
[ROW][C]1[/C][C]0.162850172010864[/C][/ROW]
[ROW][C]2[/C][C]0.0449915161201081[/C][/ROW]
[ROW][C]3[/C][C]-0.0924278515528298[/C][/ROW]
[ROW][C]4[/C][C]0.0296508653152467[/C][/ROW]
[ROW][C]5[/C][C]0.0615073508418464[/C][/ROW]
[ROW][C]6[/C][C]-0.127478017764473[/C][/ROW]
[ROW][C]7[/C][C]0.0411322911233523[/C][/ROW]
[ROW][C]8[/C][C]0.0845079848218404[/C][/ROW]
[ROW][C]9[/C][C]-0.117329934774557[/C][/ROW]
[ROW][C]10[/C][C]0.0589173006452322[/C][/ROW]
[ROW][C]11[/C][C]0.0726235609071012[/C][/ROW]
[ROW][C]12[/C][C]-0.186869605950445[/C][/ROW]
[ROW][C]13[/C][C]0.155049406738064[/C][/ROW]
[ROW][C]14[/C][C]0.0118821230821056[/C][/ROW]
[ROW][C]15[/C][C]-0.147553867663309[/C][/ROW]
[ROW][C]16[/C][C]0.14977680833976[/C][/ROW]
[ROW][C]17[/C][C]-0.0159451174170074[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=7553&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=7553&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 series1
Seasonal Period (s)12
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series1
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-17-0.102613066588082
-160.154957923479744
-15-0.0291690912772091
-14-0.0626466600703377
-130.00589310759793503
-120.148775503457454
-11-0.181087380912304
-100.174151894647307
-9-0.0472880511674319
-8-0.117736887679213
-70.169921621375792
-6-0.0851725018878482
-5-0.0973660130565397
-40.22212209731198
-3-0.0987814621230894
-2-0.109580714700707
-10.232325598869151
0-0.332941705590635
10.162850172010864
20.0449915161201081
3-0.0924278515528298
40.0296508653152467
50.0615073508418464
6-0.127478017764473
70.0411322911233523
80.0845079848218404
9-0.117329934774557
100.0589173006452322
110.0726235609071012
12-0.186869605950445
130.155049406738064
140.0118821230821056
15-0.147553867663309
160.14977680833976
17-0.0159451174170074


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; par7 = 1 ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; par7 = 1 ;
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) x <- diff(y,lag=par4,difference=par7)
x
y
bitmap(file='test1.png')
(r <- ccf(x,y,main='Cross Correlation Function',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')