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Description of Statistical Computation

Author's title

Cross Correlation Function Algemeen indexcijfer metaalverwerking; Transport...

Author*Unverified author*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationTue, 09 Dec 2008 10:38:05 -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/09/t1228844429uef4822kuhv7pdv.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=31621, 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 Impact202

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [(Partial) Autocorrelation Function] [(P)ACF Transportm...] [2008-12-04 18:19:15] [65eec331235880e0070acfba94c20cfa]
- RMPD    [Cross Correlation Function] [Cross Correlation...] [2008-12-09 17:38:05] [d41d8cd98f00b204e9800998ecf8427e] [Current]
- RMP       [Pearson Correlation] [Pearson Correlation] [2008-12-12 13:23:51] [74be16979710d4c4e7c6647856088456]
-   PD      [Cross Correlation Function] [sqddssssdsss] [2008-12-17 10:28:49] [74be16979710d4c4e7c6647856088456]
-   PD        [Cross Correlation Function] [gvgfkjhgl;kjhg] [2008-12-18 09:49:28] [74be16979710d4c4e7c6647856088456]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
100.8
105.3
116.1
112.8
114.5
117.2
77.1
80.1
120.3
133.4
109.4
93.2
91.2
99.2
108.2
101.5
106.9
104.4
77.9
60
99.5
95
105.6
102.5
93.3
97.3
127
111.7
96.4
133
72.2
95.8
124.1
127.6
110.7
104.6
112.7
115.3
139.4
119
97.4
154
81.5
88.8
127.7
105.1
114.9
106.4
104.5
121.6
141.4
99
126.7
134.1
81.3
88.6
132.7
132.9
134.4
103.7
119.7
115
132.9
108.5
113.9
142
97.7
92.2
128.8
134.9
128.2
114.8
117.9
Dataseries Y:
Download CSV
Histogram 
92
95.9
108.8
103.4
102.1
110.1
83.2
82.7
106.8
113.7
102.5
96.6
92.1
95.6
102.3
98.6
98.2
104.5
84
73.8
103.9
106
97.2
102.6
89
93.8
116.7
106.8
98.5
118.7
90
91.9
113.3
113.1
104.1
108.7
96.7
101
116.9
105.8
99
129.4
83
88.9
115.9
104.2
113.4
112.2
100.8
107.3
126.6
102.9
117.9
128.8
87.5
93.8
122.7
126.2
124.6
116.7
115.2
111.1
129.9
113.3
118.5
137.9
103.6
101.7
127.4
137.5
128.3
118.2
117.1

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'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 & 1 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31621&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31621&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31621&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'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 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 series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-14-0.164673256129133
-13-0.187411358545794
-12-0.315585262170596
-11-0.106126163367583
-10-0.159494862810995
-9-0.0970122937480774
-80.122088929952853
-7-0.160335176086776
-6-0.0139393851133349
-50.121991810595674
-40.0372927204823358
-30.274862518736692
-20.239022375643630
-10.0864170068343824
00.782990773559375
10.182786576421404
20.321833199316384
30.316248209092184
40.0741703536745971
50.239477397572063
60.346905006249803
7-0.0398181566750108
80.156708577249843
9-0.0199375181149879
10-0.137728518199153
110.0106337251821572
12-0.35581440157513
13-0.198489129147259
14-0.246399723790195

\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 & 0 \tabularnewline
Degree of seasonal differencing (D) of Y series & 1 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-14 & -0.164673256129133 \tabularnewline
-13 & -0.187411358545794 \tabularnewline
-12 & -0.315585262170596 \tabularnewline
-11 & -0.106126163367583 \tabularnewline
-10 & -0.159494862810995 \tabularnewline
-9 & -0.0970122937480774 \tabularnewline
-8 & 0.122088929952853 \tabularnewline
-7 & -0.160335176086776 \tabularnewline
-6 & -0.0139393851133349 \tabularnewline
-5 & 0.121991810595674 \tabularnewline
-4 & 0.0372927204823358 \tabularnewline
-3 & 0.274862518736692 \tabularnewline
-2 & 0.239022375643630 \tabularnewline
-1 & 0.0864170068343824 \tabularnewline
0 & 0.782990773559375 \tabularnewline
1 & 0.182786576421404 \tabularnewline
2 & 0.321833199316384 \tabularnewline
3 & 0.316248209092184 \tabularnewline
4 & 0.0741703536745971 \tabularnewline
5 & 0.239477397572063 \tabularnewline
6 & 0.346905006249803 \tabularnewline
7 & -0.0398181566750108 \tabularnewline
8 & 0.156708577249843 \tabularnewline
9 & -0.0199375181149879 \tabularnewline
10 & -0.137728518199153 \tabularnewline
11 & 0.0106337251821572 \tabularnewline
12 & -0.35581440157513 \tabularnewline
13 & -0.198489129147259 \tabularnewline
14 & -0.246399723790195 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31621&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]0[/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]-14[/C][C]-0.164673256129133[/C][/ROW]
[ROW][C]-13[/C][C]-0.187411358545794[/C][/ROW]
[ROW][C]-12[/C][C]-0.315585262170596[/C][/ROW]
[ROW][C]-11[/C][C]-0.106126163367583[/C][/ROW]
[ROW][C]-10[/C][C]-0.159494862810995[/C][/ROW]
[ROW][C]-9[/C][C]-0.0970122937480774[/C][/ROW]
[ROW][C]-8[/C][C]0.122088929952853[/C][/ROW]
[ROW][C]-7[/C][C]-0.160335176086776[/C][/ROW]
[ROW][C]-6[/C][C]-0.0139393851133349[/C][/ROW]
[ROW][C]-5[/C][C]0.121991810595674[/C][/ROW]
[ROW][C]-4[/C][C]0.0372927204823358[/C][/ROW]
[ROW][C]-3[/C][C]0.274862518736692[/C][/ROW]
[ROW][C]-2[/C][C]0.239022375643630[/C][/ROW]
[ROW][C]-1[/C][C]0.0864170068343824[/C][/ROW]
[ROW][C]0[/C][C]0.782990773559375[/C][/ROW]
[ROW][C]1[/C][C]0.182786576421404[/C][/ROW]
[ROW][C]2[/C][C]0.321833199316384[/C][/ROW]
[ROW][C]3[/C][C]0.316248209092184[/C][/ROW]
[ROW][C]4[/C][C]0.0741703536745971[/C][/ROW]
[ROW][C]5[/C][C]0.239477397572063[/C][/ROW]
[ROW][C]6[/C][C]0.346905006249803[/C][/ROW]
[ROW][C]7[/C][C]-0.0398181566750108[/C][/ROW]
[ROW][C]8[/C][C]0.156708577249843[/C][/ROW]
[ROW][C]9[/C][C]-0.0199375181149879[/C][/ROW]
[ROW][C]10[/C][C]-0.137728518199153[/C][/ROW]
[ROW][C]11[/C][C]0.0106337251821572[/C][/ROW]
[ROW][C]12[/C][C]-0.35581440157513[/C][/ROW]
[ROW][C]13[/C][C]-0.198489129147259[/C][/ROW]
[ROW][C]14[/C][C]-0.246399723790195[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31621&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31621&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 series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-14-0.164673256129133
-13-0.187411358545794
-12-0.315585262170596
-11-0.106126163367583
-10-0.159494862810995
-9-0.0970122937480774
-80.122088929952853
-7-0.160335176086776
-6-0.0139393851133349
-50.121991810595674
-40.0372927204823358
-30.274862518736692
-20.239022375643630
-10.0864170068343824
00.782990773559375
10.182786576421404
20.321833199316384
30.316248209092184
40.0741703536745971
50.239477397572063
60.346905006249803
7-0.0398181566750108
80.156708577249843
9-0.0199375181149879
10-0.137728518199153
110.0106337251821572
12-0.35581440157513
13-0.198489129147259
14-0.246399723790195


Figures (Output of Computation)

Input Parameters & R Code

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