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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 computationMon, 26 Nov 2007 17:06:03 -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/27/t1196121414q8zb3lv6lgtzo9f.htm/, Retrieved Sun, 05 May 2024 14:34:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=6751, Retrieved Sun, 05 May 2024 14:34:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact224

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] [Inducing Stationa...] [2007-11-27 00:06:03] [640491d00f3c9cca22cbf779aa38ac16] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101.30
97.60
96.40
97.00
96.40
94.70
89.30
85.90
83.30
81.50
85.00
84.80
87.50
89.00
90.00
89.60
87.40
84.80
81.90
81.10
79.10
80.50
88.50
90.90
84.90
80.00
76.50
75.40
73.50
74.30
77.70
77.90
76.70
77.20
86.00
86.90
92.00
101.70
104.50
101.70
100.60
100.30
102.50
101.00
108.60
103.40
106.40
106.60
108.90
110.50
114.00
112.80
109.60
116.00
124.60
129.00
131.50
138.60
138.10
146.30
157.60
158.40
176.30
199.90
210.40
202.60
207.10
202.00
203.40
216.30
207.30
203.50
204.40
203.70
205.70
208.00
209.30
208.70
206.50
204.50
Dataseries Y:
Download CSV
Histogram 
100.70
97.90
96.50
96.60
96.60
95.50
91.80
89.30
87.00
85.90
88.00
87.90
89.20
90.90
91.60
90.20
89.10
87.50
86.30
86.00
84.40
86.10
91.00
92.70
88.00
84.30
82.20
80.80
79.40
80.20
82.20
82.20
81.20
82.10
88.10
88.50
92.10
98.60
100.90
100.60
101.10
102.10
103.60
102.80
108.30
104.00
106.10
106.30
109.00
111.00
113.70
112.70
110.30
114.50
119.30
121.80
125.40
129.70
129.40
134.50
141.20
141.40
152.20
167.70
173.30
168.70
172.60
169.80
172.00
179.40
174.60
172.50
172.60
176.30
178.90
179.60
179.90
180.30
180.90
177.70

Tables (Output of Computation)





Summary of compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=6751&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=6751&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=6751&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series0.6
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 series0.9
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-160.319031613893005
-150.374908830793322
-140.426496659265706
-130.478744692973941
-120.5278640049764
-110.57623894074026
-100.629501882860105
-90.677729447328674
-80.721466475698734
-70.764898912668919
-60.805203454320882
-50.842432604821608
-40.878955413464269
-30.913979243037946
-20.946023256512271
-10.974885526860873
00.998524314989452
10.970545676486246
20.935844415161084
30.899843615015843
40.861476995732883
50.821917600446748
60.781556221183869
70.738958996204407
80.695890988125731
90.652249710873843
100.605199751894239
110.554687670356904
120.508942125054212
130.463737297623657
140.415373582968123
150.368753292570903
160.318286602012781

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 0.6 \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 & 0.9 \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
-16 & 0.319031613893005 \tabularnewline
-15 & 0.374908830793322 \tabularnewline
-14 & 0.426496659265706 \tabularnewline
-13 & 0.478744692973941 \tabularnewline
-12 & 0.5278640049764 \tabularnewline
-11 & 0.57623894074026 \tabularnewline
-10 & 0.629501882860105 \tabularnewline
-9 & 0.677729447328674 \tabularnewline
-8 & 0.721466475698734 \tabularnewline
-7 & 0.764898912668919 \tabularnewline
-6 & 0.805203454320882 \tabularnewline
-5 & 0.842432604821608 \tabularnewline
-4 & 0.878955413464269 \tabularnewline
-3 & 0.913979243037946 \tabularnewline
-2 & 0.946023256512271 \tabularnewline
-1 & 0.974885526860873 \tabularnewline
0 & 0.998524314989452 \tabularnewline
1 & 0.970545676486246 \tabularnewline
2 & 0.935844415161084 \tabularnewline
3 & 0.899843615015843 \tabularnewline
4 & 0.861476995732883 \tabularnewline
5 & 0.821917600446748 \tabularnewline
6 & 0.781556221183869 \tabularnewline
7 & 0.738958996204407 \tabularnewline
8 & 0.695890988125731 \tabularnewline
9 & 0.652249710873843 \tabularnewline
10 & 0.605199751894239 \tabularnewline
11 & 0.554687670356904 \tabularnewline
12 & 0.508942125054212 \tabularnewline
13 & 0.463737297623657 \tabularnewline
14 & 0.415373582968123 \tabularnewline
15 & 0.368753292570903 \tabularnewline
16 & 0.318286602012781 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=6751&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]0.6[/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]0.9[/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]-16[/C][C]0.319031613893005[/C][/ROW]
[ROW][C]-15[/C][C]0.374908830793322[/C][/ROW]
[ROW][C]-14[/C][C]0.426496659265706[/C][/ROW]
[ROW][C]-13[/C][C]0.478744692973941[/C][/ROW]
[ROW][C]-12[/C][C]0.5278640049764[/C][/ROW]
[ROW][C]-11[/C][C]0.57623894074026[/C][/ROW]
[ROW][C]-10[/C][C]0.629501882860105[/C][/ROW]
[ROW][C]-9[/C][C]0.677729447328674[/C][/ROW]
[ROW][C]-8[/C][C]0.721466475698734[/C][/ROW]
[ROW][C]-7[/C][C]0.764898912668919[/C][/ROW]
[ROW][C]-6[/C][C]0.805203454320882[/C][/ROW]
[ROW][C]-5[/C][C]0.842432604821608[/C][/ROW]
[ROW][C]-4[/C][C]0.878955413464269[/C][/ROW]
[ROW][C]-3[/C][C]0.913979243037946[/C][/ROW]
[ROW][C]-2[/C][C]0.946023256512271[/C][/ROW]
[ROW][C]-1[/C][C]0.974885526860873[/C][/ROW]
[ROW][C]0[/C][C]0.998524314989452[/C][/ROW]
[ROW][C]1[/C][C]0.970545676486246[/C][/ROW]
[ROW][C]2[/C][C]0.935844415161084[/C][/ROW]
[ROW][C]3[/C][C]0.899843615015843[/C][/ROW]
[ROW][C]4[/C][C]0.861476995732883[/C][/ROW]
[ROW][C]5[/C][C]0.821917600446748[/C][/ROW]
[ROW][C]6[/C][C]0.781556221183869[/C][/ROW]
[ROW][C]7[/C][C]0.738958996204407[/C][/ROW]
[ROW][C]8[/C][C]0.695890988125731[/C][/ROW]
[ROW][C]9[/C][C]0.652249710873843[/C][/ROW]
[ROW][C]10[/C][C]0.605199751894239[/C][/ROW]
[ROW][C]11[/C][C]0.554687670356904[/C][/ROW]
[ROW][C]12[/C][C]0.508942125054212[/C][/ROW]
[ROW][C]13[/C][C]0.463737297623657[/C][/ROW]
[ROW][C]14[/C][C]0.415373582968123[/C][/ROW]
[ROW][C]15[/C][C]0.368753292570903[/C][/ROW]
[ROW][C]16[/C][C]0.318286602012781[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=6751&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=6751&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 series0.6
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 series0.9
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-160.319031613893005
-150.374908830793322
-140.426496659265706
-130.478744692973941
-120.5278640049764
-110.57623894074026
-100.629501882860105
-90.677729447328674
-80.721466475698734
-70.764898912668919
-60.805203454320882
-50.842432604821608
-40.878955413464269
-30.913979243037946
-20.946023256512271
-10.974885526860873
00.998524314989452
10.970545676486246
20.935844415161084
30.899843615015843
40.861476995732883
50.821917600446748
60.781556221183869
70.738958996204407
80.695890988125731
90.652249710873843
100.605199751894239
110.554687670356904
120.508942125054212
130.463737297623657
140.415373582968123
150.368753292570903
160.318286602012781


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 0.6 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; par5 = 0.9 ; 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) 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')