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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 computationFri, 23 Nov 2007 08:38:27 -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/23/t1195831832vl63jz3p7mpuhtk.htm/, Retrieved Sun, 28 Apr 2024 19:37:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=6196, Retrieved Sun, 28 Apr 2024 19:37:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWorkshop 7, question 4, cross correlation, Totale en Niet-Duurzame consumptiegoederen
Estimated Impact163

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] [Workshop 7, quest...] [2007-11-23 15:38:27] [181c187d2008ac66a37ecc12859b08c5] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
108.4
117
103.8
100.8
110.6
104
112.6
107.3
98.9
109.8
104.9
102.2
123.9
124.9
112.7
121.9
100.6
104.3
120.4
107.5
102.9
125.6
107.5
108.8
128.4
121.1
119.5
128.7
108.7
105.5
119.8
111.3
110.6
120.1
97.5
107.7
127.3
117.2
119.8
116.2
111
112.4
130.6
109.1
118.8
123.9
101.6
112.8
128
129.6
125.8
119.5
115.7
113.6
129.7
112
116.8
126.3
112.9
115.9
Dataseries Y:
Download CSV
Histogram 
112.7
118.4
108.1
105.4
114.6
106.9
115.9
109.8
101.8
114.2
110.8
108.4
127.5
128.6
116.6
127.4
105
108.3
125
111.6
106.5
130.3
115
116.1
134
126.5
125.8
136.4
114.9
110.9
125.5
116.8
116.8
125.5
104.2
115.1
132.8
123.3
124.8
122
117.4
117.9
137.4
114.6
124.7
129.6
109.4
120.9
134.9
136.3
133.2
127.2
122.7
120.5
137.8
119.1
124.3
134.3
121.7
125

Tables (Output of Computation)





Summary of compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 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 & 0 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=6196&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]0 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=6196&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=6196&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 time0 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






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])
-130.0394029775322081
-120.303991523666477
-11-0.229037412475491
-100.08339442298376
-9-0.0824157078607982
-8-0.241718018666962
-70.131060246785048
-6-0.0428979596363256
-5-0.276083420907385
-40.0159666412782698
-3-0.131207303438408
-2-0.313729119699994
-1-0.035594414008618
0-0.402358158874314
1-0.166296350982909
20.118420078491692
3-0.169353220103330
4-0.0350584777806734
50.132087712636640
6-0.125392344415606
70.0261821446196233
80.0714555894300044
9-0.157593300114713
100.0798318119810698
110.132841015974748
12-0.105092197774547
130.0827940220959549

\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
-13 & 0.0394029775322081 \tabularnewline
-12 & 0.303991523666477 \tabularnewline
-11 & -0.229037412475491 \tabularnewline
-10 & 0.08339442298376 \tabularnewline
-9 & -0.0824157078607982 \tabularnewline
-8 & -0.241718018666962 \tabularnewline
-7 & 0.131060246785048 \tabularnewline
-6 & -0.0428979596363256 \tabularnewline
-5 & -0.276083420907385 \tabularnewline
-4 & 0.0159666412782698 \tabularnewline
-3 & -0.131207303438408 \tabularnewline
-2 & -0.313729119699994 \tabularnewline
-1 & -0.035594414008618 \tabularnewline
0 & -0.402358158874314 \tabularnewline
1 & -0.166296350982909 \tabularnewline
2 & 0.118420078491692 \tabularnewline
3 & -0.169353220103330 \tabularnewline
4 & -0.0350584777806734 \tabularnewline
5 & 0.132087712636640 \tabularnewline
6 & -0.125392344415606 \tabularnewline
7 & 0.0261821446196233 \tabularnewline
8 & 0.0714555894300044 \tabularnewline
9 & -0.157593300114713 \tabularnewline
10 & 0.0798318119810698 \tabularnewline
11 & 0.132841015974748 \tabularnewline
12 & -0.105092197774547 \tabularnewline
13 & 0.0827940220959549 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=6196&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]-13[/C][C]0.0394029775322081[/C][/ROW]
[ROW][C]-12[/C][C]0.303991523666477[/C][/ROW]
[ROW][C]-11[/C][C]-0.229037412475491[/C][/ROW]
[ROW][C]-10[/C][C]0.08339442298376[/C][/ROW]
[ROW][C]-9[/C][C]-0.0824157078607982[/C][/ROW]
[ROW][C]-8[/C][C]-0.241718018666962[/C][/ROW]
[ROW][C]-7[/C][C]0.131060246785048[/C][/ROW]
[ROW][C]-6[/C][C]-0.0428979596363256[/C][/ROW]
[ROW][C]-5[/C][C]-0.276083420907385[/C][/ROW]
[ROW][C]-4[/C][C]0.0159666412782698[/C][/ROW]
[ROW][C]-3[/C][C]-0.131207303438408[/C][/ROW]
[ROW][C]-2[/C][C]-0.313729119699994[/C][/ROW]
[ROW][C]-1[/C][C]-0.035594414008618[/C][/ROW]
[ROW][C]0[/C][C]-0.402358158874314[/C][/ROW]
[ROW][C]1[/C][C]-0.166296350982909[/C][/ROW]
[ROW][C]2[/C][C]0.118420078491692[/C][/ROW]
[ROW][C]3[/C][C]-0.169353220103330[/C][/ROW]
[ROW][C]4[/C][C]-0.0350584777806734[/C][/ROW]
[ROW][C]5[/C][C]0.132087712636640[/C][/ROW]
[ROW][C]6[/C][C]-0.125392344415606[/C][/ROW]
[ROW][C]7[/C][C]0.0261821446196233[/C][/ROW]
[ROW][C]8[/C][C]0.0714555894300044[/C][/ROW]
[ROW][C]9[/C][C]-0.157593300114713[/C][/ROW]
[ROW][C]10[/C][C]0.0798318119810698[/C][/ROW]
[ROW][C]11[/C][C]0.132841015974748[/C][/ROW]
[ROW][C]12[/C][C]-0.105092197774547[/C][/ROW]
[ROW][C]13[/C][C]0.0827940220959549[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=6196&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=6196&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])
-130.0394029775322081
-120.303991523666477
-11-0.229037412475491
-100.08339442298376
-9-0.0824157078607982
-8-0.241718018666962
-70.131060246785048
-6-0.0428979596363256
-5-0.276083420907385
-40.0159666412782698
-3-0.131207303438408
-2-0.313729119699994
-1-0.035594414008618
0-0.402358158874314
1-0.166296350982909
20.118420078491692
3-0.169353220103330
4-0.0350584777806734
50.132087712636640
6-0.125392344415606
70.0261821446196233
80.0714555894300044
9-0.157593300114713
100.0798318119810698
110.132841015974748
12-0.105092197774547
130.0827940220959549


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) 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')