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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 computationTue, 02 Dec 2008 16:53:23 -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/03/t1228262080v0fi8bbw4utshjh.htm/, Retrieved Sun, 19 May 2024 05:57:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28539, Retrieved Sun, 19 May 2024 05:57:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact233

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Cross Correlation Function] [Gilliam Schoorel] [2008-12-02 23:53:23] [4a7b7ae341cb1fe8993cedd56bcfa583] [Current]
Feedback Forum
2008-12-07 11:58:44 [006ad2c49b6a7c2ad6ab685cfc1dae56] [reply] 
Goede differentiatie en conclusie. De student heeft de nonsenscorrelatie uit vraag 7 omgevormd door middel van differentiatie tot een goede stationaire reeks.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101.4
100.7
111.7
96.9
101.9
107.2
86.7
92.7
101.4
107.1
100.8
91
96.3
96.7
106.7
104.8
103
105.7
92.4
91
107.7
112
102.1
94.8
99.4
98.7
106.2
103.9
99.5
105.3
93.9
88.3
109.3
112.1
100.3
101.5
96.5
98.8
115.9
106.5
100.7
114.6
97.2
96.8
117.2
112.6
107
106.6
98.9
98.8
110.3
104.4
100.7
117.7
89.1
94.9
112.4
104.9
109.3
104.3
102.3
103.2
118.8
102.6
112.2
116.6
93.6
100
116.4
118.9
114.5
106.2
109.8
107.3
121.9
108.8
111.8
119.8
102.5
103.4
114.4
124.1
115.6
105.2
114.1
115.3
115.8
119.9
112.1
119.7
106.2
101.5
119.3
Dataseries Y:
Download CSV
Histogram 
119.5
125
145
105.3
116.9
120.1
88.9
78.4
114.6
113.3
117
99.6
99.4
101.9
115.2
108.5
113.8
121
92.2
90.2
101.5
126.6
93.9
89.8
93.4
101.5
110.4
105.9
108.4
113.9
86.1
69.4
101.2
100.5
98
106.6
90.1
96.9
125.9
112
100
123.9
79.8
83.4
113.6
112.9
104
109.9
99
106.3
128.9
111.1
102.9
130
87
87.5
117.6
103.4
110.8
112.6
102.5
112.4
135.6
105.1
127.7
137
91
90.5
122.4
123.3
124.3
120
118.1
119
142.7
123.6
129.6
151.6
110.4
99.2
130.5
136.2
129.7
128
121.6
135.8
143.8
147.5
136.2
156.6
123.3
104.5
143.6

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'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28539&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28539&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28539&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'Gwilym Jenkins' @ 72.249.127.135






Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series-0.2
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 series0.3
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-16-0.0595209788511938
-15-0.0504849881290043
-140.0632055435970767
-13-0.0602953835730128
-120.0304321543204108
-110.0993292717815971
-100.00785305533178062
-90.12890087739738
-80.108942585934378
-7-0.0629940489079343
-60.220571549937982
-50.115241977578015
-40.0336222899101359
-30.318704745970617
-20.179394815269819
-10.149095179059954
00.55114589986313
1-0.0223438469597091
20.055674506970504
30.221024876984052
40.0237841089268697
50.0736949195127725
60.0614874899728209
7-0.102299491064786
8-0.00947702077992275
90.0160075753047365
10-0.150371833396293
11-0.156028120599608
12-0.0776929182653795
13-0.123781177309333
140.107462932674623
15-0.0141612572413304
16-0.0762919651888055

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & -0.2 \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 & 0.3 \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
-16 & -0.0595209788511938 \tabularnewline
-15 & -0.0504849881290043 \tabularnewline
-14 & 0.0632055435970767 \tabularnewline
-13 & -0.0602953835730128 \tabularnewline
-12 & 0.0304321543204108 \tabularnewline
-11 & 0.0993292717815971 \tabularnewline
-10 & 0.00785305533178062 \tabularnewline
-9 & 0.12890087739738 \tabularnewline
-8 & 0.108942585934378 \tabularnewline
-7 & -0.0629940489079343 \tabularnewline
-6 & 0.220571549937982 \tabularnewline
-5 & 0.115241977578015 \tabularnewline
-4 & 0.0336222899101359 \tabularnewline
-3 & 0.318704745970617 \tabularnewline
-2 & 0.179394815269819 \tabularnewline
-1 & 0.149095179059954 \tabularnewline
0 & 0.55114589986313 \tabularnewline
1 & -0.0223438469597091 \tabularnewline
2 & 0.055674506970504 \tabularnewline
3 & 0.221024876984052 \tabularnewline
4 & 0.0237841089268697 \tabularnewline
5 & 0.0736949195127725 \tabularnewline
6 & 0.0614874899728209 \tabularnewline
7 & -0.102299491064786 \tabularnewline
8 & -0.00947702077992275 \tabularnewline
9 & 0.0160075753047365 \tabularnewline
10 & -0.150371833396293 \tabularnewline
11 & -0.156028120599608 \tabularnewline
12 & -0.0776929182653795 \tabularnewline
13 & -0.123781177309333 \tabularnewline
14 & 0.107462932674623 \tabularnewline
15 & -0.0141612572413304 \tabularnewline
16 & -0.0762919651888055 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28539&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]-0.2[/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]0.3[/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]-16[/C][C]-0.0595209788511938[/C][/ROW]
[ROW][C]-15[/C][C]-0.0504849881290043[/C][/ROW]
[ROW][C]-14[/C][C]0.0632055435970767[/C][/ROW]
[ROW][C]-13[/C][C]-0.0602953835730128[/C][/ROW]
[ROW][C]-12[/C][C]0.0304321543204108[/C][/ROW]
[ROW][C]-11[/C][C]0.0993292717815971[/C][/ROW]
[ROW][C]-10[/C][C]0.00785305533178062[/C][/ROW]
[ROW][C]-9[/C][C]0.12890087739738[/C][/ROW]
[ROW][C]-8[/C][C]0.108942585934378[/C][/ROW]
[ROW][C]-7[/C][C]-0.0629940489079343[/C][/ROW]
[ROW][C]-6[/C][C]0.220571549937982[/C][/ROW]
[ROW][C]-5[/C][C]0.115241977578015[/C][/ROW]
[ROW][C]-4[/C][C]0.0336222899101359[/C][/ROW]
[ROW][C]-3[/C][C]0.318704745970617[/C][/ROW]
[ROW][C]-2[/C][C]0.179394815269819[/C][/ROW]
[ROW][C]-1[/C][C]0.149095179059954[/C][/ROW]
[ROW][C]0[/C][C]0.55114589986313[/C][/ROW]
[ROW][C]1[/C][C]-0.0223438469597091[/C][/ROW]
[ROW][C]2[/C][C]0.055674506970504[/C][/ROW]
[ROW][C]3[/C][C]0.221024876984052[/C][/ROW]
[ROW][C]4[/C][C]0.0237841089268697[/C][/ROW]
[ROW][C]5[/C][C]0.0736949195127725[/C][/ROW]
[ROW][C]6[/C][C]0.0614874899728209[/C][/ROW]
[ROW][C]7[/C][C]-0.102299491064786[/C][/ROW]
[ROW][C]8[/C][C]-0.00947702077992275[/C][/ROW]
[ROW][C]9[/C][C]0.0160075753047365[/C][/ROW]
[ROW][C]10[/C][C]-0.150371833396293[/C][/ROW]
[ROW][C]11[/C][C]-0.156028120599608[/C][/ROW]
[ROW][C]12[/C][C]-0.0776929182653795[/C][/ROW]
[ROW][C]13[/C][C]-0.123781177309333[/C][/ROW]
[ROW][C]14[/C][C]0.107462932674623[/C][/ROW]
[ROW][C]15[/C][C]-0.0141612572413304[/C][/ROW]
[ROW][C]16[/C][C]-0.0762919651888055[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28539&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28539&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 series-0.2
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 series0.3
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series1
krho(Y[t],X[t+k])
-16-0.0595209788511938
-15-0.0504849881290043
-140.0632055435970767
-13-0.0602953835730128
-120.0304321543204108
-110.0993292717815971
-100.00785305533178062
-90.12890087739738
-80.108942585934378
-7-0.0629940489079343
-60.220571549937982
-50.115241977578015
-40.0336222899101359
-30.318704745970617
-20.179394815269819
-10.149095179059954
00.55114589986313
1-0.0223438469597091
20.055674506970504
30.221024876984052
40.0237841089268697
50.0736949195127725
60.0614874899728209
7-0.102299491064786
8-0.00947702077992275
90.0160075753047365
10-0.150371833396293
11-0.156028120599608
12-0.0776929182653795
13-0.123781177309333
140.107462932674623
15-0.0141612572413304
16-0.0762919651888055


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ;
Parameters (R input):
par1 = -0.2 ; par2 = 0 ; par3 = 1 ; par4 = 12 ; par5 = 0.3 ; 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')