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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationMon, 08 Dec 2008 12:55:50 -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/08/t1228766179h6zvxizl1r60bir.htm/, Retrieved Fri, 01 Nov 2024 00:12:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=30879, Retrieved Fri, 01 Nov 2024 00:12:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact270

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Spectral Analysis] [Spectral analyse ...] [2007-11-22 13:17:53] [ced1562ed3c62c3bc1f3b66b8f83b537]
- R PD  [Spectral Analysis] [WS 9] [2007-11-26 17:48:35] [74be16979710d4c4e7c6647856088456]
F    D    [Spectral Analysis] [Q4 derde link] [2008-12-01 18:24:30] [077ffec662d24c06be4c491541a44245]
F RMPD      [Cross Correlation Function] [Q7] [2008-12-01 18:36:52] [077ffec662d24c06be4c491541a44245]
-   P           [Cross Correlation Function] [Assesment] [2008-12-08 19:55:50] [5d823194959040fa9b19b8c8302177e6] [Current]
Feedback Forum

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12300.00
12092.80
12380.80
12196.90
9455.00
13168.00
13427.90
11980.50
11884.80
11691.70
12233.80
14341.40
13130.70
12421.10
14285.80
12864.60
11160.20
14316.20
14388.70
14013.90
13419.00
12769.60
13315.50
15332.90
14243.00
13824.40
14962.90
13202.90
12199.00
15508.90
14199.80
15169.60
14058.00
13786.20
14147.90
16541.70
13587.50
15582.40
15802.80
14130.50
12923.20
15612.20
16033.70
16036.60
14037.80
15330.60
15038.30
17401.80
14992.50
16043.70
16929.60
15921.30
14417.20
15961.00
17851.90
16483.90
14215.50
17429.70
17839.50
17629.20
Dataseries Y:
Download CSV
Histogram 
15370.60
14956.90
15469.70
15101.80
11703.70
16283.60
16726.50
14968.90
14861.00
14583.30
15305.80
17903.90
16379.40
15420.30
17870.50
15912.80
13866.50
17823.20
17872.00
17420.40
16704.40
15991.20
16583.60
19123.50
17838.70
17209.40
18586.50
16258.10
15141.60
19202.10
17746.50
19090.10
18040.30
17515.50
17751.80
21072.40
17170.00
19439.50
19795.40
17574.90
16165.40
19464.60
19932.10
19961.20
17343.40
18924.20
18574.10
21350.60
18594.60
19823.10
20844.40
19640.20
17735.40
19813.60
22238.50
20682.20
17818.60
21872.10
22117.00
21865.90

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 0 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30879&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]0 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30879&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30879&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 time0 seconds
R Server'George Udny Yule' @ 72.249.76.132






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])
-13-0.000906469894998299
-120.0624909418088126
-11-0.0442176398244366
-10-0.0424500690877031
-90.108754546545995
-80.0119734286237760
-7-0.109660924509572
-60.234104199319801
-5-0.0393656275810332
-40.035937569728965
-30.216119873015760
-2-0.183263641757785
-1-0.211245876317700
00.955448919704375
1-0.230373300733635
2-0.131077444064135
30.188614412912373
4-0.0200560166519821
5-0.0455128742868736
60.167406602822670
7-0.158306861850105
8-0.0443307937900771
90.0610174425422675
10-0.065085544161141
11-0.142185704171226
120.0243942744543664
13-0.0727214657417663

\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 & -0.000906469894998299 \tabularnewline
-12 & 0.0624909418088126 \tabularnewline
-11 & -0.0442176398244366 \tabularnewline
-10 & -0.0424500690877031 \tabularnewline
-9 & 0.108754546545995 \tabularnewline
-8 & 0.0119734286237760 \tabularnewline
-7 & -0.109660924509572 \tabularnewline
-6 & 0.234104199319801 \tabularnewline
-5 & -0.0393656275810332 \tabularnewline
-4 & 0.035937569728965 \tabularnewline
-3 & 0.216119873015760 \tabularnewline
-2 & -0.183263641757785 \tabularnewline
-1 & -0.211245876317700 \tabularnewline
0 & 0.955448919704375 \tabularnewline
1 & -0.230373300733635 \tabularnewline
2 & -0.131077444064135 \tabularnewline
3 & 0.188614412912373 \tabularnewline
4 & -0.0200560166519821 \tabularnewline
5 & -0.0455128742868736 \tabularnewline
6 & 0.167406602822670 \tabularnewline
7 & -0.158306861850105 \tabularnewline
8 & -0.0443307937900771 \tabularnewline
9 & 0.0610174425422675 \tabularnewline
10 & -0.065085544161141 \tabularnewline
11 & -0.142185704171226 \tabularnewline
12 & 0.0243942744543664 \tabularnewline
13 & -0.0727214657417663 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30879&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]-0.000906469894998299[/C][/ROW]
[ROW][C]-12[/C][C]0.0624909418088126[/C][/ROW]
[ROW][C]-11[/C][C]-0.0442176398244366[/C][/ROW]
[ROW][C]-10[/C][C]-0.0424500690877031[/C][/ROW]
[ROW][C]-9[/C][C]0.108754546545995[/C][/ROW]
[ROW][C]-8[/C][C]0.0119734286237760[/C][/ROW]
[ROW][C]-7[/C][C]-0.109660924509572[/C][/ROW]
[ROW][C]-6[/C][C]0.234104199319801[/C][/ROW]
[ROW][C]-5[/C][C]-0.0393656275810332[/C][/ROW]
[ROW][C]-4[/C][C]0.035937569728965[/C][/ROW]
[ROW][C]-3[/C][C]0.216119873015760[/C][/ROW]
[ROW][C]-2[/C][C]-0.183263641757785[/C][/ROW]
[ROW][C]-1[/C][C]-0.211245876317700[/C][/ROW]
[ROW][C]0[/C][C]0.955448919704375[/C][/ROW]
[ROW][C]1[/C][C]-0.230373300733635[/C][/ROW]
[ROW][C]2[/C][C]-0.131077444064135[/C][/ROW]
[ROW][C]3[/C][C]0.188614412912373[/C][/ROW]
[ROW][C]4[/C][C]-0.0200560166519821[/C][/ROW]
[ROW][C]5[/C][C]-0.0455128742868736[/C][/ROW]
[ROW][C]6[/C][C]0.167406602822670[/C][/ROW]
[ROW][C]7[/C][C]-0.158306861850105[/C][/ROW]
[ROW][C]8[/C][C]-0.0443307937900771[/C][/ROW]
[ROW][C]9[/C][C]0.0610174425422675[/C][/ROW]
[ROW][C]10[/C][C]-0.065085544161141[/C][/ROW]
[ROW][C]11[/C][C]-0.142185704171226[/C][/ROW]
[ROW][C]12[/C][C]0.0243942744543664[/C][/ROW]
[ROW][C]13[/C][C]-0.0727214657417663[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30879&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30879&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])
-13-0.000906469894998299
-120.0624909418088126
-11-0.0442176398244366
-10-0.0424500690877031
-90.108754546545995
-80.0119734286237760
-7-0.109660924509572
-60.234104199319801
-5-0.0393656275810332
-40.035937569728965
-30.216119873015760
-2-0.183263641757785
-1-0.211245876317700
00.955448919704375
1-0.230373300733635
2-0.131077444064135
30.188614412912373
4-0.0200560166519821
5-0.0455128742868736
60.167406602822670
7-0.158306861850105
8-0.0443307937900771
90.0610174425422675
10-0.065085544161141
11-0.142185704171226
120.0243942744543664
13-0.0727214657417663


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')