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Author

*The author of this computation has been verified*

R Software Module

rwasp_spectrum.wasp

Title produced by software

Spectral Analysis

Date of computation

Sat, 06 Dec 2008 11:24:41 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/06/t1228588089o5jles4i5xs94zo.htm/, Retrieved Fri, 17 May 2024 07:01:13 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=29796, Retrieved Fri, 17 May 2024 07:01:13 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

251

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
F RMP   [Variance Reduction Matrix] [step 2] [2008-12-06 09:12:19] [9f5bfe3b95f9ec3d2ed4c0a560a9648a]
F RMPD    [Spectral Analysis] [step 2] [2008-12-06 18:09:24] [9f5bfe3b95f9ec3d2ed4c0a560a9648a]
F   P         [Spectral Analysis] [step 3] [2008-12-06 18:24:41] [a9e6d7cd6e144e8b311d9f96a24c5a25] [Current]

Feedback Forum

2008-12-13 11:29:02 [Ken Wright] [reply] 
correct, uit het cumulative periodogram is zowel de steile helling weg, die duide op LT trend en ook de getrapte structuur die duidde op seizoenaliteit. Er is nog wel duidelijk een ander proces aan de gang omdat het cumulative periodogram geen verloop heeft als een diagonaal
2008-12-16 19:12:19 [Kevin Vermeiren] [reply] 
Raw periodogram 
De student geeft een goede bespreking van het raw periodogram. Het klopt dat er hier duidelijk geen lange termijn trend en ook geen seizoenaliteit meer in zit. Door de spectrum waarden te bekijken zien we inderdaad dat de spreiding redelijk constant is.   
 
Cumulative periodogram 
De student geeft terecht weer dat de lange termijn trend en de seizoenaliteit verdwenen zijn. Hier had nog vermeld mogen worden dat dit te zien is aan het feit dat het steil begin van de curve hier niet meer van toepassing is en dat het uitgesproken trapsgewijs verloop hier niet meer opgaat. Toch  kunnen we zeggen dat er nog steeds golfbewegingen aanwezig zijn die we kunnen verklaren en patronen die we kunnen voorspellen. Verder onderzoek is aangewezen.

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Histogram

Boxplots

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 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=29796&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=29796&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29796&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Raw Periodogram
Parameter	Value
Box-Cox transformation parameter (lambda)	-0.1
Degree of non-seasonal differencing (d)	1
Degree of seasonal differencing (D)	1
Seasonal Period (s)	12
Frequency (Period)	Spectrum
0.0125 (80)	0.000151
0.025 (40)	3.2e-05
0.0375 (26.6667)	0.000386
0.05 (20)	0.000796
0.0625 (16)	2e-05
0.075 (13.3333)	1.7e-05
0.0875 (11.4286)	1.3e-05
0.1 (10)	0.000668
0.1125 (8.8889)	0.000531
0.125 (8)	0.000113
0.1375 (7.2727)	0.000382
0.15 (6.6667)	2.9e-05
0.1625 (6.1538)	0.000204
0.175 (5.7143)	6.8e-05
0.1875 (5.3333)	0.000361
0.2 (5)	0.00086
0.2125 (4.7059)	0.000198
0.225 (4.4444)	0.001446
0.2375 (4.2105)	0.003144
0.25 (4)	0.000276
0.2625 (3.8095)	0.000297
0.275 (3.6364)	0.000674
0.2875 (3.4783)	0.000707
0.3 (3.3333)	0.004146
0.3125 (3.2)	0.001686
0.325 (3.0769)	0.000799
0.3375 (2.963)	0.002227
0.35 (2.8571)	0.008855
0.3625 (2.7586)	0.000636
0.375 (2.6667)	0.000103
0.3875 (2.5806)	0.002778
0.4 (2.5)	0.000416
0.4125 (2.4242)	0.000297
0.425 (2.3529)	0.001188
0.4375 (2.2857)	0.001629
0.45 (2.2222)	0.003063
0.4625 (2.1622)	0.000443
0.475 (2.1053)	0.003262
0.4875 (2.0513)	0.000376
0.5 (2)	2.3e-05

\begin{tabular}{lllllllll}
\hline
Raw Periodogram \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) & -0.1 \tabularnewline
Degree of non-seasonal differencing (d) & 1 \tabularnewline
Degree of seasonal differencing (D) & 1 \tabularnewline
Seasonal Period (s) & 12 \tabularnewline
Frequency (Period) & Spectrum \tabularnewline
0.0125 (80) & 0.000151 \tabularnewline
0.025 (40) & 3.2e-05 \tabularnewline
0.0375 (26.6667) & 0.000386 \tabularnewline
0.05 (20) & 0.000796 \tabularnewline
0.0625 (16) & 2e-05 \tabularnewline
0.075 (13.3333) & 1.7e-05 \tabularnewline
0.0875 (11.4286) & 1.3e-05 \tabularnewline
0.1 (10) & 0.000668 \tabularnewline
0.1125 (8.8889) & 0.000531 \tabularnewline
0.125 (8) & 0.000113 \tabularnewline
0.1375 (7.2727) & 0.000382 \tabularnewline
0.15 (6.6667) & 2.9e-05 \tabularnewline
0.1625 (6.1538) & 0.000204 \tabularnewline
0.175 (5.7143) & 6.8e-05 \tabularnewline
0.1875 (5.3333) & 0.000361 \tabularnewline
0.2 (5) & 0.00086 \tabularnewline
0.2125 (4.7059) & 0.000198 \tabularnewline
0.225 (4.4444) & 0.001446 \tabularnewline
0.2375 (4.2105) & 0.003144 \tabularnewline
0.25 (4) & 0.000276 \tabularnewline
0.2625 (3.8095) & 0.000297 \tabularnewline
0.275 (3.6364) & 0.000674 \tabularnewline
0.2875 (3.4783) & 0.000707 \tabularnewline
0.3 (3.3333) & 0.004146 \tabularnewline
0.3125 (3.2) & 0.001686 \tabularnewline
0.325 (3.0769) & 0.000799 \tabularnewline
0.3375 (2.963) & 0.002227 \tabularnewline
0.35 (2.8571) & 0.008855 \tabularnewline
0.3625 (2.7586) & 0.000636 \tabularnewline
0.375 (2.6667) & 0.000103 \tabularnewline
0.3875 (2.5806) & 0.002778 \tabularnewline
0.4 (2.5) & 0.000416 \tabularnewline
0.4125 (2.4242) & 0.000297 \tabularnewline
0.425 (2.3529) & 0.001188 \tabularnewline
0.4375 (2.2857) & 0.001629 \tabularnewline
0.45 (2.2222) & 0.003063 \tabularnewline
0.4625 (2.1622) & 0.000443 \tabularnewline
0.475 (2.1053) & 0.003262 \tabularnewline
0.4875 (2.0513) & 0.000376 \tabularnewline
0.5 (2) & 2.3e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29796&T=1

[TABLE]
[ROW][C]Raw Periodogram[/C][/ROW]
[ROW][C]Parameter[/C][C]Value[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda)[/C][C]-0.1[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d)[/C][C]1[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D)[/C][C]1[/C][/ROW]
[ROW][C]Seasonal Period (s)[/C][C]12[/C][/ROW]
[ROW][C]Frequency (Period)[/C][C]Spectrum[/C][/ROW]
[ROW][C]0.0125 (80)[/C][C]0.000151[/C][/ROW]
[ROW][C]0.025 (40)[/C][C]3.2e-05[/C][/ROW]
[ROW][C]0.0375 (26.6667)[/C][C]0.000386[/C][/ROW]
[ROW][C]0.05 (20)[/C][C]0.000796[/C][/ROW]
[ROW][C]0.0625 (16)[/C][C]2e-05[/C][/ROW]
[ROW][C]0.075 (13.3333)[/C][C]1.7e-05[/C][/ROW]
[ROW][C]0.0875 (11.4286)[/C][C]1.3e-05[/C][/ROW]
[ROW][C]0.1 (10)[/C][C]0.000668[/C][/ROW]
[ROW][C]0.1125 (8.8889)[/C][C]0.000531[/C][/ROW]
[ROW][C]0.125 (8)[/C][C]0.000113[/C][/ROW]
[ROW][C]0.1375 (7.2727)[/C][C]0.000382[/C][/ROW]
[ROW][C]0.15 (6.6667)[/C][C]2.9e-05[/C][/ROW]
[ROW][C]0.1625 (6.1538)[/C][C]0.000204[/C][/ROW]
[ROW][C]0.175 (5.7143)[/C][C]6.8e-05[/C][/ROW]
[ROW][C]0.1875 (5.3333)[/C][C]0.000361[/C][/ROW]
[ROW][C]0.2 (5)[/C][C]0.00086[/C][/ROW]
[ROW][C]0.2125 (4.7059)[/C][C]0.000198[/C][/ROW]
[ROW][C]0.225 (4.4444)[/C][C]0.001446[/C][/ROW]
[ROW][C]0.2375 (4.2105)[/C][C]0.003144[/C][/ROW]
[ROW][C]0.25 (4)[/C][C]0.000276[/C][/ROW]
[ROW][C]0.2625 (3.8095)[/C][C]0.000297[/C][/ROW]
[ROW][C]0.275 (3.6364)[/C][C]0.000674[/C][/ROW]
[ROW][C]0.2875 (3.4783)[/C][C]0.000707[/C][/ROW]
[ROW][C]0.3 (3.3333)[/C][C]0.004146[/C][/ROW]
[ROW][C]0.3125 (3.2)[/C][C]0.001686[/C][/ROW]
[ROW][C]0.325 (3.0769)[/C][C]0.000799[/C][/ROW]
[ROW][C]0.3375 (2.963)[/C][C]0.002227[/C][/ROW]
[ROW][C]0.35 (2.8571)[/C][C]0.008855[/C][/ROW]
[ROW][C]0.3625 (2.7586)[/C][C]0.000636[/C][/ROW]
[ROW][C]0.375 (2.6667)[/C][C]0.000103[/C][/ROW]
[ROW][C]0.3875 (2.5806)[/C][C]0.002778[/C][/ROW]
[ROW][C]0.4 (2.5)[/C][C]0.000416[/C][/ROW]
[ROW][C]0.4125 (2.4242)[/C][C]0.000297[/C][/ROW]
[ROW][C]0.425 (2.3529)[/C][C]0.001188[/C][/ROW]
[ROW][C]0.4375 (2.2857)[/C][C]0.001629[/C][/ROW]
[ROW][C]0.45 (2.2222)[/C][C]0.003063[/C][/ROW]
[ROW][C]0.4625 (2.1622)[/C][C]0.000443[/C][/ROW]
[ROW][C]0.475 (2.1053)[/C][C]0.003262[/C][/ROW]
[ROW][C]0.4875 (2.0513)[/C][C]0.000376[/C][/ROW]
[ROW][C]0.5 (2)[/C][C]2.3e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29796&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29796&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Raw Periodogram
Parameter	Value
Box-Cox transformation parameter (lambda)	-0.1
Degree of non-seasonal differencing (d)	1
Degree of seasonal differencing (D)	1
Seasonal Period (s)	12
Frequency (Period)	Spectrum
0.0125 (80)	0.000151
0.025 (40)	3.2e-05
0.0375 (26.6667)	0.000386
0.05 (20)	0.000796
0.0625 (16)	2e-05
0.075 (13.3333)	1.7e-05
0.0875 (11.4286)	1.3e-05
0.1 (10)	0.000668
0.1125 (8.8889)	0.000531
0.125 (8)	0.000113
0.1375 (7.2727)	0.000382
0.15 (6.6667)	2.9e-05
0.1625 (6.1538)	0.000204
0.175 (5.7143)	6.8e-05
0.1875 (5.3333)	0.000361
0.2 (5)	0.00086
0.2125 (4.7059)	0.000198
0.225 (4.4444)	0.001446
0.2375 (4.2105)	0.003144
0.25 (4)	0.000276
0.2625 (3.8095)	0.000297
0.275 (3.6364)	0.000674
0.2875 (3.4783)	0.000707
0.3 (3.3333)	0.004146
0.3125 (3.2)	0.001686
0.325 (3.0769)	0.000799
0.3375 (2.963)	0.002227
0.35 (2.8571)	0.008855
0.3625 (2.7586)	0.000636
0.375 (2.6667)	0.000103
0.3875 (2.5806)	0.002778
0.4 (2.5)	0.000416
0.4125 (2.4242)	0.000297
0.425 (2.3529)	0.001188
0.4375 (2.2857)	0.001629
0.45 (2.2222)	0.003063
0.4625 (2.1622)	0.000443
0.475 (2.1053)	0.003262
0.4875 (2.0513)	0.000376
0.5 (2)	2.3e-05

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = -0.1 ; par2 = 1 ; par3 = 1 ; par4 = 12 ;

Parameters (R input):

par1 = -0.1 ; par2 = 1 ; par3 = 1 ; par4 = 12 ;

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)
if (par1 == 0) {
x <- log(x)
} else {
x <- (x ^ par1 - 1) / par1
}
if (par2 > 0) x <- diff(x,lag=1,difference=par2)
if (par3 > 0) x <- diff(x,lag=par4,difference=par3)
bitmap(file='test1.png')
r <- spectrum(x,main='Raw Periodogram')
dev.off()
bitmap(file='test2.png')
cpgram(x,main='Cumulative Periodogram')
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Raw Periodogram',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)',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)',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)',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,'Frequency (Period)',header=TRUE)
a<-table.element(a,'Spectrum',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(r$freq)) {
a<-table.row.start(a)
mylab <- round(r$freq[i],4)
mylab <- paste(mylab,' (',sep='')
mylab <- paste(mylab,round(1/r$freq[i],4),sep='')
mylab <- paste(mylab,')',sep='')
a<-table.element(a,mylab,header=TRUE)
a<-table.element(a,round(r$spec[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code