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Author's title

Author

*Unverified author*

R Software Module

rwasp_spectrum.wasp

Title produced by software

Spectral Analysis

Date of computation

Tue, 09 Dec 2008 11:07:03 -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/09/t12288460540url2pbapai7fsj.htm/, Retrieved Sun, 19 May 2024 12:00:58 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=31653, Retrieved Sun, 19 May 2024 12:00:58 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

197

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 RMPD  [Standard Deviation-Mean Plot] [Identification an...] [2008-12-09 12:57:00] [8ac58ef7b35dc5a117bc162cf16850e9]
F RM D    [Variance Reduction Matrix] [Identification an...] [2008-12-09 13:00:44] [8ac58ef7b35dc5a117bc162cf16850e9]
F RM        [(Partial) Autocorrelation Function] [Identification an...] [2008-12-09 13:03:20] [8ac58ef7b35dc5a117bc162cf16850e9]
F RM          [Spectral Analysis] [Identification an...] [2008-12-09 13:05:46] [8ac58ef7b35dc5a117bc162cf16850e9]
F   P             [Spectral Analysis] [step 2 cp ip] [2008-12-09 18:07:03] [d41d8cd98f00b204e9800998ecf8427e] [Current]

Feedback Forum

2008-12-15 21:57:24 [Jonas Janssens] [reply] 
Goede uitkomst, maar je laat zoals bij de ACF niet zien hoe je aan die uitkomst komt door enkel te kijken naar de Cumulative Periodogram. Hieruit zou je de seizonale trend moeten kunnen afleiden.
2008-12-16 16:41:46 [c00776cbed2786c9c4960950021bd861] [reply] 
Zelfde opmerking als bij de ACF, je kan beter stap voor stap werken. 
Maar de oplossing is hier wel correct.
2008-12-16 19:41:08 [Kevin Vermeiren] [reply] 
Uit het cumulative periodogram blijkt inderdaad dat er geen lange termijn trend en geen seizoenaliteit meer aanwezig. De student had zijn antwoord nog wel wat meer kunnen staven. We kunnen besluiten dat er geen lange termijn trend meer aanwezig is door dat er geen steil begin is van de grafiek. Ook is er geen sprake van een uitgesproken trapsgewijs verloop waardoor er geen sterke seizoenaliteit aanwezig. Verder had de student kunnen vermelden dat er echter nog wel sprake is van een licht trapsgewijs verloop wat duidt op nog een voorspelbaarheid van de tijdreeks. Indien er niets meer te verklaren zou zijn, zou het cumulative periodogram samenvallen met de diagonaal. Bijgevolg kunnen we dus zeggen dat er nog steeds golfbewegingen aanwezig zijn die we kunnen verklaren en patronen die we kunnen voorspellen. Hier is bijkomend onderzoek voor nodig.  
De conclusie van de student is geheel correct. 

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31653&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31653&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31653&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	2 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Raw Periodogram
Parameter	Value
Box-Cox transformation parameter (lambda)	1
Degree of non-seasonal differencing (d)	0
Degree of seasonal differencing (D)	1
Seasonal Period (s)	12
Frequency (Period)	Spectrum
0.02 (50)	3.875074
0.04 (25)	59.711711
0.06 (16.6667)	9.523267
0.08 (12.5)	7.606068
0.1 (10)	2.185706
0.12 (8.3333)	3.137351
0.14 (7.1429)	8.709469
0.16 (6.25)	3.224594
0.18 (5.5556)	18.278306
0.2 (5)	4.005259
0.22 (4.5455)	16.509143
0.24 (4.1667)	4.630949
0.26 (3.8462)	1.115416
0.28 (3.5714)	1.60113
0.3 (3.3333)	4.608198
0.32 (3.125)	0.86306
0.34 (2.9412)	59.632119
0.36 (2.7778)	35.45788
0.38 (2.6316)	3.708664
0.4 (2.5)	24.7205
0.42 (2.381)	24.720328
0.44 (2.2727)	0.398693
0.46 (2.1739)	3.91109
0.48 (2.0833)	12.419976
0.5 (2)	0.502252

\begin{tabular}{lllllllll}
\hline
Raw Periodogram \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) & 1 \tabularnewline
Degree of non-seasonal differencing (d) & 0 \tabularnewline
Degree of seasonal differencing (D) & 1 \tabularnewline
Seasonal Period (s) & 12 \tabularnewline
Frequency (Period) & Spectrum \tabularnewline
0.02 (50) & 3.875074 \tabularnewline
0.04 (25) & 59.711711 \tabularnewline
0.06 (16.6667) & 9.523267 \tabularnewline
0.08 (12.5) & 7.606068 \tabularnewline
0.1 (10) & 2.185706 \tabularnewline
0.12 (8.3333) & 3.137351 \tabularnewline
0.14 (7.1429) & 8.709469 \tabularnewline
0.16 (6.25) & 3.224594 \tabularnewline
0.18 (5.5556) & 18.278306 \tabularnewline
0.2 (5) & 4.005259 \tabularnewline
0.22 (4.5455) & 16.509143 \tabularnewline
0.24 (4.1667) & 4.630949 \tabularnewline
0.26 (3.8462) & 1.115416 \tabularnewline
0.28 (3.5714) & 1.60113 \tabularnewline
0.3 (3.3333) & 4.608198 \tabularnewline
0.32 (3.125) & 0.86306 \tabularnewline
0.34 (2.9412) & 59.632119 \tabularnewline
0.36 (2.7778) & 35.45788 \tabularnewline
0.38 (2.6316) & 3.708664 \tabularnewline
0.4 (2.5) & 24.7205 \tabularnewline
0.42 (2.381) & 24.720328 \tabularnewline
0.44 (2.2727) & 0.398693 \tabularnewline
0.46 (2.1739) & 3.91109 \tabularnewline
0.48 (2.0833) & 12.419976 \tabularnewline
0.5 (2) & 0.502252 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31653&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]1[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d)[/C][C]0[/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]0.02 (50)[/C][C]3.875074[/C][/ROW]
[ROW][C]0.04 (25)[/C][C]59.711711[/C][/ROW]
[ROW][C]0.06 (16.6667)[/C][C]9.523267[/C][/ROW]
[ROW][C]0.08 (12.5)[/C][C]7.606068[/C][/ROW]
[ROW][C]0.1 (10)[/C][C]2.185706[/C][/ROW]
[ROW][C]0.12 (8.3333)[/C][C]3.137351[/C][/ROW]
[ROW][C]0.14 (7.1429)[/C][C]8.709469[/C][/ROW]
[ROW][C]0.16 (6.25)[/C][C]3.224594[/C][/ROW]
[ROW][C]0.18 (5.5556)[/C][C]18.278306[/C][/ROW]
[ROW][C]0.2 (5)[/C][C]4.005259[/C][/ROW]
[ROW][C]0.22 (4.5455)[/C][C]16.509143[/C][/ROW]
[ROW][C]0.24 (4.1667)[/C][C]4.630949[/C][/ROW]
[ROW][C]0.26 (3.8462)[/C][C]1.115416[/C][/ROW]
[ROW][C]0.28 (3.5714)[/C][C]1.60113[/C][/ROW]
[ROW][C]0.3 (3.3333)[/C][C]4.608198[/C][/ROW]
[ROW][C]0.32 (3.125)[/C][C]0.86306[/C][/ROW]
[ROW][C]0.34 (2.9412)[/C][C]59.632119[/C][/ROW]
[ROW][C]0.36 (2.7778)[/C][C]35.45788[/C][/ROW]
[ROW][C]0.38 (2.6316)[/C][C]3.708664[/C][/ROW]
[ROW][C]0.4 (2.5)[/C][C]24.7205[/C][/ROW]
[ROW][C]0.42 (2.381)[/C][C]24.720328[/C][/ROW]
[ROW][C]0.44 (2.2727)[/C][C]0.398693[/C][/ROW]
[ROW][C]0.46 (2.1739)[/C][C]3.91109[/C][/ROW]
[ROW][C]0.48 (2.0833)[/C][C]12.419976[/C][/ROW]
[ROW][C]0.5 (2)[/C][C]0.502252[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31653&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31653&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)	1
Degree of non-seasonal differencing (d)	0
Degree of seasonal differencing (D)	1
Seasonal Period (s)	12
Frequency (Period)	Spectrum
0.02 (50)	3.875074
0.04 (25)	59.711711
0.06 (16.6667)	9.523267
0.08 (12.5)	7.606068
0.1 (10)	2.185706
0.12 (8.3333)	3.137351
0.14 (7.1429)	8.709469
0.16 (6.25)	3.224594
0.18 (5.5556)	18.278306
0.2 (5)	4.005259
0.22 (4.5455)	16.509143
0.24 (4.1667)	4.630949
0.26 (3.8462)	1.115416
0.28 (3.5714)	1.60113
0.3 (3.3333)	4.608198
0.32 (3.125)	0.86306
0.34 (2.9412)	59.632119
0.36 (2.7778)	35.45788
0.38 (2.6316)	3.708664
0.4 (2.5)	24.7205
0.42 (2.381)	24.720328
0.44 (2.2727)	0.398693
0.46 (2.1739)	3.91109
0.48 (2.0833)	12.419976
0.5 (2)	0.502252

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = 0 ; par3 = 1 ; par4 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = 0 ; 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