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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 10:36:18 -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/t1228844218yynzgxde35usd86.htm/, Retrieved Sun, 19 May 2024 10:08:42 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=31618, Retrieved Sun, 19 May 2024 10:08:42 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

131

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

F       [Spectral Analysis] [Toon Wouters] [2008-12-09 17:36:18] [14e94996a4178d938cb12bed20a4a373] [Current]

Feedback Forum

2008-12-15 18:51:16 [Steven Vercammen] [reply] 
Dit is correct. Spectraalanalyse wordt gebruikt om willekeurige tijdreeksen te ontbinden in regelmatige golfbewegingen. Een lange termijntrend betekent dat er sprake is van een lange periode en een trage frequentie. Het spectrum geeft de intensiteit van een golfperiode aan, maw hoe belangrijk is deze golfbeweging. We zien hier dat de waarden allemaal zeer laag zijn. Wanneer er nog een lange termijn trend in de reeks aanwezig zou zijn dan zouden we zeer hoge waarde zien bij de lange periodes, dit is echter niet het geval. Er zijn ook geen hoge waarden te vinden bij periodes 6,12, 24,… wat er op wijst dat er ook geen seizonaliteit aanwezig is in de tijdreeks. 
Op het raw en cumulative periodogram zien we ook geen aanwijzingen meer dat de tijdreeks niet stationair zou zijn. Er zijn maw geen indicaties voor een lange termijn trend of seizonaliteit. 
 

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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=31618&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=31618&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31618&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
0.0139 (72)	420.978254
0.0278 (36)	75.454533
0.0417 (24)	169.173479
0.0556 (18)	26.381099
0.0694 (14.4)	8.394154
0.0833 (12)	5.325382
0.0972 (10.2857)	29.730698
0.1111 (9)	11.429221
0.125 (8)	110.328652
0.1389 (7.2)	31.847757
0.1528 (6.5455)	0.372536
0.1667 (6)	15.780973
0.1806 (5.5385)	1.090437
0.1944 (5.1429)	39.786665
0.2083 (4.8)	47.132746
0.2222 (4.5)	45.263777
0.2361 (4.2353)	50.712896
0.25 (4)	7.364523
0.2639 (3.7895)	57.43044
0.2778 (3.6)	10.271468
0.2917 (3.4286)	37.358939
0.3056 (3.2727)	36.5454
0.3194 (3.1304)	11.717553
0.3333 (3)	32.909787
0.3472 (2.88)	136.031926
0.3611 (2.7692)	55.753292
0.375 (2.6667)	24.056349
0.3889 (2.5714)	46.091732
0.4028 (2.4828)	59.348735
0.4167 (2.4)	14.081638
0.4306 (2.3226)	70.834586
0.4444 (2.25)	44.078405
0.4583 (2.1818)	155.465307
0.4722 (2.1176)	23.452672
0.4861 (2.0571)	10.877267
0.5 (2)	1.02736

\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
0.0139 (72) & 420.978254 \tabularnewline
0.0278 (36) & 75.454533 \tabularnewline
0.0417 (24) & 169.173479 \tabularnewline
0.0556 (18) & 26.381099 \tabularnewline
0.0694 (14.4) & 8.394154 \tabularnewline
0.0833 (12) & 5.325382 \tabularnewline
0.0972 (10.2857) & 29.730698 \tabularnewline
0.1111 (9) & 11.429221 \tabularnewline
0.125 (8) & 110.328652 \tabularnewline
0.1389 (7.2) & 31.847757 \tabularnewline
0.1528 (6.5455) & 0.372536 \tabularnewline
0.1667 (6) & 15.780973 \tabularnewline
0.1806 (5.5385) & 1.090437 \tabularnewline
0.1944 (5.1429) & 39.786665 \tabularnewline
0.2083 (4.8) & 47.132746 \tabularnewline
0.2222 (4.5) & 45.263777 \tabularnewline
0.2361 (4.2353) & 50.712896 \tabularnewline
0.25 (4) & 7.364523 \tabularnewline
0.2639 (3.7895) & 57.43044 \tabularnewline
0.2778 (3.6) & 10.271468 \tabularnewline
0.2917 (3.4286) & 37.358939 \tabularnewline
0.3056 (3.2727) & 36.5454 \tabularnewline
0.3194 (3.1304) & 11.717553 \tabularnewline
0.3333 (3) & 32.909787 \tabularnewline
0.3472 (2.88) & 136.031926 \tabularnewline
0.3611 (2.7692) & 55.753292 \tabularnewline
0.375 (2.6667) & 24.056349 \tabularnewline
0.3889 (2.5714) & 46.091732 \tabularnewline
0.4028 (2.4828) & 59.348735 \tabularnewline
0.4167 (2.4) & 14.081638 \tabularnewline
0.4306 (2.3226) & 70.834586 \tabularnewline
0.4444 (2.25) & 44.078405 \tabularnewline
0.4583 (2.1818) & 155.465307 \tabularnewline
0.4722 (2.1176) & 23.452672 \tabularnewline
0.4861 (2.0571) & 10.877267 \tabularnewline
0.5 (2) & 1.02736 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31618&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]0.0139 (72)[/C][C]420.978254[/C][/ROW]
[ROW][C]0.0278 (36)[/C][C]75.454533[/C][/ROW]
[ROW][C]0.0417 (24)[/C][C]169.173479[/C][/ROW]
[ROW][C]0.0556 (18)[/C][C]26.381099[/C][/ROW]
[ROW][C]0.0694 (14.4)[/C][C]8.394154[/C][/ROW]
[ROW][C]0.0833 (12)[/C][C]5.325382[/C][/ROW]
[ROW][C]0.0972 (10.2857)[/C][C]29.730698[/C][/ROW]
[ROW][C]0.1111 (9)[/C][C]11.429221[/C][/ROW]
[ROW][C]0.125 (8)[/C][C]110.328652[/C][/ROW]
[ROW][C]0.1389 (7.2)[/C][C]31.847757[/C][/ROW]
[ROW][C]0.1528 (6.5455)[/C][C]0.372536[/C][/ROW]
[ROW][C]0.1667 (6)[/C][C]15.780973[/C][/ROW]
[ROW][C]0.1806 (5.5385)[/C][C]1.090437[/C][/ROW]
[ROW][C]0.1944 (5.1429)[/C][C]39.786665[/C][/ROW]
[ROW][C]0.2083 (4.8)[/C][C]47.132746[/C][/ROW]
[ROW][C]0.2222 (4.5)[/C][C]45.263777[/C][/ROW]
[ROW][C]0.2361 (4.2353)[/C][C]50.712896[/C][/ROW]
[ROW][C]0.25 (4)[/C][C]7.364523[/C][/ROW]
[ROW][C]0.2639 (3.7895)[/C][C]57.43044[/C][/ROW]
[ROW][C]0.2778 (3.6)[/C][C]10.271468[/C][/ROW]
[ROW][C]0.2917 (3.4286)[/C][C]37.358939[/C][/ROW]
[ROW][C]0.3056 (3.2727)[/C][C]36.5454[/C][/ROW]
[ROW][C]0.3194 (3.1304)[/C][C]11.717553[/C][/ROW]
[ROW][C]0.3333 (3)[/C][C]32.909787[/C][/ROW]
[ROW][C]0.3472 (2.88)[/C][C]136.031926[/C][/ROW]
[ROW][C]0.3611 (2.7692)[/C][C]55.753292[/C][/ROW]
[ROW][C]0.375 (2.6667)[/C][C]24.056349[/C][/ROW]
[ROW][C]0.3889 (2.5714)[/C][C]46.091732[/C][/ROW]
[ROW][C]0.4028 (2.4828)[/C][C]59.348735[/C][/ROW]
[ROW][C]0.4167 (2.4)[/C][C]14.081638[/C][/ROW]
[ROW][C]0.4306 (2.3226)[/C][C]70.834586[/C][/ROW]
[ROW][C]0.4444 (2.25)[/C][C]44.078405[/C][/ROW]
[ROW][C]0.4583 (2.1818)[/C][C]155.465307[/C][/ROW]
[ROW][C]0.4722 (2.1176)[/C][C]23.452672[/C][/ROW]
[ROW][C]0.4861 (2.0571)[/C][C]10.877267[/C][/ROW]
[ROW][C]0.5 (2)[/C][C]1.02736[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31618&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31618&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
0.0139 (72)	420.978254
0.0278 (36)	75.454533
0.0417 (24)	169.173479
0.0556 (18)	26.381099
0.0694 (14.4)	8.394154
0.0833 (12)	5.325382
0.0972 (10.2857)	29.730698
0.1111 (9)	11.429221
0.125 (8)	110.328652
0.1389 (7.2)	31.847757
0.1528 (6.5455)	0.372536
0.1667 (6)	15.780973
0.1806 (5.5385)	1.090437
0.1944 (5.1429)	39.786665
0.2083 (4.8)	47.132746
0.2222 (4.5)	45.263777
0.2361 (4.2353)	50.712896
0.25 (4)	7.364523
0.2639 (3.7895)	57.43044
0.2778 (3.6)	10.271468
0.2917 (3.4286)	37.358939
0.3056 (3.2727)	36.5454
0.3194 (3.1304)	11.717553
0.3333 (3)	32.909787
0.3472 (2.88)	136.031926
0.3611 (2.7692)	55.753292
0.375 (2.6667)	24.056349
0.3889 (2.5714)	46.091732
0.4028 (2.4828)	59.348735
0.4167 (2.4)	14.081638
0.4306 (2.3226)	70.834586
0.4444 (2.25)	44.078405
0.4583 (2.1818)	155.465307
0.4722 (2.1176)	23.452672
0.4861 (2.0571)	10.877267
0.5 (2)	1.02736

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