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Author

*Unverified author*

R Software Module

rwasp_spectrum.wasp

Title produced by software

Spectral Analysis

Date of computation

Tue, 09 Dec 2008 07:55:22 -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/t1228834559xyikprpca6oz6g0.htm/, Retrieved Fri, 17 May 2024 03:05:14 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=31490, Retrieved Fri, 17 May 2024 03:05:14 +0000

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Original text written by user:

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No (this computation is public)

User-defined keywords

Estimated Impact

169

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

F       [Spectral Analysis] [step2] [2008-12-09 14:55:22] [a413cf7744efd6bb212437a3916e2f23] [Current]

Feedback Forum

2008-12-14 13:25:39 [Gert-Jan Geudens] [reply] 
Hier lijkt het ons het best om seizonaal te differentiëren. Dit werd reeds bevestigd door de VRM. Daarom kiezen we voor parameters d=0, D=1.
2008-12-15 14:28:05 [Jonas Scheltjens] [reply] 
Deze Step werd erg beperkt besproken, daarom verwijs ik (aangezien het niet de taak is van de persoon die de assessments doet om deze taak voor de student te maken) voor verdere informatie dan ook voor de algemene en volledige uitleg voor deze Step naar Step 2 voor de unemployment data, dewelke ik zeer uitgebreid heb besproken en waar alle informatie in staat om deze vraag correct op te lossen. 
In het cumulative Periodogram dat de student heeft weergegeven heeft de student de waarden D=0 en d=0 gebruikt. Er is inderdaad seizoenaliteit aanwezig, wat we overduidelijk kunnen zien aan de trap-beweging van de lijn. De waarde van de getrimde variantie is inderdaad het laagste bij d=0 en D=1, net zoals de student zegt, misschien ook beter dat de student vermeldt dat hij dit vond in de Variance Reduction Matrix, aangezien het anders lijkt dat hij dit uit het cumulative Periodogram kan afleiden, wat mij vreemd zou lijken. Aangezien de getrimde variantie van de methode waar d=1 en D=1 ook zeer dicht in de buurt liggen zou dit ook misschien berekend kunnen worden om misschien betere resultaten te bekomen. 

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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	3 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31490&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31490&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31490&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	3 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

Raw Periodogram
Parameter	Value
Box-Cox transformation parameter (lambda)	1
Degree of non-seasonal differencing (d)	0
Degree of seasonal differencing (D)	0
Seasonal Period (s)	12
Frequency (Period)	Spectrum
0.0167 (60)	205001.702952
0.0333 (30)	242341.608623
0.05 (20)	122085.211421
0.0667 (15)	21332.197938
0.0833 (12)	492617.530789
0.1 (10)	29105.517084
0.1167 (8.5714)	17059.964293
0.1333 (7.5)	11892.49159
0.15 (6.6667)	71128.771029
0.1667 (6)	373751.484152
0.1833 (5.4545)	43848.665203
0.2 (5)	25464.207783
0.2167 (4.6154)	39623.430603
0.2333 (4.2857)	29292.691618
0.25 (4)	420365.131742
0.2667 (3.75)	24637.857379
0.2833 (3.5294)	64646.069275
0.3 (3.3333)	54682.956765
0.3167 (3.1579)	87231.766624
0.3333 (3)	342019.181151
0.35 (2.8571)	138259.035993
0.3667 (2.7273)	3493.474553
0.3833 (2.6087)	12560.386604
0.4 (2.5)	7488.505222
0.4167 (2.4)	47324.081561
0.4333 (2.3077)	944.358135
0.45 (2.2222)	5339.83941
0.4667 (2.1429)	4938.127581
0.4833 (2.069)	74005.875158
0.5 (2)	199191.471822

\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) & 0 \tabularnewline
Seasonal Period (s) & 12 \tabularnewline
Frequency (Period) & Spectrum \tabularnewline
0.0167 (60) & 205001.702952 \tabularnewline
0.0333 (30) & 242341.608623 \tabularnewline
0.05 (20) & 122085.211421 \tabularnewline
0.0667 (15) & 21332.197938 \tabularnewline
0.0833 (12) & 492617.530789 \tabularnewline
0.1 (10) & 29105.517084 \tabularnewline
0.1167 (8.5714) & 17059.964293 \tabularnewline
0.1333 (7.5) & 11892.49159 \tabularnewline
0.15 (6.6667) & 71128.771029 \tabularnewline
0.1667 (6) & 373751.484152 \tabularnewline
0.1833 (5.4545) & 43848.665203 \tabularnewline
0.2 (5) & 25464.207783 \tabularnewline
0.2167 (4.6154) & 39623.430603 \tabularnewline
0.2333 (4.2857) & 29292.691618 \tabularnewline
0.25 (4) & 420365.131742 \tabularnewline
0.2667 (3.75) & 24637.857379 \tabularnewline
0.2833 (3.5294) & 64646.069275 \tabularnewline
0.3 (3.3333) & 54682.956765 \tabularnewline
0.3167 (3.1579) & 87231.766624 \tabularnewline
0.3333 (3) & 342019.181151 \tabularnewline
0.35 (2.8571) & 138259.035993 \tabularnewline
0.3667 (2.7273) & 3493.474553 \tabularnewline
0.3833 (2.6087) & 12560.386604 \tabularnewline
0.4 (2.5) & 7488.505222 \tabularnewline
0.4167 (2.4) & 47324.081561 \tabularnewline
0.4333 (2.3077) & 944.358135 \tabularnewline
0.45 (2.2222) & 5339.83941 \tabularnewline
0.4667 (2.1429) & 4938.127581 \tabularnewline
0.4833 (2.069) & 74005.875158 \tabularnewline
0.5 (2) & 199191.471822 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=31490&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]0[/C][/ROW]
[ROW][C]Seasonal Period (s)[/C][C]12[/C][/ROW]
[ROW][C]Frequency (Period)[/C][C]Spectrum[/C][/ROW]
[ROW][C]0.0167 (60)[/C][C]205001.702952[/C][/ROW]
[ROW][C]0.0333 (30)[/C][C]242341.608623[/C][/ROW]
[ROW][C]0.05 (20)[/C][C]122085.211421[/C][/ROW]
[ROW][C]0.0667 (15)[/C][C]21332.197938[/C][/ROW]
[ROW][C]0.0833 (12)[/C][C]492617.530789[/C][/ROW]
[ROW][C]0.1 (10)[/C][C]29105.517084[/C][/ROW]
[ROW][C]0.1167 (8.5714)[/C][C]17059.964293[/C][/ROW]
[ROW][C]0.1333 (7.5)[/C][C]11892.49159[/C][/ROW]
[ROW][C]0.15 (6.6667)[/C][C]71128.771029[/C][/ROW]
[ROW][C]0.1667 (6)[/C][C]373751.484152[/C][/ROW]
[ROW][C]0.1833 (5.4545)[/C][C]43848.665203[/C][/ROW]
[ROW][C]0.2 (5)[/C][C]25464.207783[/C][/ROW]
[ROW][C]0.2167 (4.6154)[/C][C]39623.430603[/C][/ROW]
[ROW][C]0.2333 (4.2857)[/C][C]29292.691618[/C][/ROW]
[ROW][C]0.25 (4)[/C][C]420365.131742[/C][/ROW]
[ROW][C]0.2667 (3.75)[/C][C]24637.857379[/C][/ROW]
[ROW][C]0.2833 (3.5294)[/C][C]64646.069275[/C][/ROW]
[ROW][C]0.3 (3.3333)[/C][C]54682.956765[/C][/ROW]
[ROW][C]0.3167 (3.1579)[/C][C]87231.766624[/C][/ROW]
[ROW][C]0.3333 (3)[/C][C]342019.181151[/C][/ROW]
[ROW][C]0.35 (2.8571)[/C][C]138259.035993[/C][/ROW]
[ROW][C]0.3667 (2.7273)[/C][C]3493.474553[/C][/ROW]
[ROW][C]0.3833 (2.6087)[/C][C]12560.386604[/C][/ROW]
[ROW][C]0.4 (2.5)[/C][C]7488.505222[/C][/ROW]
[ROW][C]0.4167 (2.4)[/C][C]47324.081561[/C][/ROW]
[ROW][C]0.4333 (2.3077)[/C][C]944.358135[/C][/ROW]
[ROW][C]0.45 (2.2222)[/C][C]5339.83941[/C][/ROW]
[ROW][C]0.4667 (2.1429)[/C][C]4938.127581[/C][/ROW]
[ROW][C]0.4833 (2.069)[/C][C]74005.875158[/C][/ROW]
[ROW][C]0.5 (2)[/C][C]199191.471822[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=31490&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=31490&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)	0
Seasonal Period (s)	12
Frequency (Period)	Spectrum
0.0167 (60)	205001.702952
0.0333 (30)	242341.608623
0.05 (20)	122085.211421
0.0667 (15)	21332.197938
0.0833 (12)	492617.530789
0.1 (10)	29105.517084
0.1167 (8.5714)	17059.964293
0.1333 (7.5)	11892.49159
0.15 (6.6667)	71128.771029
0.1667 (6)	373751.484152
0.1833 (5.4545)	43848.665203
0.2 (5)	25464.207783
0.2167 (4.6154)	39623.430603
0.2333 (4.2857)	29292.691618
0.25 (4)	420365.131742
0.2667 (3.75)	24637.857379
0.2833 (3.5294)	64646.069275
0.3 (3.3333)	54682.956765
0.3167 (3.1579)	87231.766624
0.3333 (3)	342019.181151
0.35 (2.8571)	138259.035993
0.3667 (2.7273)	3493.474553
0.3833 (2.6087)	12560.386604
0.4 (2.5)	7488.505222
0.4167 (2.4)	47324.081561
0.4333 (2.3077)	944.358135
0.45 (2.2222)	5339.83941
0.4667 (2.1429)	4938.127581
0.4833 (2.069)	74005.875158
0.5 (2)	199191.471822

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

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

Parameters (R input):

par1 = 1 ; par2 = 0 ; par3 = 0 ; 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