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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_spectrum.wasp
Title produced by softwareSpectral Analysis
Date of computationSun, 30 Nov 2008 03:43:17 -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/Nov/30/t1228041998antx54c8ag7xzew.htm/, Retrieved Sun, 19 May 2024 09:38:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=26423, Retrieved Sun, 19 May 2024 09:38:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact201

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Spectral Analysis] [opdracht hfdst 12...] [2008-11-30 10:43:17] [e1dd70d3b1099218056e8ae5041dcc2f] [Current]
Feedback Forum
2008-12-04 13:17:48 [Steven Vercammen] [reply] 
Dit klopt volledig. Op de grafieken kan men zien dat er geen sprake is van seizonaliteit of een langetermijntrend.
2008-12-06 12:32:43 [Maarten Van Gucht] [reply] 
In deze vraag moet je naar de optimale lambda gaan zoeken of bepalen om de tijdreeks stationair te krijgen. de lambda gaan zoeken doen we door de standard deviation mean plot. dit heeft de student niet gedaan, de student heeft met de spectraalanalyse gewerkt.

https://automated.biganalytics.eu/rwasp_smp.wasp?parent=t1228041998antx54c8ag7xzew#output

dit is de nieuwe berekening van de student zijn gegevens zoals het wel zou moeten,
je kan in de grafieken zien dat de spreiding zeer ruim is. in de tabel gaan we zoeken naar de kleinste variantie.
we kunnen tenslotte ook gebruik maken van de reductiematrix waar we de kleinste variantie kunnen zoeken en zo de kleinde en grote d kunnen zoeken.
2008-12-08 15:14:10 [Sam De Cuyper] [reply] 
Het is inderdaad de bedoeling te gaan zoeken naar de meest optimale waarde voor lambda en om zo de tijdreeks zo stationair mogelijk te maken. Dit doe je door gebruik te maken van de SMP en dat is niet gebeurt.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
90,7
94,3
104,6
111,1
110,8
107,2
99
99
91
96,2
96,9
96,2
100,1
99
115,4
106,9
107,1
99,3
99,2
108,3
105,6
99,5
107,4
93,1
88,1
110,7
113,1
99,6
93,6
98,6
99,6
114,3
107,8
101,2
112,5
100,5
93,9
116,2
112
106,4
95,7
96
95,8
103
102,2
98,4
111,4
86,6
91,3
107,9
101,8
104,4
93,4
100,1
98,5
112,9
101,4
107,1
110,8
90,3
95,5
111,4
113
107,5
95,9
106,3
105,2
117,2
106,9
108,2
113
97,2
99,9
108,1
118,1
109,1
93,3
112,1
111,8
112,5
116,3
110,3
117,1
103,4
96,2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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=26423&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=26423&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26423&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Raw Periodogram
ParameterValue
Box-Cox transformation parameter (lambda)1
Degree of non-seasonal differencing (d)0
Degree of seasonal differencing (D)0
Seasonal Period (s)1
Frequency (Period)Spectrum
0.0111 (90)61.743144
0.0222 (45)81.155949
0.0333 (30)15.2571
0.0444 (22.5)41.346619
0.0556 (18)50.489142
0.0667 (15)51.449785
0.0778 (12.8571)66.275495
0.0889 (11.25)29.501312
0.1 (10)34.226704
0.1111 (9)7.47333
0.1222 (8.1818)22.818714
0.1333 (7.5)1.004513
0.1444 (6.9231)29.308993
0.1556 (6.4286)49.184729
0.1667 (6)499.78111
0.1778 (5.625)103.008415
0.1889 (5.2941)90.074655
0.2 (5)10.632184
0.2111 (4.7368)3.947515
0.2222 (4.5)12.836733
0.2333 (4.2857)18.120841
0.2444 (4.0909)177.147189
0.2556 (3.913)264.842448
0.2667 (3.75)4.382058
0.2778 (3.6)7.957605
0.2889 (3.4615)11.434112
0.3 (3.3333)7.24869
0.3111 (3.2143)40.392975
0.3222 (3.1034)31.904455
0.3333 (3)168.795449
0.3444 (2.9032)126.364175
0.3556 (2.8125)12.07582
0.3667 (2.7273)8.475957
0.3778 (2.6471)10.923899
0.3889 (2.5714)1.975761
0.4 (2.5)16.925754
0.4111 (2.4324)110.232404
0.4222 (2.3684)76.853661
0.4333 (2.3077)54.280289
0.4444 (2.25)4.616217
0.4556 (2.1951)12.305391
0.4667 (2.1429)17.775757
0.4778 (2.093)0.2318
0.4889 (2.0455)18.095336
0.5 (2)16.645707

\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) & 1 \tabularnewline
Frequency (Period) & Spectrum \tabularnewline
0.0111 (90) & 61.743144 \tabularnewline
0.0222 (45) & 81.155949 \tabularnewline
0.0333 (30) & 15.2571 \tabularnewline
0.0444 (22.5) & 41.346619 \tabularnewline
0.0556 (18) & 50.489142 \tabularnewline
0.0667 (15) & 51.449785 \tabularnewline
0.0778 (12.8571) & 66.275495 \tabularnewline
0.0889 (11.25) & 29.501312 \tabularnewline
0.1 (10) & 34.226704 \tabularnewline
0.1111 (9) & 7.47333 \tabularnewline
0.1222 (8.1818) & 22.818714 \tabularnewline
0.1333 (7.5) & 1.004513 \tabularnewline
0.1444 (6.9231) & 29.308993 \tabularnewline
0.1556 (6.4286) & 49.184729 \tabularnewline
0.1667 (6) & 499.78111 \tabularnewline
0.1778 (5.625) & 103.008415 \tabularnewline
0.1889 (5.2941) & 90.074655 \tabularnewline
0.2 (5) & 10.632184 \tabularnewline
0.2111 (4.7368) & 3.947515 \tabularnewline
0.2222 (4.5) & 12.836733 \tabularnewline
0.2333 (4.2857) & 18.120841 \tabularnewline
0.2444 (4.0909) & 177.147189 \tabularnewline
0.2556 (3.913) & 264.842448 \tabularnewline
0.2667 (3.75) & 4.382058 \tabularnewline
0.2778 (3.6) & 7.957605 \tabularnewline
0.2889 (3.4615) & 11.434112 \tabularnewline
0.3 (3.3333) & 7.24869 \tabularnewline
0.3111 (3.2143) & 40.392975 \tabularnewline
0.3222 (3.1034) & 31.904455 \tabularnewline
0.3333 (3) & 168.795449 \tabularnewline
0.3444 (2.9032) & 126.364175 \tabularnewline
0.3556 (2.8125) & 12.07582 \tabularnewline
0.3667 (2.7273) & 8.475957 \tabularnewline
0.3778 (2.6471) & 10.923899 \tabularnewline
0.3889 (2.5714) & 1.975761 \tabularnewline
0.4 (2.5) & 16.925754 \tabularnewline
0.4111 (2.4324) & 110.232404 \tabularnewline
0.4222 (2.3684) & 76.853661 \tabularnewline
0.4333 (2.3077) & 54.280289 \tabularnewline
0.4444 (2.25) & 4.616217 \tabularnewline
0.4556 (2.1951) & 12.305391 \tabularnewline
0.4667 (2.1429) & 17.775757 \tabularnewline
0.4778 (2.093) & 0.2318 \tabularnewline
0.4889 (2.0455) & 18.095336 \tabularnewline
0.5 (2) & 16.645707 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=26423&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]1[/C][/ROW]
[ROW][C]Frequency (Period)[/C][C]Spectrum[/C][/ROW]
[ROW][C]0.0111 (90)[/C][C]61.743144[/C][/ROW]
[ROW][C]0.0222 (45)[/C][C]81.155949[/C][/ROW]
[ROW][C]0.0333 (30)[/C][C]15.2571[/C][/ROW]
[ROW][C]0.0444 (22.5)[/C][C]41.346619[/C][/ROW]
[ROW][C]0.0556 (18)[/C][C]50.489142[/C][/ROW]
[ROW][C]0.0667 (15)[/C][C]51.449785[/C][/ROW]
[ROW][C]0.0778 (12.8571)[/C][C]66.275495[/C][/ROW]
[ROW][C]0.0889 (11.25)[/C][C]29.501312[/C][/ROW]
[ROW][C]0.1 (10)[/C][C]34.226704[/C][/ROW]
[ROW][C]0.1111 (9)[/C][C]7.47333[/C][/ROW]
[ROW][C]0.1222 (8.1818)[/C][C]22.818714[/C][/ROW]
[ROW][C]0.1333 (7.5)[/C][C]1.004513[/C][/ROW]
[ROW][C]0.1444 (6.9231)[/C][C]29.308993[/C][/ROW]
[ROW][C]0.1556 (6.4286)[/C][C]49.184729[/C][/ROW]
[ROW][C]0.1667 (6)[/C][C]499.78111[/C][/ROW]
[ROW][C]0.1778 (5.625)[/C][C]103.008415[/C][/ROW]
[ROW][C]0.1889 (5.2941)[/C][C]90.074655[/C][/ROW]
[ROW][C]0.2 (5)[/C][C]10.632184[/C][/ROW]
[ROW][C]0.2111 (4.7368)[/C][C]3.947515[/C][/ROW]
[ROW][C]0.2222 (4.5)[/C][C]12.836733[/C][/ROW]
[ROW][C]0.2333 (4.2857)[/C][C]18.120841[/C][/ROW]
[ROW][C]0.2444 (4.0909)[/C][C]177.147189[/C][/ROW]
[ROW][C]0.2556 (3.913)[/C][C]264.842448[/C][/ROW]
[ROW][C]0.2667 (3.75)[/C][C]4.382058[/C][/ROW]
[ROW][C]0.2778 (3.6)[/C][C]7.957605[/C][/ROW]
[ROW][C]0.2889 (3.4615)[/C][C]11.434112[/C][/ROW]
[ROW][C]0.3 (3.3333)[/C][C]7.24869[/C][/ROW]
[ROW][C]0.3111 (3.2143)[/C][C]40.392975[/C][/ROW]
[ROW][C]0.3222 (3.1034)[/C][C]31.904455[/C][/ROW]
[ROW][C]0.3333 (3)[/C][C]168.795449[/C][/ROW]
[ROW][C]0.3444 (2.9032)[/C][C]126.364175[/C][/ROW]
[ROW][C]0.3556 (2.8125)[/C][C]12.07582[/C][/ROW]
[ROW][C]0.3667 (2.7273)[/C][C]8.475957[/C][/ROW]
[ROW][C]0.3778 (2.6471)[/C][C]10.923899[/C][/ROW]
[ROW][C]0.3889 (2.5714)[/C][C]1.975761[/C][/ROW]
[ROW][C]0.4 (2.5)[/C][C]16.925754[/C][/ROW]
[ROW][C]0.4111 (2.4324)[/C][C]110.232404[/C][/ROW]
[ROW][C]0.4222 (2.3684)[/C][C]76.853661[/C][/ROW]
[ROW][C]0.4333 (2.3077)[/C][C]54.280289[/C][/ROW]
[ROW][C]0.4444 (2.25)[/C][C]4.616217[/C][/ROW]
[ROW][C]0.4556 (2.1951)[/C][C]12.305391[/C][/ROW]
[ROW][C]0.4667 (2.1429)[/C][C]17.775757[/C][/ROW]
[ROW][C]0.4778 (2.093)[/C][C]0.2318[/C][/ROW]
[ROW][C]0.4889 (2.0455)[/C][C]18.095336[/C][/ROW]
[ROW][C]0.5 (2)[/C][C]16.645707[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=26423&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=26423&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:

Raw Periodogram
ParameterValue
Box-Cox transformation parameter (lambda)1
Degree of non-seasonal differencing (d)0
Degree of seasonal differencing (D)0
Seasonal Period (s)1
Frequency (Period)Spectrum
0.0111 (90)61.743144
0.0222 (45)81.155949
0.0333 (30)15.2571
0.0444 (22.5)41.346619
0.0556 (18)50.489142
0.0667 (15)51.449785
0.0778 (12.8571)66.275495
0.0889 (11.25)29.501312
0.1 (10)34.226704
0.1111 (9)7.47333
0.1222 (8.1818)22.818714
0.1333 (7.5)1.004513
0.1444 (6.9231)29.308993
0.1556 (6.4286)49.184729
0.1667 (6)499.78111
0.1778 (5.625)103.008415
0.1889 (5.2941)90.074655
0.2 (5)10.632184
0.2111 (4.7368)3.947515
0.2222 (4.5)12.836733
0.2333 (4.2857)18.120841
0.2444 (4.0909)177.147189
0.2556 (3.913)264.842448
0.2667 (3.75)4.382058
0.2778 (3.6)7.957605
0.2889 (3.4615)11.434112
0.3 (3.3333)7.24869
0.3111 (3.2143)40.392975
0.3222 (3.1034)31.904455
0.3333 (3)168.795449
0.3444 (2.9032)126.364175
0.3556 (2.8125)12.07582
0.3667 (2.7273)8.475957
0.3778 (2.6471)10.923899
0.3889 (2.5714)1.975761
0.4 (2.5)16.925754
0.4111 (2.4324)110.232404
0.4222 (2.3684)76.853661
0.4333 (2.3077)54.280289
0.4444 (2.25)4.616217
0.4556 (2.1951)12.305391
0.4667 (2.1429)17.775757
0.4778 (2.093)0.2318
0.4889 (2.0455)18.095336
0.5 (2)16.645707


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 1 ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 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)
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')