FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_spectrum.wasp
Title produced by softwareSpectral Analysis
Date of computationMon, 08 Dec 2008 11:54:14 -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/Dec/08/t1228762506sc2m4oxipi3wjlv.htm/, Retrieved Thu, 16 May 2024 11:25:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=30705, Retrieved Thu, 16 May 2024 11:25:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact223

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Spectral Analysis] [Unemployment - St...] [2008-12-08 17:28:52] [57850c80fd59ccfb28f882be994e814e]
-   P   [Spectral Analysis] [Unemployment - St...] [2008-12-08 18:03:50] [57850c80fd59ccfb28f882be994e814e]
F           [Spectral Analysis] [STEP 2 2 ] [2008-12-08 18:54:14] [e11d930c9e2984715c66c796cf63ef19] [Current]
Feedback Forum
2008-12-11 13:21:20 [72e979bcc364082694890d2eccc1a66f] [reply] 
Ook hier kan je inderdaad duidelijk een trend en seizoenaliteit waarnemen.
In het cumulatief periodogram zie je eerst een sterke stijging wat wijst op een trend en dan de trapbeweging wat wijst op seizoenaliteit. Dit moeten we dus verbeteren door d en D gelijk te stellen aan 1, dus telkens eenmaal te differentiëren.
2008-12-13 20:49:46 [Li Tang Hu] [reply] 
ook hier is uiteraard de lambdawaarde niet ingevuld waardoor je een andere grafiek krijgt...maar we kunnen in beide grafieken duidlijk zien dat er een lange termijn trend is (dalend verloop van de raw periodogram en steile stijging aan de linkerkant van de cumulatieve periodogram) en seizoenaliteit (pieken in de raw periodogram en trappatroon in de cumulatieve)
we gaan dus zeker seizoenaal en trendmatig moeten differentieeren.
2008-12-14 10:25:31 [Stéphanie Claes] [reply] 
Vanuit deze analyse kunnen we afleiden dat er een dalende lange termijn trend is.
Dit kunnen we besluiten doordat we zien dat de lage frequentie golven dominant zijn. 60% van de grafiek wordt bepaald door deze lange termijn trend. We zien ook seizoenale pieken.
We kunnen hierbij opnieuw besluiten dat we d en D instellen op 1.
2008-12-14 10:45:20 [94a54c888ac7f7d6874c3108eb0e1808] [reply] 
Ook hier zien we zeer duidelijk de lange termijn trend en de seizonale trend. We zien ook dat 60% van de tijdsreeks verklaard kan worden.
2008-12-15 12:50:25 [Toon Wouters] [reply] 
Hier kun je pas vaststellen dat er een lange termijn trend aanwezig is door de snelle stijging bij lage frequency en ook is er seinoenaliteit aanwezig door het trapgewijs verloop van curve.
2008-12-16 13:29:42 [Roel Geudens] [reply] 
er is een dalende trend en er zijn seizonale bewegingen. Hierdoor D=1 en d=1.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
569323
579714
577992
565464
547344
554788
562325
560854
555332
543599
536662
542722
593530
610763
612613
611324
594167
595454
590865
589379
584428
573100
567456
569028
620735
628884
628232
612117
595404
597141
593408
590072
579799
574205
572775
572942
619567
625809
619916
587625
565742
557274
560576
548854
531673
525919
511038
498662
555362
564591
541657
527070
509846
514258
516922
507561
492622
490243
469357
477580
528379

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30705&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30705&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30705&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 time1 seconds
R Server'George Udny Yule' @ 72.249.76.132






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)12
Frequency (Period)Spectrum
0.0156 (64)15507186445.0789
0.0312 (32)1619594471.94059
0.0469 (21.3333)2072739276.84291
0.0625 (16)600449197.62021
0.0781 (12.8)5789163969.671
0.0938 (10.6667)2508989347.1938
0.1094 (9.1429)91076399.943969
0.125 (8)7791784.038587
0.1406 (7.1111)227892152.625167
0.1562 (6.4)780049151.486941
0.1719 (5.8182)3068351465.47330
0.1875 (5.3333)69526396.302195
0.2031 (4.9231)37038412.399033
0.2188 (4.5714)61192225.77603
0.2344 (4.2667)125081628.090235
0.25 (4)248532172.763281
0.2656 (3.7647)10526190.097361
0.2812 (3.5556)68404068.393974
0.2969 (3.3684)22169091.498819
0.3125 (3.2)5856448.63212
0.3281 (3.0476)251092526.994107
0.3438 (2.9091)46871535.919175
0.3594 (2.7826)51165238.257596
0.375 (2.6667)13096926.133035
0.3906 (2.56)11796327.105729
0.4062 (2.4615)47639258.749722
0.4219 (2.3704)180758476.188013
0.4375 (2.2857)9880771.971243
0.4531 (2.2069)4954478.484169
0.4688 (2.1333)11600590.316173
0.4844 (2.0645)29835572.5456
0.5 (2)45398631.91753

\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
0.0156 (64) & 15507186445.0789 \tabularnewline
0.0312 (32) & 1619594471.94059 \tabularnewline
0.0469 (21.3333) & 2072739276.84291 \tabularnewline
0.0625 (16) & 600449197.62021 \tabularnewline
0.0781 (12.8) & 5789163969.671 \tabularnewline
0.0938 (10.6667) & 2508989347.1938 \tabularnewline
0.1094 (9.1429) & 91076399.943969 \tabularnewline
0.125 (8) & 7791784.038587 \tabularnewline
0.1406 (7.1111) & 227892152.625167 \tabularnewline
0.1562 (6.4) & 780049151.486941 \tabularnewline
0.1719 (5.8182) & 3068351465.47330 \tabularnewline
0.1875 (5.3333) & 69526396.302195 \tabularnewline
0.2031 (4.9231) & 37038412.399033 \tabularnewline
0.2188 (4.5714) & 61192225.77603 \tabularnewline
0.2344 (4.2667) & 125081628.090235 \tabularnewline
0.25 (4) & 248532172.763281 \tabularnewline
0.2656 (3.7647) & 10526190.097361 \tabularnewline
0.2812 (3.5556) & 68404068.393974 \tabularnewline
0.2969 (3.3684) & 22169091.498819 \tabularnewline
0.3125 (3.2) & 5856448.63212 \tabularnewline
0.3281 (3.0476) & 251092526.994107 \tabularnewline
0.3438 (2.9091) & 46871535.919175 \tabularnewline
0.3594 (2.7826) & 51165238.257596 \tabularnewline
0.375 (2.6667) & 13096926.133035 \tabularnewline
0.3906 (2.56) & 11796327.105729 \tabularnewline
0.4062 (2.4615) & 47639258.749722 \tabularnewline
0.4219 (2.3704) & 180758476.188013 \tabularnewline
0.4375 (2.2857) & 9880771.971243 \tabularnewline
0.4531 (2.2069) & 4954478.484169 \tabularnewline
0.4688 (2.1333) & 11600590.316173 \tabularnewline
0.4844 (2.0645) & 29835572.5456 \tabularnewline
0.5 (2) & 45398631.91753 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30705&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]0.0156 (64)[/C][C]15507186445.0789[/C][/ROW]
[ROW][C]0.0312 (32)[/C][C]1619594471.94059[/C][/ROW]
[ROW][C]0.0469 (21.3333)[/C][C]2072739276.84291[/C][/ROW]
[ROW][C]0.0625 (16)[/C][C]600449197.62021[/C][/ROW]
[ROW][C]0.0781 (12.8)[/C][C]5789163969.671[/C][/ROW]
[ROW][C]0.0938 (10.6667)[/C][C]2508989347.1938[/C][/ROW]
[ROW][C]0.1094 (9.1429)[/C][C]91076399.943969[/C][/ROW]
[ROW][C]0.125 (8)[/C][C]7791784.038587[/C][/ROW]
[ROW][C]0.1406 (7.1111)[/C][C]227892152.625167[/C][/ROW]
[ROW][C]0.1562 (6.4)[/C][C]780049151.486941[/C][/ROW]
[ROW][C]0.1719 (5.8182)[/C][C]3068351465.47330[/C][/ROW]
[ROW][C]0.1875 (5.3333)[/C][C]69526396.302195[/C][/ROW]
[ROW][C]0.2031 (4.9231)[/C][C]37038412.399033[/C][/ROW]
[ROW][C]0.2188 (4.5714)[/C][C]61192225.77603[/C][/ROW]
[ROW][C]0.2344 (4.2667)[/C][C]125081628.090235[/C][/ROW]
[ROW][C]0.25 (4)[/C][C]248532172.763281[/C][/ROW]
[ROW][C]0.2656 (3.7647)[/C][C]10526190.097361[/C][/ROW]
[ROW][C]0.2812 (3.5556)[/C][C]68404068.393974[/C][/ROW]
[ROW][C]0.2969 (3.3684)[/C][C]22169091.498819[/C][/ROW]
[ROW][C]0.3125 (3.2)[/C][C]5856448.63212[/C][/ROW]
[ROW][C]0.3281 (3.0476)[/C][C]251092526.994107[/C][/ROW]
[ROW][C]0.3438 (2.9091)[/C][C]46871535.919175[/C][/ROW]
[ROW][C]0.3594 (2.7826)[/C][C]51165238.257596[/C][/ROW]
[ROW][C]0.375 (2.6667)[/C][C]13096926.133035[/C][/ROW]
[ROW][C]0.3906 (2.56)[/C][C]11796327.105729[/C][/ROW]
[ROW][C]0.4062 (2.4615)[/C][C]47639258.749722[/C][/ROW]
[ROW][C]0.4219 (2.3704)[/C][C]180758476.188013[/C][/ROW]
[ROW][C]0.4375 (2.2857)[/C][C]9880771.971243[/C][/ROW]
[ROW][C]0.4531 (2.2069)[/C][C]4954478.484169[/C][/ROW]
[ROW][C]0.4688 (2.1333)[/C][C]11600590.316173[/C][/ROW]
[ROW][C]0.4844 (2.0645)[/C][C]29835572.5456[/C][/ROW]
[ROW][C]0.5 (2)[/C][C]45398631.91753[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30705&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30705&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)12
Frequency (Period)Spectrum
0.0156 (64)15507186445.0789
0.0312 (32)1619594471.94059
0.0469 (21.3333)2072739276.84291
0.0625 (16)600449197.62021
0.0781 (12.8)5789163969.671
0.0938 (10.6667)2508989347.1938
0.1094 (9.1429)91076399.943969
0.125 (8)7791784.038587
0.1406 (7.1111)227892152.625167
0.1562 (6.4)780049151.486941
0.1719 (5.8182)3068351465.47330
0.1875 (5.3333)69526396.302195
0.2031 (4.9231)37038412.399033
0.2188 (4.5714)61192225.77603
0.2344 (4.2667)125081628.090235
0.25 (4)248532172.763281
0.2656 (3.7647)10526190.097361
0.2812 (3.5556)68404068.393974
0.2969 (3.3684)22169091.498819
0.3125 (3.2)5856448.63212
0.3281 (3.0476)251092526.994107
0.3438 (2.9091)46871535.919175
0.3594 (2.7826)51165238.257596
0.375 (2.6667)13096926.133035
0.3906 (2.56)11796327.105729
0.4062 (2.4615)47639258.749722
0.4219 (2.3704)180758476.188013
0.4375 (2.2857)9880771.971243
0.4531 (2.2069)4954478.484169
0.4688 (2.1333)11600590.316173
0.4844 (2.0645)29835572.5456
0.5 (2)45398631.91753


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')