## Free Statistics

of Irreproducible Research!

Author's title
Author*The author of this computation has been verified*
R Software Modulerwasp_variancereduction.wasp
Title produced by softwareVariance Reduction Matrix
Date of computationTue, 24 Nov 2009 13:46:29 -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/2009/Nov/24/t125909564004s2fbgt2bt7mn3.htm/, Retrieved Fri, 09 Aug 2024 03:14:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=59274, Retrieved Fri, 09 Aug 2024 03:14:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact222
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Variance Reduction Matrix] [Identifying Integ...] [2009-11-22 12:29:54] [b98453cac15ba1066b407e146608df68]
F    D          [Variance Reduction Matrix] [ws 8] [2009-11-24 20:46:29] [f7d3e79b917995ba1c8c80042fc22ef9] [Current]
-    D            [Variance Reduction Matrix] [WS8 VRM] [2009-11-27 15:39:14] [37a8d600db9abe09a2528d150ccff095]
- RMPD            [Spectral Analysis] [spectrum d=1 en D=1] [2009-12-08 18:53:31] [cd6314e7e707a6546bd4604c9d1f2b69]
- RMPD            [Spectral Analysis] [spectrum d=2 en D=1] [2009-12-08 18:55:13] [cd6314e7e707a6546bd4604c9d1f2b69]
- RM D            [Mean Plot] [verbetering] [2009-12-09 21:09:27] [f5d341d4bbba73282fc6e80153a6d315]
-    D              [Mean Plot] [verbetering] [2009-12-10 16:01:19] [f5d341d4bbba73282fc6e80153a6d315]
-    D                [Mean Plot] [verbetering] [2009-12-10 17:38:18] [f5d341d4bbba73282fc6e80153a6d315]
-    D                [Mean Plot] [verbetering] [2009-12-11 09:16:10] [f5d341d4bbba73282fc6e80153a6d315]
Feedback Forum
 2009-12-08 19:55:18 [Joris Van Mol] [reply] Bij deze methode zou het echter nuttig geweest zijn om ook de spectraalanalyse er eventjes bij te nemen om eventueel uitsluitsel te geven over deze differentiatie. Ik heb dit even voor jouw gedaan en heb de uitkomsten hier geblogd: Spectrum d=1 en D=1 http://www.freestatistics.org/blog/index.php?v=date/2009/Dec/08/t1260298450kf8i2alpqlye926.htm/ spectrum d=2 en D=1 http://www.freestatistics.org/blog/index.php?v=date/2009/Dec/08/t1260298560m9o5ffosedxe09y.htm/ Ik zou aan de hand van deze output durven stellen dat d=2 en D=1 de beste mogelijkheid is omdat bij deze differentiatie het cumulatief periodogram het best binnen het 95% betrouwbaarheidsinterval ligt.

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Dataseries X:
9,3
9,3
8,7
8,2
8,3
8,5
8,6
8,5
8,2
8,1
7,9
8,6
8,7
8,7
8,5
8,4
8,5
8,7
8,7
8,6
8,5
8,3
8
8,2
8,1
8,1
8
7,9
7,9
8
8
7,9
8
7,7
7,2
7,5
7,3
7
7
7
7,2
7,3
7,1
6,8
6,4
6,1
6,5
7,7
7,9
7,5
6,9
6,6
6,9
7,7
8
8
7,7
7,3
7,4
8,1

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 1 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59274&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59274&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59274&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 1 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135

 Variance Reduction Matrix V(Y[t],d=0,D=0) 0.501387005649717 Range 3.2 Trim Var. 0.300261224489796 V(Y[t],d=1,D=0) 0.111303331385155 Range 1.8 Trim Var. 0.0417411764705882 V(Y[t],d=2,D=0) 0.145641258318209 Range 1.9 Trim Var. 0.0881749622926093 V(Y[t],d=3,D=0) 0.282763157894737 Range 2.8 Trim Var. 0.120031372549020 V(Y[t],d=0,D=1) 0.48040780141844 Range 2.9 Trim Var. 0.282346109175378 V(Y[t],d=1,D=1) 0.102580943570768 Range 1.5 Trim Var. 0.0442439024390244 V(Y[t],d=2,D=1) 0.0912125603864735 Range 1.40000000000000 Trim Var. 0.0491025641025642 V(Y[t],d=3,D=1) 0.143 Range 1.7 Trim Var. 0.0741430499325237 V(Y[t],d=0,D=2) 1.21139682539683 Range 4 Trim Var. 0.804475806451613 V(Y[t],d=1,D=2) 0.271731092436975 Range 2.5 Trim Var. 0.132129032258064 V(Y[t],d=2,D=2) 0.210374331550802 Range 2.1 Trim Var. 0.11351724137931 V(Y[t],d=3,D=2) 0.357178030303029 Range 2.60000000000000 Trim Var. 0.184630541871921

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 0.501387005649717 & Range & 3.2 & Trim Var. & 0.300261224489796 \tabularnewline
V(Y[t],d=1,D=0) & 0.111303331385155 & Range & 1.8 & Trim Var. & 0.0417411764705882 \tabularnewline
V(Y[t],d=2,D=0) & 0.145641258318209 & Range & 1.9 & Trim Var. & 0.0881749622926093 \tabularnewline
V(Y[t],d=3,D=0) & 0.282763157894737 & Range & 2.8 & Trim Var. & 0.120031372549020 \tabularnewline
V(Y[t],d=0,D=1) & 0.48040780141844 & Range & 2.9 & Trim Var. & 0.282346109175378 \tabularnewline
V(Y[t],d=1,D=1) & 0.102580943570768 & Range & 1.5 & Trim Var. & 0.0442439024390244 \tabularnewline
V(Y[t],d=2,D=1) & 0.0912125603864735 & Range & 1.40000000000000 & Trim Var. & 0.0491025641025642 \tabularnewline
V(Y[t],d=3,D=1) & 0.143 & Range & 1.7 & Trim Var. & 0.0741430499325237 \tabularnewline
V(Y[t],d=0,D=2) & 1.21139682539683 & Range & 4 & Trim Var. & 0.804475806451613 \tabularnewline
V(Y[t],d=1,D=2) & 0.271731092436975 & Range & 2.5 & Trim Var. & 0.132129032258064 \tabularnewline
V(Y[t],d=2,D=2) & 0.210374331550802 & Range & 2.1 & Trim Var. & 0.11351724137931 \tabularnewline
V(Y[t],d=3,D=2) & 0.357178030303029 & Range & 2.60000000000000 & Trim Var. & 0.184630541871921 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59274&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]0.501387005649717[/C][C]Range[/C][C]3.2[/C][C]Trim Var.[/C][C]0.300261224489796[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]0.111303331385155[/C][C]Range[/C][C]1.8[/C][C]Trim Var.[/C][C]0.0417411764705882[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]0.145641258318209[/C][C]Range[/C][C]1.9[/C][C]Trim Var.[/C][C]0.0881749622926093[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]0.282763157894737[/C][C]Range[/C][C]2.8[/C][C]Trim Var.[/C][C]0.120031372549020[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]0.48040780141844[/C][C]Range[/C][C]2.9[/C][C]Trim Var.[/C][C]0.282346109175378[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]0.102580943570768[/C][C]Range[/C][C]1.5[/C][C]Trim Var.[/C][C]0.0442439024390244[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]0.0912125603864735[/C][C]Range[/C][C]1.40000000000000[/C][C]Trim Var.[/C][C]0.0491025641025642[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]0.143[/C][C]Range[/C][C]1.7[/C][C]Trim Var.[/C][C]0.0741430499325237[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]1.21139682539683[/C][C]Range[/C][C]4[/C][C]Trim Var.[/C][C]0.804475806451613[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]0.271731092436975[/C][C]Range[/C][C]2.5[/C][C]Trim Var.[/C][C]0.132129032258064[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]0.210374331550802[/C][C]Range[/C][C]2.1[/C][C]Trim Var.[/C][C]0.11351724137931[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]0.357178030303029[/C][C]Range[/C][C]2.60000000000000[/C][C]Trim Var.[/C][C]0.184630541871921[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59274&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59274&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

 Variance Reduction Matrix V(Y[t],d=0,D=0) 0.501387005649717 Range 3.2 Trim Var. 0.300261224489796 V(Y[t],d=1,D=0) 0.111303331385155 Range 1.8 Trim Var. 0.0417411764705882 V(Y[t],d=2,D=0) 0.145641258318209 Range 1.9 Trim Var. 0.0881749622926093 V(Y[t],d=3,D=0) 0.282763157894737 Range 2.8 Trim Var. 0.120031372549020 V(Y[t],d=0,D=1) 0.48040780141844 Range 2.9 Trim Var. 0.282346109175378 V(Y[t],d=1,D=1) 0.102580943570768 Range 1.5 Trim Var. 0.0442439024390244 V(Y[t],d=2,D=1) 0.0912125603864735 Range 1.40000000000000 Trim Var. 0.0491025641025642 V(Y[t],d=3,D=1) 0.143 Range 1.7 Trim Var. 0.0741430499325237 V(Y[t],d=0,D=2) 1.21139682539683 Range 4 Trim Var. 0.804475806451613 V(Y[t],d=1,D=2) 0.271731092436975 Range 2.5 Trim Var. 0.132129032258064 V(Y[t],d=2,D=2) 0.210374331550802 Range 2.1 Trim Var. 0.11351724137931 V(Y[t],d=3,D=2) 0.357178030303029 Range 2.60000000000000 Trim Var. 0.184630541871921

Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)n <- length(x)sx <- sort(x)load(file='createtable')a<-table.start()a<-table.row.start(a)a<-table.element(a,'Variance Reduction Matrix',6,TRUE)a<-table.row.end(a)for (bigd in 0:2) {for (smalld in 0:3) {mylabel <- 'V(Y[t],d='mylabel <- paste(mylabel,as.character(smalld),sep='')mylabel <- paste(mylabel,',D=',sep='')mylabel <- paste(mylabel,as.character(bigd),sep='')mylabel <- paste(mylabel,')',sep='')a<-table.row.start(a)a<-table.element(a,mylabel,header=TRUE)myx <- xif (smalld > 0) myx <- diff(x,lag=1,differences=smalld)if (bigd > 0) myx <- diff(myx,lag=par1,differences=bigd)a<-table.element(a,var(myx))a<-table.element(a,'Range',header=TRUE)a<-table.element(a,max(myx)-min(myx))a<-table.element(a,'Trim Var.',header=TRUE)smyx <- sort(myx)sn <- length(smyx)a<-table.element(a,var(smyx[smyx>quantile(smyx,0.05) & smyxa<-table.row.end(a)}}a<-table.end(a)table.save(a,file='mytable.tab')