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R Software Module

rwasp_variancereduction.wasp

Title produced by software

Variance Reduction Matrix

Date of computation

Tue, 02 Dec 2008 13:51:47 -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/02/t1228251156hzdqmkau59cg1fi.htm/, Retrieved Sun, 19 May 2024 12:14:45 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28437, Retrieved Sun, 19 May 2024 12:14:45 +0000

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No (this computation is public)

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Estimated Impact

158

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

F       [Variance Reduction Matrix] [Q6 VRM] [2008-12-02 20:51:47] [52492148dbcac26917ed19e489351f79] [Current]

Feedback Forum

2008-12-06 11:54:22 [Loïque Verhasselt] [reply] 
Via VRM:de student geeft de output maar geen conclusie.We moeten echter wel in acht nemen dat deze methode beïnvloed wordt door outliers. Indien er outliers zijn, kunnen we best de getrimde variantie bekijken. Voor de zekerheid zullen we even nagaan of er een verschil is.D= 1 en d=1 wat de laagste variantie(152,69 of getrimd:73,12) oplevert.
2008-12-08 19:40:20 [Charis Berrevoets] [reply] 
Je hebt hier wederom niets geschreven. Je had moeten kijken naar de variantie. Als je zoveel mogelijk wil verklaren moet de variantie zo klein mogelijk zijn. De kleinste variantie is 152 bij een differentiatie van d=1 en D=1. Je mag echter niet vergeten dat outliers een grote invloed hebben op de variantie. Daarom is het vaak beter om naar de getrimde variantie te kijken want hier is het effect van de outliers reeds uitgehaald. Daar zien we echter ook het kleinste resultaat bij een differentiatie van d=1 en D=1 (variantie = 73). Dit is dezelfde conclusie als je ook al kon trekken door de methode van ACF te gebruiken. 

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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	1 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=28437&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=28437&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28437&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	'George Udny Yule' @ 72.249.76.132

Variance Reduction Matrix
V(Y[t],d=0,D=0)	14391.9172008547	Range	518	Trim Var.	9847.79429133858
V(Y[t],d=1,D=0)	1139.35152171772	Range	188	Trim Var.	612.29533808274
V(Y[t],d=2,D=0)	1588.47427829388	Range	228	Trim Var.	876.603936507937
V(Y[t],d=3,D=0)	3719.00577507599	Range	327	Trim Var.	2090.46334906897
V(Y[t],d=0,D=1)	311.688410825815	Range	82	Trim Var.	209.573091659299
V(Y[t],d=1,D=1)	152.689254257193	Range	90	Trim Var.	73.119940029985
V(Y[t],d=2,D=1)	402.897078115683	Range	153	Trim Var.	195.552248875562
V(Y[t],d=3,D=1)	1339.09750484496	Range	269	Trim Var.	676.122807017544
V(Y[t],d=0,D=2)	608.806442577031	Range	129	Trim Var.	391.097525095189
V(Y[t],d=1,D=2)	325.280871670702	Range	104	Trim Var.	170.03423180593
V(Y[t],d=2,D=2)	869.159785600464	Range	174	Trim Var.	476.192857142857
V(Y[t],d=3,D=2)	2962.97539050987	Range	322	Trim Var.	1636.54032860344

\begin{tabular}{lllllllll}
\hline
Variance Reduction Matrix \tabularnewline
V(Y[t],d=0,D=0) & 14391.9172008547 & Range & 518 & Trim Var. & 9847.79429133858 \tabularnewline
V(Y[t],d=1,D=0) & 1139.35152171772 & Range & 188 & Trim Var. & 612.29533808274 \tabularnewline
V(Y[t],d=2,D=0) & 1588.47427829388 & Range & 228 & Trim Var. & 876.603936507937 \tabularnewline
V(Y[t],d=3,D=0) & 3719.00577507599 & Range & 327 & Trim Var. & 2090.46334906897 \tabularnewline
V(Y[t],d=0,D=1) & 311.688410825815 & Range & 82 & Trim Var. & 209.573091659299 \tabularnewline
V(Y[t],d=1,D=1) & 152.689254257193 & Range & 90 & Trim Var. & 73.119940029985 \tabularnewline
V(Y[t],d=2,D=1) & 402.897078115683 & Range & 153 & Trim Var. & 195.552248875562 \tabularnewline
V(Y[t],d=3,D=1) & 1339.09750484496 & Range & 269 & Trim Var. & 676.122807017544 \tabularnewline
V(Y[t],d=0,D=2) & 608.806442577031 & Range & 129 & Trim Var. & 391.097525095189 \tabularnewline
V(Y[t],d=1,D=2) & 325.280871670702 & Range & 104 & Trim Var. & 170.03423180593 \tabularnewline
V(Y[t],d=2,D=2) & 869.159785600464 & Range & 174 & Trim Var. & 476.192857142857 \tabularnewline
V(Y[t],d=3,D=2) & 2962.97539050987 & Range & 322 & Trim Var. & 1636.54032860344 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28437&T=1

[TABLE]
[ROW][C]Variance Reduction Matrix[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=0)[/C][C]14391.9172008547[/C][C]Range[/C][C]518[/C][C]Trim Var.[/C][C]9847.79429133858[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=0)[/C][C]1139.35152171772[/C][C]Range[/C][C]188[/C][C]Trim Var.[/C][C]612.29533808274[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=0)[/C][C]1588.47427829388[/C][C]Range[/C][C]228[/C][C]Trim Var.[/C][C]876.603936507937[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=0)[/C][C]3719.00577507599[/C][C]Range[/C][C]327[/C][C]Trim Var.[/C][C]2090.46334906897[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=1)[/C][C]311.688410825815[/C][C]Range[/C][C]82[/C][C]Trim Var.[/C][C]209.573091659299[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=1)[/C][C]152.689254257193[/C][C]Range[/C][C]90[/C][C]Trim Var.[/C][C]73.119940029985[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=1)[/C][C]402.897078115683[/C][C]Range[/C][C]153[/C][C]Trim Var.[/C][C]195.552248875562[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=1)[/C][C]1339.09750484496[/C][C]Range[/C][C]269[/C][C]Trim Var.[/C][C]676.122807017544[/C][/ROW]
[ROW][C]V(Y[t],d=0,D=2)[/C][C]608.806442577031[/C][C]Range[/C][C]129[/C][C]Trim Var.[/C][C]391.097525095189[/C][/ROW]
[ROW][C]V(Y[t],d=1,D=2)[/C][C]325.280871670702[/C][C]Range[/C][C]104[/C][C]Trim Var.[/C][C]170.03423180593[/C][/ROW]
[ROW][C]V(Y[t],d=2,D=2)[/C][C]869.159785600464[/C][C]Range[/C][C]174[/C][C]Trim Var.[/C][C]476.192857142857[/C][/ROW]
[ROW][C]V(Y[t],d=3,D=2)[/C][C]2962.97539050987[/C][C]Range[/C][C]322[/C][C]Trim Var.[/C][C]1636.54032860344[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28437&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28437&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)	14391.9172008547	Range	518	Trim Var.	9847.79429133858
V(Y[t],d=1,D=0)	1139.35152171772	Range	188	Trim Var.	612.29533808274
V(Y[t],d=2,D=0)	1588.47427829388	Range	228	Trim Var.	876.603936507937
V(Y[t],d=3,D=0)	3719.00577507599	Range	327	Trim Var.	2090.46334906897
V(Y[t],d=0,D=1)	311.688410825815	Range	82	Trim Var.	209.573091659299
V(Y[t],d=1,D=1)	152.689254257193	Range	90	Trim Var.	73.119940029985
V(Y[t],d=2,D=1)	402.897078115683	Range	153	Trim Var.	195.552248875562
V(Y[t],d=3,D=1)	1339.09750484496	Range	269	Trim Var.	676.122807017544
V(Y[t],d=0,D=2)	608.806442577031	Range	129	Trim Var.	391.097525095189
V(Y[t],d=1,D=2)	325.280871670702	Range	104	Trim Var.	170.03423180593
V(Y[t],d=2,D=2)	869.159785600464	Range	174	Trim Var.	476.192857142857
V(Y[t],d=3,D=2)	2962.97539050987	Range	322	Trim Var.	1636.54032860344

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 <- x
if (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')

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