Forward estimators after m steps

Description

A Forward Search gives a sequence of regression estimators. This function gives the regression estimators when stopped at m.

Usage

ForwardSearch.stopped(FS, m)

Arguments

Value

Author(s)

Bent Nielsen <bent.nielsen@nuffield.ox.ac.uk> 9 Sep 2014

References

Johansen, S. and Nielsen, B. (2013) Asymptotic analysis of the Forward Search. Download: Nuffield DP.

Johansen, S. and Nielsen, B. (2014) Outlier detection algorithms for least squares time series. Download: Nuffield DP.

Examples

#####################
#	EXAMPLE 1
#	using Fulton Fish data,
#	see Johansen and Nielsen (2014).

#	Call package
library(ForwardSearch)

#	Call data
data(Fulton)
mdata	<- as.matrix(Fulton)
n		<- nrow(mdata)

#	Identify variable to reproduce Johansen and Nielsen (2014)
q		<- mdata[2:n		,9]
q_1		<- mdata[1:(n-1) ,9]
s		<- mdata[2:n		,6]
x.q.s	<- cbind(q_1,s)
colnames(x.q.s	)	<- c("q_1","stormy")

#	Fit Forward Search
FS95	<- ForwardSearch.fit(x.q.s,q,psi.0=0.95)

ForwardSearch.stopped(FS95,107)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(ForwardSearch)
Loading required package: robustbase
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/ForwardSearch/ForwardSearch.stopped.Rd_%03d_medium.png", width=480, height=480)
> ### Name: ForwardSearch.stopped
> ### Title: Forward estimators after m steps
> ### Aliases: ForwardSearch.stopped
> 
> ### ** Examples
> 
> #####################
> #	EXAMPLE 1
> #	using Fulton Fish data,
> #	see Johansen and Nielsen (2014).
> 
> #	Call package
> library(ForwardSearch)
> 
> #	Call data
> data(Fulton)
> mdata	<- as.matrix(Fulton)
> n		<- nrow(mdata)
> 
> #	Identify variable to reproduce Johansen and Nielsen (2014)
> q		<- mdata[2:n		,9]
> q_1		<- mdata[1:(n-1) ,9]
> s		<- mdata[2:n		,6]
> x.q.s	<- cbind(q_1,s)
> colnames(x.q.s	)	<- c("q_1","stormy")
> 
> #	Fit Forward Search
> FS95	<- ForwardSearch.fit(x.q.s,q,psi.0=0.95)
> 
> ForwardSearch.stopped(FS95,107)
$res
               [,1]
  [1,] -0.643120941
  [2,] -0.276639648
  [3,]  0.364210858
  [4,] -0.475854200
  [5,]  0.662212451
  [6,]  0.151680947
  [7,]  0.370940422
  [8,] -0.443856126
  [9,]  0.529152538
 [10,]  0.183196876
 [11,]  0.212387653
 [12,]  0.557285732
 [13,]  0.847669950
 [14,]  0.332051069
 [15,]  0.693556422
 [16,] -0.628388507
 [17,] -1.963154228
 [18,]  0.261579655
 [19,]  0.841030661
 [20,] -0.494715888
 [21,] -0.358568522
 [22,]  0.203320030
 [23,]  0.996026199
 [24,] -0.258010987
 [25,] -0.120547741
 [26,]  0.851843435
 [27,] -0.220442565
 [28,]  0.683225679
 [29,] -0.147866333
 [30,] -0.590044082
 [31,]  0.215960636
 [32,] -1.144971170
 [33,] -1.823326081
 [34,] -1.193777405
 [35,] -0.200088378
 [36,]  0.500632806
 [37,]  0.559376968
 [38,] -0.015251986
 [39,] -0.064196651
 [40,] -0.563659216
 [41,]  0.044932157
 [42,] -0.298953221
 [43,]  0.719234124
 [44,] -0.823951410
 [45,] -1.064851264
 [46,]  0.301618644
 [47,]  0.283101422
 [48,]  0.395470594
 [49,] -0.434313447
 [50,]  0.340268643
 [51,]  0.330224908
 [52,]  0.780413417
 [53,]  0.984106091
 [54,] -0.652240637
 [55,]  0.010497938
 [56,]  0.321427688
 [57,] -0.369586392
 [58,] -1.036750532
 [59,] -0.768113493
 [60,] -0.293393867
 [61,]  0.473175075
 [62,] -0.084904056
 [63,]  0.478362082
 [64,]  0.270093626
 [65,]  0.482167732
 [66,] -0.193605683
 [67,]  1.277737769
 [68,]  0.342505951
 [69,] -0.640744222
 [70,]  0.969427033
 [71,] -0.355882686
 [72,]  0.991603927
 [73,]  0.368764186
 [74,] -1.315814409
 [75,]  0.011180212
 [76,]  0.585753171
 [77,] -0.875898385
 [78,] -0.412068038
 [79,] -0.687131592
 [80,]  0.744270392
 [81,] -1.065422651
 [82,] -0.530578984
 [83,] -1.221325575
 [84,] -0.072131103
 [85,]  0.636543402
 [86,]  0.806952001
 [87,]  0.181785219
 [88,] -1.450318870
 [89,]  0.826114797
 [90,] -0.038843596
 [91,] -0.134109101
 [92,] -0.047273023
 [93,] -1.327615354
 [94,] -2.403622110
 [95,]  0.695619068
 [96,]  0.020718996
 [97,]  0.538574923
 [98,] -0.471337865
 [99,]  0.057962207
[100,]  0.933140570
[101,] -0.247668393
[102,] -0.538847931
[103,] -0.004494437
[104,]  0.387341099
[105,]  0.263348661
[106,] -0.132707181
[107,] -1.545004136
[108,] -1.216260789
[109,]  0.167984555
[110,] -0.392624728

$ranks.selected
  [1] 103  55  75  38  96  90  41  92  99  39  84  62  25 106  91  29   6 109
 [19]  87  10  66  35  22  11  31  27 101  24  18 105  64   2  47  60  42  46
 [37]  56  51  14  50  68  71  21   3  73  57   7 104 110  48  78  49   8  98
 [55]  61   4  63  65  20  36   9  82  97 102  12  37  40  76  30  16  85  69
 [73]   1  54   5  28  79  15  95  43  80  59  52  86  44  89  19  13  26  77
 [91] 100  70  53  72  23  58  45  81  32  34 108  83  67  74  93  88 107

$ranks.outliers
[1] 33 17 94

$beta.m
[1]  7.92662347  0.08834236 -0.37144951

$sigma2.biased
[1] 0.4018263

> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>