ForwardSearch.plot: Plots forward residuals with simultaneous confidence bands

In ForwardSearch: Forward Search using asymptotic theory

Description

Plots forward residuals with simultaneous confidence bands based on Johansen and Nielsen (2013, 2014).

Usage

ForwardSearch.plot(FS, ref.dist = "normal",
	bias.correct = FALSE, return = FALSE, plot.legend = TRUE,
	col = NULL, legend = NULL, lty = NULL, lwd = NULL,
	main = NULL, type = NULL, xlab = NULL, ylab = NULL)

Arguments

FS	List. Value of the function ForwardSearch.fit.
ref.dist	Character. Reference distribution. "normal" standard normal distribution.
bias.correct	Logical. If FALSE do not bias correct variance, so plots have appearance similar to Atkinson and Riani (2000). If TRUE do bias correct variance, so plots start at origin. Default is FALSE.
return	Logical. Default is FALSE: do not return values.
plot.legend	Logical. Default is TRUE: include legend in plot.
col	plot parameter. Vector of 6 colours.
legend	plot parameter. Vector of 6 characters.
lty	plot parameter. Vector of 6 line types.
lwd	plot parameter. Vector of 6 line widths.
main	plot parameter. Character.
type	plot parameter. Charcater for plot type.
xlab	plot parameter. Charcater for x label.
ylab	plot parameter. Charcater for y label.

Value

ref.dist	Character. From argument.
bias.correct	Logical. From argument.
forward.residual.scaled	Vector. Forward residuals scaled by estimated variance. The estimated variance is or is not bias corrected depending on the choice of bias.correct.
forward.asymp.median	Vector. Asymptotic median.
forward.asymp.sdv	Vector. Asymptotic standard deviation. Not divided by squareroot of sample size.
cut.off	Matrix. Cut-offs taken from Table 3 of Johansen and Nielsen (2014).

Author(s)

Bent Nielsen <[e-mail]> 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.plot(FS95)

ForwardSearch documentation built on May 1, 2019, 6:51 p.m.