PointsUpdate {adlift}R Documentation

PointsUpdate

Description

This function performs the update lifting step using a given configuration of neighbours and boundary handling.

Usage

PointsUpdate(X, coeff, nbrs, index, remove, pointsin, weights, lengths,
 updateboundhandl)

Arguments

X the vector of grid values.
coeff the vector of detail and scaling coefficients at that step of the transform.
nbrs the indices (into X) of the neighbours to be used in the lifting step.
index the indices into pointsin of nbrs, the neighbours of remove.
remove the index (into X) of the point to be removed.
pointsin The indices of gridpoints still to be removed.
weights the prediction weights obtained from the regression in the prediction step of the transform.
lengths the vector of interval lengths at the present step of the transform (to be updated).
updateboundhandl boundary handling in the update step. Possible values are "reflect", "stop" and "add". If the point to be removed is at the boundary, "reflect" updates the neighbour interval to be symmetrical about its gridpoint; "stop" extends its length up until the boundary gridpoint; and "add" increases its interval length by the interval length associated to the removed boundary point.

Details

The procedure performs a minimum norm update lifting step. Firstly the interval lengths are updated using the coefficients obtained. Secondly, the scaling and detail coefficient vector is modified using the new interval lengths.

Value

coeff vector of (modified) detail and scaling coefficients to be used in the next step of the transform.
lengths the vector of interval lengths after the update step of the transform.
r the index into pointsin of remove.
N length(pointsin).
weights The regression coefficients used in prediction.
alpha the update weights used to update lengths and coeff.

Author(s)

Matt Nunes (matt.nunes@bristol.ac.uk), Marina Popa (Marina.Popa@bristol.ac.uk)

See Also

AdaptNeigh, AdaptPred, CubicPred, fwtnp, LinearPred, QuadPred, UndoPointsUpdate

Examples

#
# Generate some blocks data: 100 observations.
#
x <- runif(100)
y <-make.signal2("blocks",x=x)
#
#find initial interval lengths...
#
I<-intervals(x,"reflect")
l<-lengthintervals(x,I$intervals,neighbours=2,closest=FALSE)
lengths<-l$lengths
#
#perform prediction step...
p<-AdaptNeigh(order(x),x,y,32,5,TRUE,2)
#
#
u<-PointsUpdate(x,p$results[[6]],p$newinfo[[3]],p$newinfo[[4]],5,order(x),p$results[[4]],
lengths,"add")
#
#and here are the updated coefficients...
u$coeff
# 


[Package adlift version 0.9-6 Index]