PointsUpdate {adlift} | R Documentation |
This function performs the update lifting step using a given configuration of neighbours and boundary handling.
PointsUpdate(X, coeff, nbrs, index, remove, pointsin, weights, lengths, updateboundhandl)
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. |
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.
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. |
Matt Nunes (matt.nunes@bristol.ac.uk), Marina Popa (Marina.Popa@bristol.ac.uk)
AdaptNeigh
, AdaptPred
, CubicPred
, fwtnp
, LinearPred
, QuadPred
, UndoPointsUpdate
# # 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 #