spa.control: Control Parameters for spa
In spa: Implements The Sequential Predictions Algorithm

Description Usage Arguments Note Author(s) References

Controls various aspects of fitting the ‘spa’ object.

1
2
3

  spa.control(eps=1e-6,maxiter=20,gcv=c("lGCV","tGCV","fGCV","aGCV"),
              lqmax=0.2,lqmin=0.05,ldepth=10,ltmin=0.05,lgrid=NULL,
              lval=NULL,dissimilar=TRUE,pce=FALSE,adjust=0,warn=FALSE,...)

`eps`	the tolerance parameter for spa using a type=‘class’ argument.
`maxiter`	the maximum number of iterations to run the algorithm using type=‘class’ argument. This parameter forces the algorithm to stop even if eps is not met.
`gcv`	aGCV=approximate GCV using the smoother SLL+t(SU)*SUL, tGCV=GCV using the smoother SLL+SLUsolve(I-SUU,SUL) (can be slow), lGCV=GCV using the supervised smoother (fast but not that good), and fGCV=approximate GCV using the smoother S with approximation above (this is no longer documented but it is still implemented).
`lqmax`	max quantile on the density of distance for data-driven estimation
`lqmin`	min quantile on the density of distance for data-driven estimation
`ldepth`	the depth of the search for divide and conquer parameter estimation
`ltmin`	the minimum value, in-case `lqmin` is negative
`lgrid`	if set to an integer, then the divide and conquer approach is bypassed
`lval`	if set then the smoothing parameter is `lval`
`dissimilar`	if the edges represent similarity then set this to TRUE. This flag is intended for use with the Laplacain smoother as input (for SPS this flag is ignored and the graph is assumed to be dissimilar). If the flag is FALSE then the supplied kernel is used to convert the graph to similarity.
`warn`	if TRUE then the procedure warns the user that a ginv will be used in the matrix inversion (i.e. the matrix is not invertible)
`pce`	parameter adjust is meant for adjusting hard probability class estimates to soft (i.e. if p(z)=1 then p(z)=0.9999), for GCV estimation, this pushes GCV away from selecting smaller values.
`adjust`	apply adjustment W=W+adjust.
`...`	mop up additional parameters passed in.

Keep in mind, that for exponential loss (hard) we are being somewhat non-conventional by using GCV at all, i.e. loss/df where df=1-tr/m (m is known data size).

Mark Culp

M. Culp (2011). spa: A Semi-Supervised R Package for Semi-Parametric Graph-Based Estimation. Journal of Statistical Software, 40(10), 1-29. URL http://www.jstatsoft.org/v40/i10/.

spa documentation built on May 30, 2017, 1:27 a.m.