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spa (version 2.0)

spa.control: Control Parameters for spa

Description

Controls various aspects of fitting the ‘spa’ object.

Usage

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,...)

Arguments

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.

References

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/.