SemiSupervised.control: Control Parameters for the S4-generic generic functions...
In SemiSupervised: Safe Semi-Supervised Learning Tools

View source: R/SemiSupervised-external.R

Controls various aspects of fitting any ‘SemiSupervised’ object.

             
SemiSupervised.control(normalize=TRUE,stability=NULL,k=NULL,nok=FALSE,
dissimilar=TRUE,l.eps=1e-5,l.thresh=25L,h.thresh=1e-5,U.as.anchor.thresh=600L,
U.as.anchor=TRUE,sig.est=TRUE,sig.frac=0.5,iter.max=1000L,
anchor.seed=100,sfac=5L,cn=4L,LAE.thresh=100L,LAE.eps=1e-4,
cv.fold=3L,cv.seed=100L,cv.cl=TRUE,cv.type="scv",cv.adjust=0.001)

`normalize`	flags whether or not the normalized or combinatorial graph operator should be used. This is not used by ‘jtharm’. Further, it has no effect on anchor graph based approaches.
`stability`	stabilization parameter for necessary inverses. A NULL value allows it to be set internally (recommended) but can be set manually.
`k`	in fitting a distance graph with the ‘formula’ as y~. this will set the default k-NN graph parameter. It is not used otherwise. In the case of an anchor graph the k parameter is the number of anchors used by k-means.
`nok`	flags the y~. ‘formula’ call to either fit or not fit a k-NN parameter which overrides the k argument.
`dissimilar`	flags whether the graph is similar or dissimilar. This is not necessary in the ‘formula’ call since a call to `sG` in the formula automatically flags the graph as similarity or a call to `dG` to dissimilar. This is necessary in all non-formula calls with similarity graphs.
`sfac`	the ‘s’ parameter for the LAE method.
`cn`	the ‘cn’ parameter for the LAE method.
`LAE.thresh`	thresh hold for LAE algorithm.
`LAE.eps`	convergence tolerance for LAE algorithm.
`iter.max`	maximum number of iterations for `kmeans`.
`anchor.seed`	sets the seed for the `kmeans` algorithm.
`sig.est`	use an internal estimation scheme to estimate the parameter for an rbf kernel.
`sig.frac`	when ‘sig.est’ is true, the fraction of training data necessary for the computaton is used.
`l.thresh`	max iteration parameter for the underlying logistic regression algorithm when fitting classification.
`l.eps`	threshold for stopping the underlying logistic regression algorithm when fitting classification.
`U.as.anchor`	if n>U.as.anchor.thresh then the anchor points are fitted as the unlabeled cases to speed up the approach. This only works in the formula call where the graph is unspecified, i.e., y~.
`U.as.anchor.thresh`	threshold for determining when the unlabeled are to be anchors.
`h.thresh`	minimum allowable choice for h in the grid used by CV.
`cv.fold`	the number of folds used for K-fold CV.
`cv.seed`	the seed to generate the folds for K-fold CV.
`cv.cl`	forces in classification at least two distinct responses in each fold and should be set to TRUE.
`cv.type`	use "scv" or "cv". The "scv" is faster and should be used.
`cv.adjust`	forces an inverse stabilization in "scv".