SuRF.vs - Subsampling Ranking Forward Selection (SuRF)
Performs variable selection based on subsampling, ranking
forward selection. Details of the method are published in Lihui
Liu, Hong Gu, Johan Van Limbergen, Toby Kenney (2020) SuRF: A
new method for sparse variable selection, with application in
microbiome data analysis Statistics in Medicine 40 897-919
<doi:10.1002/sim.8809>. Xo is the matrix of predictor
variables. y is the response variable. Currently only binary
responses using logistic regression are supported. X is a
matrix of additional predictors which should be scaled to have
sum 1 prior to analysis. fold is the number of folds for
cross-validation. Alpha is the parameter for the elastic net
method used in the subsampling procedure: the default value of
1 corresponds to LASSO. prop is the proportion of variables to
remove in the each subsample. weights indicates whether
observations should be weighted by class size. When the class
sizes are unbalanced, weighting observations can improve
results. B is the number of subsamples to use for ranking the
variables. C is the number of permutations to use for
estimating the critical value of the null distribution. If the
'doParallel' package is installed, the function can be run in
parallel by setting ncores to the number of threads to use. If
the default value of 1 is used, or if the 'doParallel' package
is not installed, the function does not run in parallel.
display.progress indicates whether the function should display
messages indicating its progress. family is a family variable
for the glm() fitting. Note that the 'glmnet' package does not
permit the use of nonstandard link functions, so will always
use the default link function. However, the glm() fitting will
use the specified link. The default is binomial with logistic
regression, because this is a common use case. pval is the
p-value for inclusion of a variable in the model. Under the
null case, the number of false positives will be geometrically
distributed with this as probability of success, so if this
parameter is set to p, the expected number of false positives
should be p/(1-p).
