| Name : R-SuRF.vs
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| Version : 1.1.0.1
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp153.7.7
| Date : 2024-06-14 11:51:52
|
| Group : Development/Libraries/Other
| Source RPM : R-SuRF.vs-1.1.0.1-lp153.7.7.src.rpm
|
| Size : 0.11 MB
| |
| Packager : (none)
| |
| Summary : Subsampling Ranking Forward Selection (SuRF)
|
Description :
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).
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
