Create a Solver class object using the Ridge solver

Create a Solver class object using the Ridge solver

RidgeSolver(
  mtx.assay = matrix(),
  targetGene,
  candidateRegulators,
  regulatorWeights = rep(1, length(candidateRegulators)),
  alpha = 0,
  lambda = numeric(0),
  keep.metrics = FALSE,
  quiet = TRUE
)

Arguments

mtx.assay	An assay matrix of gene expression data
targetGene	A designated target gene that should be part of the mtx.assay data
candidateRegulators	The designated set of transcription factors that could be associated with the target gene
regulatorWeights	A set of weights on the transcription factors (default = rep(1, length(tfs)))
alpha	A parameter from 0-1 that determines the proportion of LASSO to ridge used in the elastic net solver, with 0 being fully ridge and 1 being fully LASSO (default = 0.9)
lambda	A tuning parameter that determines the severity of the penalty function imposed on the elastic net regression. If unspecified, lambda will be determined via permutation testing (default = numeric(0)).
keep.metrics	A logical denoting whether or not to keep the initial supplied metrics versus determining new ones
quiet	A logical denoting whether or not the solver should print output

Value

A Solver class object with Ridge as the solver

See also

solve.Ridge, getAssayData

Other Solver class objects: BayesSpikeSolver, EnsembleSolver, HumanDHSFilter-class, LassoPVSolver, LassoSolver, PearsonSolver, RandomForestSolver, Solver-class, SpearmanSolver, SqrtLassoSolver, XGBoostSolver

Examples

if (FALSE) {
   load(system.file(package="trena", "extdata/ampAD.154genes.mef2cTFs.278samples.RData"))
   target.gene <- "MEF2C"
   tfs <- setdiff(rownames(mtx.sub), target.gene)
   ridge.solver <- RidgeSolver(mtx.sub, target.gene, tfs)
   }