optimizeNewLambda | R Documentation |
Uses an efficient strategy for updating that takes advantage of the information in the existing factorization; uses previous k. Recommended mainly when re-optimizing for higher lambda and when new lambda value is significantly different; otherwise may not return optimal results.
optimizeNewLambda(
object,
new.lambda,
thresh = 1e-04,
max.iters = 100,
rand.seed = 1,
verbose = TRUE
)
object |
|
new.lambda |
Regularization parameter. Larger values penalize dataset-specific effects more strongly. |
thresh |
Convergence threshold. Convergence occurs when |obj0-obj|/(mean(obj0,obj)) < thresh |
max.iters |
Maximum number of block coordinate descent iterations to perform (default 100). |
rand.seed |
Random seed for reproducibility (default 1). |
verbose |
Print progress bar/messages (TRUE by default) |
liger
object with optimized factorization values
ligerex <- createLiger(list(ctrl = ctrl, stim = stim))
ligerex <- normalize(ligerex)
ligerex <- selectGenes(ligerex)
ligerex <- scaleNotCenter(ligerex)
# Assume we are performing the factorization
# Specification for minimal example run time, not converging.
ligerex <- optimizeALS(ligerex, k = 5, lambda = 5, max.iters = 1)
# decide to run with lambda = 15 instead (keeping k the same)
ligerex <- optimizeNewLambda(ligerex, new.lambda = 15, max.iters = 1)
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