trainSlalom {slalom} | R Documentation |
Train a SlalomModel to infer model parameters.
trainSlalom(object, nIterations = 5000, minIterations = 700, tolerance = 1e-08, forceIterations = FALSE, shuffle = TRUE, pretrain = TRUE, verbose = TRUE, seed = NULL, drop_factors = TRUE)
object |
a |
nIterations |
integer(1) maximum number of iterations to use in training the model (default: 5000) |
minIterations |
integer(1) minimum number of iterations to perform. |
tolerance |
numeric(1) tolerance to allow between iterations (default 1e-08) |
forceIterations |
logical(1) should the model be forced to update
|
shuffle |
logical(1) should the order in which factors are updated be
shuffled between iterations? Shuffling generally helps speed up convergence
so is recommended and defaults is |
pretrain |
logical(1), should the model be "pre-trained" to achieve
faster convergence and obtain an initial update order? Recommended; default
is |
verbose |
logical(1), should messages be printed about what the function
is doing? Default is |
seed |
integer(1) value supplying a random seed to make results
reproducible (default is |
drop_factors |
logical(1), should factors be dropped from the model if
the model determines them not to be relevant? Default is |
Train the model using variational Bayes methods to infer parameters.
an 'Rcpp_SlalomModel' object
Davis McCarthy
gmtfile <- system.file("extdata", "reactome_subset.gmt", package = "slalom") genesets <- GSEABase::getGmt(gmtfile) data("mesc") model <- newSlalomModel(mesc, genesets, n_hidden = 5, min_genes = 10) model <- initSlalom(model) model <- trainSlalom(model, nIterations = 10)