trainSlalom: Train a SlalomModel object

Description Usage Arguments Details Value Author(s) Examples

Description

Train a SlalomModel to infer model parameters.

Usage

1
2
3
trainSlalom(object, nIterations = 5000, minIterations = 700,
  tolerance = 1e-08, forceIterations = FALSE, shuffle = TRUE,
  pretrain = TRUE, verbose = TRUE, seed = NULL, drop_factors = TRUE)

Arguments

object

a Rcpp_SlalomModel object

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 nIteration times?

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 TRUE

pretrain

logical(1), should the model be "pre-trained" to achieve faster convergence and obtain an initial update order? Recommended; default is TRUE

verbose

logical(1), should messages be printed about what the function is doing? Default is TRUE.

seed

integer(1) value supplying a random seed to make results reproducible (default is NULL)

drop_factors

logical(1), should factors be dropped from the model if the model determines them not to be relevant? Default is TRUE.

Details

Train the model using variational Bayes methods to infer parameters.

Value

an 'Rcpp_SlalomModel' object

Author(s)

Davis McCarthy

Examples

1
2
3
4
5
6
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)

PMBio/slalom documentation built on May 20, 2019, 1:26 p.m.