Aligns LDA topics across models — align_lda

align_lda_topics() estimates LDA models for a range of topic numbers and aligns the topics across models using the product and transport method.

Usage

align_lda_topics(
  ta,
  k_range = 2:10,
  seed = 42,
  min_prevalence = 0.05,
  taxon = taxon_id,
  sample = sample_id,
  ...
)

ta

A tidytacos object.

k_range

A range of topic numbers to estimate.

seed

A seed for reproducibility.

min_prevalence

The lowest percentage (0-1) of samples taxa need to be present in for the taxa to be used in model estimation.

taxon

The column name in the taxa table with taxa identifiers.

sample

The column name in the sample table with sample identifiers.

...

Arguments passed on to alto::run_lda_models

data: (required) a matrix, data.frame or slam::simple_triplet_matrix containing the counts (integers) of each feature (e.g. words) and each sample (or document). If data is provided as matrix or data.frame, each row is a sample, each column is a feature.
lda_varying_params_lists: (required) a list specifying the parameter for each models that needs to be ran. Currently, supported parameters are "k" (the number of topic), "method" ("VEM" or "Gibbs"), and "control", a list of type LDAcontrol. See topicmodels::LDA for details and below for examples.
lda_fixed_params_list: (optional) a list specifying the parameters common to all models to be fitted. Values provided by lda_fixed_params_list are overwritten by those provided by lda_varying_params_lists.
dir: (optional) a character specifying the directory in which individual LDA models should be stored. If not specified, individual LDA models are not stored. This option is especially useful for data exploration as it allows to save execution time if one wishes to add models to an existing model list. (see examples)
reset: (optional, default = FALSE). Should any cached models in the save directory be cleared?
verbose: (optional, default = FALSE) Print verbose output while running models?

A list of estimated models, aligned topics using the product method and aligned topics using the transport method.