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Fit Topic Prevalence Model

Source: R/topic_modeling.R
fit_topic_prevalence_model.Rd

Fits a count regression model to topic prevalence data, auto-selecting between Poisson, Negative Binomial, and Zero-Inflated Negative Binomial based on dispersion ratio and zero-inflation diagnostics.

Usage

fit_topic_prevalence_model(
  topic_proportions,
  metadata,
  formula,
  model_type = "auto",
  zero_inflation_threshold = 0.5,
  count_multiplier = 1000,
  max_iterations = 200
)

Arguments

topic_proportions

Numeric vector of topic proportions (0-1) for one topic.

metadata

Data frame of document-level covariates.

formula

Model formula (character or formula object). Response variable is created internally as topic_count.

model_type

Model selection strategy: "auto" (default), "poisson", "negbin", or "zeroinfl".

zero_inflation_threshold

Proportion of zeros above which a zero-inflated model is attempted (default: 0.5).

count_multiplier

Multiplier to convert proportions to pseudo-counts (default: 1000).

max_iterations

Maximum iterations for model fitting (default: 200).

Value

List containing:

  • model: Fitted model object

  • summary: Tidy summary with odds ratios

  • model_type: Selected model type

  • diagnostics: Zero proportion, dispersion ratio, mean/variance

  • formula: Formula used

See also

Other topic-modeling: analyze_semantic_evolution(), assess_embedding_stability(), assess_hybrid_stability(), auto_tune_embedding_topics(), calculate_assignment_consistency(), calculate_keyword_stability(), calculate_semantic_drift(), calculate_topic_probability(), calculate_topic_stability(), find_optimal_k(), find_topic_matches(), fit_embedding_model(), fit_hybrid_model(), fit_temporal_model(), generate_topic_labels(), get_topic_prevalence(), get_topic_terms(), get_topic_texts(), identify_topic_trends(), plot_model_comparison(), plot_quality_metrics(), plot_topic_effects_categorical(), plot_topic_effects_continuous(), plot_topic_probability(), plot_word_probability(), run_neural_topics_internal(), validate_semantic_coherence()