Plot Topic Effects for Continuous Variables

Creates a faceted plot showing how continuous variables affect topic proportions.

Usage

plot_topic_effects_continuous(
  effects_data,
  ncol = 2,
  height = 800,
  width = 1000,
  title = "Continuous Variable Effects",
  base_font_size = 11
)

Arguments

effects_data

Data frame with columns: topic, value, proportion, lower, upper

ncol

Number of columns for faceting (default: 2)

height

Plot height in pixels (default: 800)

width

Plot width in pixels (default: 1000)

title

Plot title (default: "Continuous Variable Effects")

base_font_size

Base font size in points for the plot theme (default: 11). Axis text and strip text will be base_font_size + 2.

Value

A plotly object

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(), fit_topic_prevalence_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_probability(), plot_word_probability(), run_neural_topics_internal(), validate_semantic_coherence()