Estimate topic prevalence effects from an STM model — estimate_topic

Estimates how a document-level covariate shifts topic prevalence, returning a tidy data frame of per-topic estimates with intervals.

Usage

estimate_topic_effects(
  estimates,
  variable,
  type = c("pointestimate", "continuous"),
  method = c("stm", "beta"),
  interval = NULL,
  model = NULL,
  documents = NULL,
  npoints = 100,
  nsims = 100,
  ci = 0.95
)

Arguments

estimates: An estimateEffect object from stm::estimateEffect.
variable: Name of the covariate to evaluate.
type: "pointestimate" for a categorical covariate or "continuous" for a numeric one.
method: "stm" (method of composition, default) or "beta" (bounded per-topic Beta regression).
interval: "eti" equal-tailed (default for "stm") or "hpd" highest-posterior-density (default for "beta").
model: Fitted stm model; required for method = "beta".
documents: Document list passed to stm; required for method = "beta".
npoints: Grid resolution for a continuous covariate (default 100).
nsims: Posterior draws (default 100; 25 for method = "beta").
ci: Interval width (default 0.95).

Value

A data frame with columns topic, value, proportion, lower, and upper.