Average marginal component effects from a conjoint_instrument()
administration: one OLS regression of profile choice on
treatment-coded dummies for all attributes simultaneously, with CR1
cluster-robust standard errors clustered by persona and 95% intervals
on the t distribution with G - 1 degrees of freedom (G personas).
Under uniform, independent profile randomization this is the standard
AMCE estimator. The regression uses the respondent-level profiles recorded
during administration, not the profiles in the initial design table.
Arguments
- responses
A
panel_administer()result whose instrument came fromconjoint_instrument().
Value
A conjoint_amce tibble: attribute, level, estimate, std_error,
ci_lo, ci_hi. Baseline levels (the first level present, in the
design's order) appear with estimate 0 and std_error = NA, so the
table feeds the familiar conjoint plot directly. The ordinary columns
n_profiles, n_respondents, n_dropped_na, and
n_execution_failures record the profile rows used, the respondents
administered, missing task responses dropped, and failed executions.
References
Hainmueller, Jens, Daniel J. Hopkins, and Teppei Yamamoto (2014). "Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments." Political Analysis 22(1), 1-30.
Examples
if (FALSE) { # \dontrun{
set.seed(110)
panel <- panel_from_margins(list(group = c(A = .5, B = .5)), n = 6)
design <- conjoint_design(
list(color = c("blue", "red"), cost = c("low", "high")),
n_tasks = 6)
instrument <- conjoint_instrument(design)
cfg <- LLMR::llm_config("groq", "openai/gpt-oss-20b")
r <- panel_administer(panel, instrument, cfg)
conjoint_amce(r)
} # }