projoint: Analyze a conjoint dataset with measurement-error correction
In projoint: Conjoint Analysis with Reliability Correction and Visualization

projoint

R Documentation

Analyze a conjoint dataset with measurement-error correction

Description

Computes marginal means (MMs) or average marginal component effects (AMCEs) with correction for intra-respondent reliability (IRR, \tau). When a repeated task is present, IRR is estimated unless a fixed value is supplied. Results are returned in a structured object ready for plotting and summary.

Usage

projoint(
  .data,
  .qoi = NULL,
  .by_var = NULL,
  .structure = "choice_level",
  .estimand = "mm",
  .se_method = "analytical",
  .irr = NULL,
  .remove_ties = TRUE,
  .ignore_position = NULL,
  .n_sims = NULL,
  .n_boot = NULL,
  .weights_1 = NULL,
  .clusters_1 = NULL,
  .se_type_1 = NULL,
  .weights_2 = NULL,
  .clusters_2 = NULL,
  .se_type_2 = NULL,
  .auto_cluster = TRUE,
  .seed = NULL
)

Arguments

`.data`	A `projoint_data` created by `reshape_projoint` or `make_projoint_data`.
`.qoi`	Optional `projoint_qoi` describing the quantity of interest. If supplied, its fields override `.structure` and `.estimand`.
`.by_var`	Optional column name (character) for subgroup analysis; must be logical (TRUE/FALSE) or numeric/integer coded as 0/1. Only supported for `.structure == "profile_level"` (ignored otherwise).
`.structure`	Either `"profile_level"` or `"choice_level"` (default `"choice_level"`). Overridden by `.qoi$structure` if present.
`.estimand`	Either `"mm"` (marginal mean) or `"amce"` (average marginal component effect). Default `"mm"`. Overridden by `.qoi$estimand` if present.
`.se_method`	Standard-error method: `"analytical"` (default), `"simulation"`, or `"bootstrap"`.
`.irr`	Numeric or `NULL`. If `NULL` (default), IRR is estimated (when design allows); otherwise a fixed IRR value is used and IRR estimation is skipped.
`.remove_ties`	Logical; remove ties in choice data before estimation? Default `TRUE`.
`.ignore_position`	Logical; choice-level only. Ignore profile position (left/right)? Default `TRUE`.
`.n_sims`	Integer; required when `.se_method = "simulation"`.
`.n_boot`	Integer; required when `.se_method = "bootstrap"`.
`.weights_1`, `.clusters_1`, `.se_type_1`	Passed to `lm_robust` when estimating IRR.
`.weights_2`, `.clusters_2`, `.se_type_2`	Passed to `lm_robust` when estimating MMs/AMCEs.
`.auto_cluster`	Logical. If `TRUE` (default), automatically cluster on an `id` column when present and no `.clusters_` are supplied. Auto-clustering only occurs when the corresponding `.se_type_` is `NULL`.
`.seed`	Optional integer. Sets a temporary RNG seed for reproducible simulation/bootstrap inside the call; restores the previous RNG state on exit.

Details

Most users will pass a projoint_data object (from reshape_projoint or make_projoint_data). Advanced users may specify custom quantities via projoint_qoi; if provided, its structure and estimand override .structure and .estimand.

Valid se_type_* values depend on clustering:

Without clusters: "classical", "HC0", "HC1", "HC2", "HC3"
With clusters: "CR0", "CR1", "CR2", "stata", "none"

If NULL, estimatr defaults are used (HC2 when unclustered; CR2 when clustered).

Value

A projoint_results object (list-like) with components such as:

$estimand, $structure, $se_method, $irr, $tau
$labels: attribute/level mapping used in estimation
$estimates: a data frame of estimates with columns like att_level_choose, att_level_notchoose (if choice-level), estimate, se / std.error, conf.low, conf.high, and an estimand label such as "mm_corrected" or "amce_uncorrected".

This object is suitable for downstream use in plot.projoint_results, summary.projoint_results, and related helpers.

Examples


# Prepare example data
data(exampleData1)
outcomes <- c(paste0("choice", 1:8), "choice1_repeated_flipped")
pj <- reshape_projoint(exampleData1, outcomes)

# Choice-level QoI based on pj$labels
att <- unique(pj$labels$attribute_id)[1]
lev_ids   <- pj$labels$level_id[pj$labels$attribute_id == att]
lev_names <- sub(".*:", "", lev_ids)

q <- set_qoi(
    .structure     = "choice_level",
    .estimand      = "mm",
    .att_choose    = att,
    .lev_choose    = lev_names[2],
    .att_notchoose = att,
    .lev_notchoose = lev_names[1]
  )
  
# Choice-level, marginal means (fast example: fix IRR)
fit_choice <- projoint(
  pj,
  .qoi = q,
  .structure = "choice_level",
  .estimand  = "mm",
  .irr       = 0.80,        # skip IRR estimation for a quick example
  .se_method = "analytical"
)
head(summary(fit_choice))

# Profile-level AMCEs
fit_profile <- projoint(
  pj,
  .structure = "profile_level",
  .estimand  = "amce",
  .se_method = "analytical"
)
# Plot using the S3 plot method
p <- plot(fit_profile, .estimates = "both")
print(p)

projoint documentation built on Feb. 16, 2026, 5:10 p.m.