Fits a Latent Rank Theory model, which is a mixture model with ordered ranks and Gaussian Process smoothing on the mean profiles.
Usage
rtmb_lrt(
formula,
k = 3,
data = NULL,
rank_coords = NULL,
covariance = c("diagonal", "diagonal_equal", "full", "full_equal", "full_equal_corr"),
magnitude = NULL,
smoothing = NULL,
noise = 0.01,
prob_smoothing = FALSE,
link = c("ordered", "sequential"),
prior = prior_flat(),
y_range = NULL,
fixed = NULL,
two_stage = FALSE,
WAIC = FALSE,
...
)Arguments
- formula
A formula specifying the response variable(s).
- k
Number of ranks (mixture components).
- data
A data frame containing the variables.
- rank_coords
Optional numeric vector of coordinates for each rank. Default is 1:k.
- covariance
Covariance structure: "diagonal", "diagonal_equal", "full", "full_equal", or "full_equal_corr".
- magnitude
Signal standard deviation for the GP prior. If NULL, it is estimated.
- smoothing
Length-scale for the GP prior. If NULL, it is estimated.
- noise
Measurement noise for the GP prior (default is 0.01).
- prob_smoothing
Logical; whether to apply smoothing to the class membership probabilities.
- link
Link function for class probabilities: "ordered" or "sequential".
- prior
Prior configuration: `prior_flat()`, `prior_normal()`, `prior_weak()`, `prior_rhs()`, or `prior_ssp()`. Default is `prior_flat()`. If `y_range` is supplied with the default flat prior, the wrapper automatically switches to `prior_weak()`.
- y_range
Optional numeric vector or matrix defining the theoretical range (min, max) of response variables. Specifying this automatically enables weakly informative priors if `prior` is `prior_flat()`.
- fixed
Optional named list of fixed values for specific parameters.
- two_stage
Logical; if TRUE, estimate the latent-rank measurement model first and then estimate the rank regression with delta-method uncertainty propagation. Currently supported for `$optimize()` only.
- WAIC
Logical; if TRUE, add pointwise `log_lik` to the generate block for WAIC.
- ...
Additional arguments passed to `rtmb_model`.