get_NIW_categorization_function: Get NIW categorization function

In hlplab/MVBeliefUpdatr: Fitting, Summarizing, and Visualizing of Multivariate Gaussian Ideal Observers and Adaptors

Get NIW categorization function

Description

Returns a categorization function for the first category, based on a set of parameters for the Normal-Inverse-Wishart (NIW) distribution. ms, Ss, kappas, nus, and priors are assumed to be of the same length and sorted the same way, so that the first element of ms is corresponding to the same category as the first element of Ss, kappas, nus, and priors, etc.

Usage

get_categorization_function_from_grouped_ibbu_stanfit_draws(fit, ...)

get_categorization_function_from_NIW_ideal_adaptor(model, ...)

get_NIW_categorization_function(
  ms,
  Ss,
  kappas,
  nus,
  priors = rep(1/length(ms), length(ms)),
  lapse_rate = NULL,
  lapse_biases = rep(1/length(ms), length(ms)),
  Sigma_noise = matrix(0, nrow = if (is.null(dim(Ss[[1]]))) 1 else max(dim(Ss[[1]])),
    ncol = if (is.null(dim(Ss[[1]]))) 1 else max(dim(Ss[[1]]))),
  noise_treatment = if (is.null(Sigma_noise)) "no_noise" else "marginalize",
  logit = FALSE
)

Arguments

ms	Means of the multivariate normal distributions over category means.
Ss	Scatter matrices of the inverse Wishart distribution over category covariance matrices.
kappas	Strength of the beliefs into the distribution over category means.
nus	Strength of the beliefs into the distribution over category covariance matrices.
priors	Vector of categories' prior probabilities. (default: uniform prior over categories)
lapse_rate	A lapse rate for the categorization responses.
lapse_biases	A lapse bias for the categorization responses. (default: uniform bias over categories)
Sigma_noise	A noise matrix. (default: a 0-matrix)
noise_treatment	How should the noise specified in Sigma_noise be considered in the categorization function? For details, see get_NIW_posterior_predictive.
logit	Should the function that is returned return log-odds (TRUE) or probabilities (FALSE)? (default: TRUE)

Value

A function that takes as input cue values and returns posterior probabilities of the first category, based on the posterior predictive of the cues given the (IBBU-derived parameters for the) categories' m, S, kappa, nu, and prior, as well as the lapse rate.

See Also

TBD

hlplab/MVBeliefUpdatr documentation built on March 29, 2025, 10:42 p.m.