View source: R/get-info-from-model.R
| get_categorization_from_MVG_ideal_observer | R Documentation | 
Categorize a single observation based a model. The decision rule can be specified to be either the criterion choice rule, proportional matching (Luce's choice rule), or the sampling-based interpretation of Luce's choice rule.
get_categorization_from_MVG_ideal_observer(
  x,
  model,
  decision_rule = "sampling",
  noise_treatment = if (decision_rule == "sampling") "sample" else
    infer_default_noise_treatment(model$Sigma_noise),
  lapse_treatment = if (decision_rule == "sampling") "sample" else "marginalize",
  simplify = F
)
get_categorization_from_model(model, decision_rule = "sampling", ...)
x | 
 A vector of observations.  | 
model | 
 A model object.  | 
decision_rule | 
 Must be one of "criterion", "proportional", or "sampling". (default: "sampling")  | 
noise_treatment | 
 Determines whether and how multivariate Gaussian noise is added to the input.
See   | 
lapse_treatment | 
 Determines whether and how lapses will be treated. Can be "no_lapses", "sample" or "marginalize". If "sample", whether a trial is lapsing or not will be sampled for each observations. If a trial is sampled to be a lapsing trial the lapse biases are used as the posterior for that trial. If "marginalize", the posterior probability will be adjusted based on the lapse formula lapse_rate lapse_bias + (1 - lapse_rate) posterior probability from perceptual model. (default: "sample" if decision_rule is "sample"; "marginalize" otherwise).  | 
simplify | 
 Should the output be simplified, and just the label of the selected category be returned? This option is only available for the criterion and sampling decision rules. (default: 'FALSE')  | 
Either a tibble of observations with posterior probabilities for each category (in long format), or a character vector indicating the chosen category in the same order as the observations in x (if simplify = 'TRUE').
TBD
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