sample_MVG_data: Sample multivariate Gaussian exposure data.

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

Sample multivariate Gaussian exposure data.

Description

Returns a tibble of observations drawn from multivariate Gaussians, with one observation per row. Each row provides the category label and cue values. If keep.input_parameters = T then the parameters (N, mean, sigma) are also returned.

Usage

sample_MVG_data(
  Ns,
  mus,
  Sigmas,
  category.labels = NULL,
  cue.labels = NULL,
  randomize.order = F,
  keep.input_parameters = F
)

sample_MVG_data_from_model(
  Ns,
  model = NULL,
  randomize.order = F,
  keep.input_parameters = F
)

Arguments

Ns	Integer vector, with each number specifying the number of observations to be drawn from the corresponding Gaussian.
mus	List of mean vectors, each specifying the mean of a multivariate Gaussian.
category.labels	Character vector of category names, each specifying the category label of a multivariate Gaussian. If NULL (default) then Gaussians will be numbered from 1:N.
cue.labels	Character vector of cue names. If NULL (default) then the cues will be numbered cue1, cue2, ...
randomize.order	Should the order of the data be randomized? (default: FALSE) This won't affect the final outcome of NIW belief updating, but it will change the incremental updates (and thus, for example, visualizations of the update process).
keep.input_parameters	Should the parameters handed to this function be included in the output? (default: FALSE)
model	MVG, MVG_ideal_observer, NIW_belief, or NIW_ideal_adaptor object.
sigmas	List of covariance matrices, each specifying the covariance of a multivariate Gaussian.

Details

The input is expected to be lists/vectors of parameters with the n-th element of each list/vector specifying the category label, number of observations, mu, and Sigma of the n-th Gaussian.

Value

A tibble.

See Also

TBD

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