Sample observations from the ordinal MRF

mrfSampler() was renamed to simulate_mrf() as of bgms 0.1.6.3 to follow the package's naming conventions.

Usage

mrfSampler(
  num_states,
  num_variables,
  num_categories,
  pairwise,
  main,
  variable_type = "ordinal",
  baseline_category,
  iter = 1000,
  seed = NULL
)

Arguments

num_states

The number of observations to be generated.

num_variables

The number of variables in the MRF.

num_categories

Either a positive integer or a vector of positive integers of length num_variables. The number of response categories on top of the base category: num_categories = 1 generates binary states. Only used for ordinal and Blume-Capel variables; ignored when variable_type = "continuous".

pairwise

A symmetric num_variables by num_variables matrix. For ordinal and Blume-Capel variables, this contains the pairwise interaction parameters; only the off-diagonal elements are used. For continuous variables, this is the precision matrix \(\Omega\) (including diagonal) and must be positive definite.

main

For ordinal and Blume-Capel variables: a num_variables by max(num_categories) matrix of category thresholds. The elements in row i indicate the thresholds of variable i. If num_categories is a vector, only the first num_categories[i] elements are used in row i. If the Blume-Capel model is used for the category thresholds for variable i, then row i requires two values (details below); the first is \(\alpha\), the linear contribution of the Blume-Capel model and the second is \(\beta\), the quadratic contribution. For continuous variables: a numeric vector of length num_variables containing the means \(\mu\) for each variable. Defaults to zeros if not supplied or if all values are zero.

variable_type

What kind of variables are simulated? Can be a single character string specifying the variable type of all p variables at once or a vector of character strings of length p specifying the type for each variable separately. Currently, bgm supports "ordinal", "blume-capel", and "continuous". Binary variables are automatically treated as "ordinal". Ordinal and Blume-Capel variables can be mixed freely, but continuous variables cannot be mixed with ordinal or Blume-Capel variables. When variable_type = "continuous", the function simulates from a Gaussian graphical model. Defaults to variable_type = "ordinal".

baseline_category

An integer vector of length num_variables specifying the baseline_category category that is used for the Blume-Capel model (details below). Can be any integer value between 0 and num_categories (or num_categories[i]).

iter

The number of iterations used by the Gibbs sampler (ordinal/Blume-Capel variables only). The function provides the last state of the Gibbs sampler as output. Ignored for continuous variables. By default set to 1e3.

seed

Optional integer seed for reproducibility. If NULL, a seed is generated from R's random number generator (so set.seed() can be used before calling this function).

Value

A matrix of simulated observations (see simulate_mrf()).

See also

simulate_mrf() for the current function.