`r lifecycle::badge("deprecated")`
`mrfSampler()` was renamed to [simulate_mrf()] as of bgms 0.1.6.3 to follow the package's naming conventions.
mrfSampler(
num_states,
num_variables,
num_categories,
pairwise,
main,
variable_type = "ordinal",
baseline_category,
iter = 1000,
seed = NULL
)A matrix of simulated observations (see [simulate_mrf()]).
The number of states of the ordinal MRF to be generated.
The number of variables in the ordinal MRF.
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.
A symmetric num_variables by num_variables matrix of
pairwise interactions. Only its off-diagonal elements are used.
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.
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'' and
``blume-capel''. Binary variables are automatically treated as ``ordinal''.
Defaults to variable_type = "ordinal".
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]).
The number of iterations used by the Gibbs sampler.
The function provides the last state of the Gibbs sampler as output. By
default set to 1e3.
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).
[simulate_mrf()] for the current function.