Fit Bayesian path analysis models — minorbpa • minorbsem

Fit Bayesian path anlaysis models with tests of conditional independence.

Usage

minorbpa(
  model = NULL,
  data = NULL,
  sample_cov = NULL,
  sample_nobs = NULL,
  data_list = NULL,
  method = "normal",
  orthogonal = FALSE,
  correlation = FALSE,
  centered = TRUE,
  seed = 12345,
  warmup = 1000,
  sampling = 1000,
  refresh = (warmup + sampling)/10,
  adapt_delta = 0.9,
  max_treedepth = 10,
  chains = 3,
  ncores = max(parallel::detectCores() - 2, 1),
  priors = new_mbsempriors(),
  show = TRUE,
  show_messages = TRUE,
  compute_ll = FALSE,
  acov_mat = NULL,
  ret_data_list = FALSE
)

Arguments

model: A description of the user-specified model, lavaan syntax.
data: An optional data frame containing the observed variables used in the model.
sample_cov: (matrix) sample variance-covariance matrix. The rownames and/or colnames must contain the observed variable names.
sample_nobs: (positive integer) Number of observations if the full data frame is missing and only sample covariance matrix is given.
data_list: (list) A modified version of the data_list returned by minorbsem. Can be used to modify specific priors, see example below.
method: (character) One of "normal", "lasso", "logistic", "GDP", "WB", "WB-cond", "WW", or "none". See details below.
orthogonal: (logical) constrain factors orthogonal, must be TRUE to fit bifactor models.
correlation: (LOGICAL) If TRUE: perform correlation structure analysis based on logarithm of a matrix transformation archakov_new_2021minorbsem; If FALSE (default): perform covariance structure analysis.
centered: (LOGICAL) Only relevant for WB-cond and WW methods when correlation = TRUE. If TRUE (default): Use a centered parameterization; If FALSE: Use a non-centered parameterization.
seed: (positive integer) seed, set to obtain replicable results.
warmup: (positive integer) The number of warmup iterations to run per chain.
sampling: (positive integer) The number of post-warmup iterations to run per chain, retained for inference.
refresh: (positive integer) How often to print the status of the sampler.
adapt_delta: (real in (0, 1)) Increase to resolve divergent transitions.
max_treedepth: (positive integer) Increase to resolve problems with maximum tree depth.
chains: (positive integer) The number of Markov chains to run.
ncores: (positive integer) The number of chains to run in parallel.
priors: An object of mbsempriors-class. See new_mbsempriors for more information.
show: (Logical) If TRUE, show table of results, if FALSE, do not show table of results. As an example, use FALSE for simulation studies.
show_messages: (Logical) If TRUE, show messages from Stan sampler, if FALSE, hide messages.
compute_ll: (Logical) If TRUE, compute log-likelihood, if FALSE, do not. This may be useful for cross-validation. This argument is ignored when: (i) the full dataset is not provided; (ii) the method is WB, use WB-cond instead.
acov_mat: (Optional) Asymptotic variance matrix of lower triangular half (column-order) of the correlation matrix to be used for correlation structure analysis. This parameter is useful if importing polychoric or meta-analytic SEM pooled correlation matrix.
ret_data_list: (LOGICAL) If TRUE, returns the data_list and prior objects, see example. If FALSE (default), fits the model given user inputs.

Value

An object of mbsem-class

Examples

if (FALSE) { # \dontrun{
minorbpa("x3 ~ x1 + x2\n x4 ~ x3 + x1", HS)
} # }