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Fit random-effects Bayesian meta-analytic CFAs with minor factors assumed.

bmasem_stage_1.Rd

A function to pool correlation matrices permitting fixed-, random-effects, and clustered-samples pooling. Correlation matrices must be complete. This will change in the near future.

Usage

bmasem_stage_1(
  sample_cov = NULL,
  sample_nobs = NULL,
  type = "re",
  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_bmasempriors(),
  show = TRUE,
  show_messages = TRUE,
  cluster = NULL,
  conditional_re = TRUE
)

Arguments

sample_cov

(list of matrices) sample covariance or correlation matrices. The rownames and/or colnames must contain the observed variable names. For now, assumes there are no missing elements in the covariance matrices.

sample_nobs

(vector of positive integer) Number of observations for each study.

type

(character) One of "fe", "re", or "dep" for fixed-effects, random-effects, and dependent-samples MASEM respectively. The "dep" argument is experimental, see details below.

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 bmasempriors-class. See new_bmasempriors 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.

cluster

An optional integer vector identifying the cluster each group belongs to. Asssume there are five groups, the first three belong to cluster 1 and the last two belong to cluster 2, then the argument would be: cluster = c(1, 1, 1, 2, 2). This feature is experimental, see details below.

conditional_re

(LOGICAL) Only relevant for analysis of correlation structures. If TRUE, sample levels of the study-level random effect; If FALSE, don't.

Value

A list containing fit indices, pooled correlation matrix and its asymptotic covariance matrix, pooled partial correlation matrix and its asymptotic covariance matrix, Stan object and data_list used to fit Stan object.

Details

When type = "dep", the user must supply the cluster IDs, see cluster parameter documentation above. However, this feature is experimental. Additionally, the cluster inputs are not validated.

References

There are no references for Rd macro \insertAllCites on this help page.

Examples