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

A function to fit fixed-, random-effects, and clustered Bayesian meta-analytic CFAs with minor factors assumed. Does not yet accomodate moderators, and correlation matrices must be complete. This will change in the near future.

Usage

bmasem(
  model = NULL,
  data = NULL,
  group = NULL,
  sample_cov = NULL,
  sample_nobs = NULL,
  correlation = TRUE,
  method = "normal",
  type = "re",
  orthogonal = FALSE,
  simple_struc = TRUE,
  x_mat = NULL,
  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

model: A description of the user-specified model, lavaan syntax.
data: An optional data frame containing the observed variables used in the model.
group: An optional string identifying the grouping variable in the data object.
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.
correlation: (LOGICAL) If TRUE (default): analyze correlation matrices based on logarithm of a matrix transformation (Archakov and Hansen 2021) ; If FALSE: analyze covariance matrices methods in (Uanhoro 2023) .
method: (character) One of "normal", "lasso", "logistic", "GDP", or "none". See details below.
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.
orthogonal: (LOGICAL) If TRUE: constrain all factors orthogonal (overrides model syntax); If FALSE (default): according to model syntax.
simple_struc: (LOGICAL) Only relevant for CFAs. If TRUE (default): assume simple structure; If FALSE: estimate all cross-loadings using generalized
x_mat: (data.frame) Meta-analytic predictors. Each row contains the data for a group and each column is a variable. Column names should be labelled.
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

An object of bmasem-class

Details

CFAs assume standardized factors. Latent variable regression models are not yet implemented.

There are different methods for estimating models in this package:

normal: under belief that minor factor influences are on average zero with continuous deviations away from zero.
lasso: under belief that minor factor influences are largely zero with a small number of non-zero residual covariances.
logistic: for similar belief as normal but more readily accomodates extreme outliers.
GDP: to mimic a global-local approach, i.e. attempt to shrink near 0 residual covariances to 0 with minimal shrinking for larger residual covariances (Armagan et al. 2013) .
none: if intending to ignore the influence of minor factors.

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

Archakov I, Hansen PR (2021). “A New Parametrization of Correlation Matrices.” Econometrica, 89(4), 1699--1715. ISSN 1468-0262, doi:10.3982/ECTA16910 , https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA16910.

Armagan A, Dunson DB, Lee J (2013). “Generalized double Pareto shrinkage.” Statistica Sinica, 23(1), 119--143. ISSN 1017-0405, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3903426/.

Uanhoro JO (2023). “Hierarchical Covariance Estimation Approach to Meta-Analytic Structural Equation Modeling.” Structural Equation Modeling: A Multidisciplinary Journal, 30(4), 532--546. ISSN 1070-5511, doi:10.1080/10705511.2022.2142128 , 2023-06-19.

Examples

if (FALSE) {
model_syntax <- "# latent variable definitions
F1 =~ JP1 + JP2 + JP3
F2 =~ JN1 + JN2 + JN4 + JN4
F3 =~ TD1 + TD2"
bmasem(
  model_syntax,
  sample_cov = issp89$data, sample_nobs = issp89$n
)
model_syntax <- paste0(
  "distress =~ ", paste0("x", 1:14, collapse = " + "), "\n",
  "anxiety =~ ", paste0("x", seq(1, 14, 2), collapse = " + "), "\n",
  "depression =~ ", paste0("x", seq(2, 14, 2), collapse = " + ")
)
bmasem(
  model_syntax,
  sample_cov = Norton13$data, sample_nobs = Norton13$n, orthogonal = TRUE
)
}