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

bmasem_stage_2.Rd

A function that takes a pooled correlation matrix object and returns a fitted SEM.

Usage

bmasem_stage_2(
  model = NULL,
  pool_fit = NULL,
  method = "normal",
  orthogonal = FALSE,
  simple_struc = 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_bmasempriors(),
  show = TRUE,
  show_messages = TRUE
)

Arguments

model

A description of the user-specified model, lavaan syntax.

pool_fit

The list returned by the bmasem_stage_1 function.

method

(character) One of "normal", "lasso", "logistic", "GDP", or "none". 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

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.

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.

References

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/.

Examples

if (FALSE) {
pool_fit_issp <- bmasem_stage_1(
  sample_cov = issp89$data, sample_nobs = issp89$n
)
model_syntax_issp <- "# latent variable definitions
F1 =~ JP1 + JP2 + JP3
F2 =~ JN1 + JN2 + JN4 + JN4
F3 =~ TD1 + TD2"
bmasem_stage_2(model_syntax_issp, pool_fit_issp)
pool_fit_norton <- bmasem_stage_1(
  sample_cov = Norton13$data, sample_nobs = Norton13$n
)
model_syntax_norton <- 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_stage_2(model_syntax_norton, pool_fit_norton, orthogonal = TRUE)
}