Confirmatory Factor Analysis After Correlation Synthesis

Convenience wrapper around path_model for latent (=~) model syntax using synthesized correlation matrices.

Usage

cfa_from_synthesis(
  mars_object,
  model,
  num_obs = NULL,
  method_null = "sem",
  synthesis_method = c("model", "average", "weighted"),
  synthesis_transform = c("none", "fisher_z"),
  missing_corr = c("available", "pairwise", "em"),
  attenuation = c("none", "correct"),
  reliability = NULL,
  reliability_missing = c("error", "skip", "impute_mean", "assume_1"),
  pd_adjust = c("none", "eigen_clip", "nearpd"),
  pd_tol = 1e-08,
  ...
)

Arguments

mars_object: A fitted object returned by mars.
model: Lavaan-style latent model syntax.
num_obs: Optional sample size.
method_null: Null model method passed to path_model.
synthesis_method: Correlation synthesis method: "model", "average", or "weighted".
synthesis_transform: Optional pooling transform for "average"/"weighted": "none" or "fisher_z".
missing_corr: Missing-correlation handling for synthesized matrices. One of "available", "pairwise", or "em".
attenuation: Attenuation-correction mode for synthesized correlations. One of "none" or "correct".
reliability: Reliability input used when attenuation = "correct".
reliability_missing: Strategy for missing reliability values. One of "error", "skip", "impute_mean", or "assume_1".
pd_adjust: Positive-definite repair for synthesized correlations: "none", "eigen_clip", or "nearpd".
pd_tol: Minimum eigenvalue tolerance used by pd_adjust = "eigen_clip".
...: Additional arguments passed to path_model.

Value

A path object with additional class cfa_mars.

Examples

if (FALSE) { # \dontrun{
fit <- mars(
  data = becker09,
  studyID = "ID",
  effectID = "numID",
  sample_size = "N",
  effectsize_type = "cor",
  varcov_type = "weighted",
  variable_names = c(
    "Cognitive_Performance",
    "Somatic_Performance",
    "Selfconfidence_Performance",
    "Somatic_Cognitive",
    "Selfconfidence_Cognitive",
    "Selfconfidence_Somatic"
  )
)
cfa_from_synthesis(fit, "Performance =~ Cognitive + Somatic + Selfconfidence")
} # }