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EFAtools (version 1.0.0)

EFA_POOLED: Exploratory factor analysis on multiple data imputations

Description

[Superseded]

EFA_POOLED() has been superseded by efa_mi(), which is the recommended interface going forward. It remains available and unchanged so existing code keeps working.

Usage

EFA_POOLED(
  data_list,
  p = 0.05,
  target_method = c("first_target", "consensus"),
  align_unrotated = c("signed_tucker_congruence", "none", "procrustes"),
  fit_pool_method = c("D2"),
  consensus_args = list(),
  procrustes_args = list(),
  rmsea_ci_level = 0.9,
  rmsr_upper = TRUE,
  ...
)

Value

The value of efa_mi(), normally a list of class c("efa_mi", "EFA_POOLED", "efa", "EFA"); see there for the components.

Arguments

data_list

A list of length \(m\), where \(m\) is the number of imputations. Each list element is a data frame or matrix of raw data, or a correlation matrix. See argument x in efa_fit().

p

Numeric in \((0, 1)\). One minus the confidence level used for pooled Wald-type bootstrap/MI confidence intervals when bootstrap replicates are available. For example, p = .05 gives 95% intervals.

target_method

Character. How rotated solutions are aligned across imputations before pooling: "first_target" (the default) aligns every imputation to the first imputation's rotated solution, while "consensus" refines a centroid target by Generalized Procrustes Analysis (orthogonal rotations only). See Aligning solutions across imputations in Details.

align_unrotated

Character. How unrotated loadings are aligned before pooling: "signed_tucker_congruence" (the default; sign/permutation via Tucker congruence, anchored on the medoid imputation and returned in the extraction's canonical gauge), "procrustes" (orthogonal Procrustes to the first imputation), or "none". See Aligning solutions across imputations in Details.

fit_pool_method

Character. Currently only "D2" is implemented for chi-square-type fit. If no chi-square is available, only residual-based fit and descriptive quantities are returned. See Pooling the model chi-square and fit indices in Details.

consensus_args

List of additional arguments controlling the GPA-consensus iteration when target_method = "consensus". Recognised tuning parameters include the convergence tolerances tol and loss_tol, the iteration bounds min_iter and max_iter, the target-update damping alpha, and the multi-start controls multi_start and starts.

procrustes_args

List of additional arguments passed to efa_procrustes() for fixed-target alignment.

rmsea_ci_level

Numeric. Confidence level for the RMSEA CI.

rmsr_upper

Logical. If TRUE, compute RMSR from the unique off-diagonal residual correlations. If FALSE, use the full off-diagonal matrix.

...

Additional arguments passed to efa_fit() (e.g. estimator, rotation, se, n_factors, N). These select the estimator, rotation, standard-error method, and fit indices used for every imputation; see efa_fit() for the available options, their properties, and which combinations are valid.

See Also

efa_mi()