EFA_AVERAGE: Model averaging across different EFA methods and types

Description

EFA_AVERAGE() has been superseded by efa_average(), which is the recommended interface going forward.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

Usage

EFA_AVERAGE(
  x,
  n_factors,
  N = NA,
  method = "PAF",
  rotation = "promax",
  type = "none",
  averaging = c("mean", "median"),
  trim = 0,
  salience_threshold = 0.3,
  max_iter = 10000,
  init_comm = c("smc", "mac", "unity"),
  criterion = c(0.001),
  criterion_type = c("sum", "max_individual"),
  abs_eigen = c(TRUE),
  varimax_type = c("svd", "kaiser"),
  normalize = TRUE,
  k_promax = 2:4,
  k_simplimax = ncol(x),
  P_type = c("norm", "unnorm"),
  precision = 1e-05,
  start_method = c("psych", "factanal"),
  use = c("pairwise.complete.obs", "all.obs", "complete.obs", "everything",
    "na.or.complete"),
  cor_method = c("pearson", "spearman", "kendall", "poly", "tetra", "fiml"),
  show_progress = TRUE
)

Value

The value of efa_average(), normally a list of class c("efa_average", "EFA_AVERAGE"); see there for the components.

Arguments

x: data.frame or matrix. Dataframe or matrix of raw data or matrix with correlations. If raw data is entered, the correlation matrix is found from the data.
n_factors: numeric. Number of factors to extract.
N: numeric. The number of observations. Needs only be specified if a correlation matrix is used. If input is a correlation matrix and N = NA (default), not all fit indices can be computed.
method: character vector. Any combination of "PAF", "ML", and "ULS", the estimators to average across; passed to efa_average() as its estimator argument. Default is "PAF".
rotation: character vector. Either perform no rotation ("none"), any combination of orthogonal rotations ("varimax", "equamax", "quartimax", "geominT", "bentlerT", and "bifactorT"; using "orthogonal" runs all of these), or of oblique rotations ("promax", "oblimin", "quartimin", "simplimax", "bentlerQ", "geominQ", and "bifactorQ"; using "oblique" runs all of these). Rotation types (no rotation, orthogonal rotations, and oblique rotations) cannot be mixed. Default is "promax".
type: character vector. Any combination of "none" (default), "EFAtools", "psych", and "SPSS" can be entered. "none" allows the specification of various combinations of the arguments controlling both factor extraction methods and the rotations. The others ("EFAtools", "psych", and "SPSS") take the extraction and rotation tuning of the respective implementation: this package's default procedure, the psych package's, and SPSS's. A specific psych implementation exists for PAF, ML, varimax, and promax. The SPSS implementation exists for PAF, varimax, and promax. For details, see efa_fit(). The factor ordering is the one setting a named type does not bring here: every solution in the grid is fitted with the eigenvalue-based ordering, so that the solutions can be realigned to a common target before averaging.
averaging: character. One of "mean" (default), and "median". Controls whether the different results should be averaged using the (trimmed) mean, or the median.
trim: numeric. If averaging is set to "mean", this argument controls the trimming of extremes (for details see base::mean()). By default no trimming is done (i.e., trim = 0).
salience_threshold: numeric. The threshold to use to classify a pattern coefficient or loading as salient (i.e., substantial enough to assign it to a factor). Default is 0.3. Indicator-to-factor correspondences will be inferred based on this threshold. Note that this may not be meaningful if rotation = "none" and n_factors > 1 are used, as no simple structure is present there.
max_iter: numeric. The maximum number of iterations to perform after which the iterative PAF procedure is halted with a warning. Default is 10,000. It is only evaluated for the "PAF" solutions run under type "none": a named type brings the iteration cap that defines it ("SPSS" 25, "psych" 50, and "EFAtools" 300), and "ML" and "ULS" do not iterate this way. Note that non-converged procedures are excluded from the averaging procedure.
init_comm: character vector. Any combination of "smc", "mac", and "unity". Controls the methods to estimate the initial communalities in PAF if "none" is among the specified types. "smc" will use squared multiple correlations, "mac" will use maximum absolute correlations, "unity" will use 1s (for details see efa_fit()). Default is c("smc", "mac", "unity").
criterion: numeric vector. The convergence criterion used for PAF if "none" is among the specified types. If the change in communalities from one iteration to the next is smaller than this criterion the solution is accepted and the procedure ends. Default is 0.001.
criterion_type: character vector. Any combination of "max_individual" and "sum". Type of convergence criterion used for PAF if "none" is among the specified types. "max_individual" selects the maximum change in any of the communalities from one iteration to the next and tests it against the specified criterion. "sum" takes the difference of the sum of all communalities in one iteration and the sum of all communalities in the next iteration and tests this against the criterion (for details see efa_fit()). Default is c("sum", "max_individual").
abs_eigen: logical vector. Any combination of TRUE and FALSE. Which algorithm to use in the PAF iterations if "none" is among the specified types. If FALSE, the loadings are computed from the eigenvalues. This is also used by the psych::fa() function. If TRUE the loadings are computed with the absolute eigenvalues as done by SPSS (for details see efa_fit()). Default is TRUE.
varimax_type: character vector. Any combination of "svd" and "kaiser". The type of the varimax rotation performed if "none" is among the specified types and "varimax", "promax", "orthogonal", or "oblique" is among the specified rotations. "svd" uses singular value decomposition, as stats::varimax() does, and "kaiser" uses the varimax procedure performed in SPSS. This is the original procedure from Kaiser (1958), but with slight alterations in the varimax criterion (for details, see efa_fit() and Grieder & Steiner, 2022). Default is c("svd", "kaiser").
normalize: logical vector. Any combination of TRUE and FALSE. TRUE performs a kaiser normalization before the specified rotation(s). Default is TRUE.
k_promax: numeric vector. The power used for computing the target matrix P in the promax rotation if "none" is among the specified types and "promax" or "oblique" is among the specified rotations. Default is 2:4.
k_simplimax: numeric. The number of 'close to zero loadings' for the simplimax rotation if "simplimax" or "oblique" is among the specified rotations. Default is ncol(x), where x is the entered data. It counts loadings, so each value must be a whole number no larger than the number of loadings in the solution; a simplimax fit given anything else fails and is reported as an errored solution in the grid.
P_type: character vector. Any combination of "norm" and "unnorm". How the promax target matrix P is computed if "none" is among the specified types and "promax" or "oblique" is among the specified rotations: "unnorm" uses the unnormalized target matrix of Hendrickson and White (1964), "norm" a normalized one. This frozen argument keeps its original name; efa_average() takes the same setting as p_type. Default is c("norm", "unnorm").
precision: numeric vector. The tolerance for stopping in the rotation procedure(s). Default is 10^-5.
start_method: character vector. Any combination of "psych" and "factanal". How to specify the starting values for the optimization procedure for ML. "psych" takes the starting values specified in psych::fa(). "factanal" takes the starting values specified in the stats::factanal() function. Default is c("psych", "factanal").
use: character. Passed to stats::cor() if raw data is given as input. Default is "pairwise.complete.obs". It is ignored when cor_method = "fiml", which handles the missingness itself, so every case contributes.
cor_method: character. Correlation computed from raw data: "pearson", "spearman", or "kendall" (passed to stats::cor()), "poly" / "tetra" for polychoric / tetrachoric correlations of ordinal / binary data (a two-step estimator), or "fiml" for a two-stage full-information maximum-likelihood correlation from raw data with missing values. With "fiml" the saturated multivariate-normal mean and covariance are estimated by an EM algorithm assuming the data are missing at random and the standardized covariance is analysed, reproducing psych::corFiml() followed by psych::fa() and lavaan(missing = "two.stage"), not lavaan::efa(missing = "ml") (see efa_fit() and the details). Default is "pearson".
show_progress: logical. Whether a progress bar should be shown in the console. Default is TRUE.

Description

Usage

Value

Arguments

See Also