N_FACTORS: Various factor retention criteria

Description

N_FACTORS() has been superseded by efa_retain(), which is the recommended interface going forward.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

Usage

N_FACTORS(
  x,
  criteria = c("CD", "EKC", "HULL", "MAP", "NEST", "PARALLEL"),
  suitability = TRUE,
  N = NA,
  use = c("pairwise.complete.obs", "all.obs", "complete.obs", "everything",
    "na.or.complete"),
  cor_method = c("pearson", "spearman", "kendall", "poly", "tetra"),
  n_factors_max = NA,
  N_pop = 10000,
  N_samples = 500,
  alpha = 0.3,
  max_iter_CD = 50,
  n_fac_theor = NA,
  method = c("ML", "PAF", "ULS"),
  gof = c("CAF", "CFI", "RMSEA"),
  eigen_type_HULL = c("SMC", "PCA", "EFA"),
  eigen_type_other = c("SMC"),
  n_factors = 1,
  n_datasets = 1000,
  percent = 95,
  decision_rule = c("means", "percentile", "crawford"),
  ekc_type = lifecycle::deprecated(),
  n_datasets_nest = 1000,
  alpha_nest = 0.05,
  show_progress = FALSE,
  ...
)

Value

A list of class c("efa_retain", "N_FACTORS"), identical to the value of efa_retain(); see there for the components.

Arguments

x: data.frame or matrix. Raw data, or a correlation matrix. If "CD" is included as a criterion, x must be raw data.
criteria: character. A vector with the factor retention methods to perform. Possible inputs are: "CD", "EKC", "HULL", "KGC", "MAP", "NEST", "PARALLEL", "SCREE", and "SMT" (see the details in efa_retain()). By default, a subset of often used, well-performing methods are performed.
suitability: logical. Whether the data should be checked for suitability for factor analysis using Bartlett's test of sphericity and the Kaiser-Meyer-Olkin criterion (see details). Default is TRUE.
N: numeric. The number of observations. Only needed if x is a correlation matrix.
use: character. Passed to stats::cor() if raw data is given as input. Default is "pairwise.complete.obs".
cor_method: character. Correlation computed from raw data: "pearson", "spearman", or "kendall" (passed to stats::cor()), or "poly" / "tetra" for polychoric / tetrachoric correlations (a two-step estimator). CD, PARALLEL, NEST, HULL, and SMT do not support "poly" / "tetra" and are skipped automatically if you request them together. Default is "pearson".
n_factors_max: numeric. Passed to efa_cd(). The maximum number of factors to test against. Larger numbers will increase the duration the procedure takes, but test more possible solutions. If left NA (default), the maximum number of factors for which the model is still over-identified (df > 0) is used.
N_pop: numeric. Passed to efa_cd(). Size of finite populations of comparison data. Default is 10000.
N_samples: numeric. Passed to efa_cd(). Number of samples drawn from each population. Default is 500.
alpha: numeric. Passed to efa_cd(). The alpha level used to test the significance of the improvement added by an additional factor. Default is .30.
max_iter_CD: numeric. Passed to efa_cd(). The maximum number of iterations to perform after which the iterative PAF procedure is halted. Default is 50.
n_fac_theor: numeric. Passed to efa_hull(). Theoretical number of factors to retain. The Hull method uses one plus the larger of this number and the number of factors suggested by efa_parallel() as its upper bound.
method: character. The estimator to use in the criteria that fit EFA models; passed to efa_retain() as its estimator argument. One of "ML", "PAF", or "ULS".
gof: character. Passed to efa_hull(). The goodness of fit index to use. Either "CAF", "CFI", or "RMSEA", or any combination of them. With the "PAF" estimator, only the CAF can be used as goodness of fit index. For details on the CAF, see Lorenzo-Seva, Timmerman, and Kiers (2011).
eigen_type_HULL: character. Passed to efa_parallel() in efa_hull(). What the eigenvalues in the parallel analysis are based on. One of "SMC", "PCA", or "EFA" -- different ways of estimating how much variance each indicator shares with the others before the eigenvalues are computed. "SMC" (default) uses each indicator's squared multiple correlation with the others (its diagonal value in the correlation matrix). "PCA" leaves the diagonal at 1, so each indicator's total variance -- not just the shared part -- feeds into the eigenvalues. "EFA" uses the communalities from a fitted EFA solution instead.
eigen_type_other: character. Passed to efa_kgc(), efa_scree(), and efa_parallel(). The same as eigen_type_HULL, but multiple inputs are possible here (any combination of "PCA", "SMC", and "EFA"). Default is "SMC".
n_factors: numeric. Passed to efa_parallel() (also within efa_hull()), efa_kgc(), and efa_scree(). Number of factors to extract if "EFA" is included in eigen_type_HULL or eigen_type_other. Default is 1.
n_datasets: numeric. Passed to efa_parallel() (also within efa_hull()). The number of datasets to simulate. Default is 1000.
percent: numeric. Passed to efa_parallel() (also within efa_hull()). The percentile to take from the simulated eigenvalues. Default is 95.
decision_rule: character. Passed to efa_parallel() (also within efa_hull()). Which rule to use to determine the number of factors to retain. Default is "means", which uses the average simulated eigenvalues. "percentile" uses the percentiles specified in percent. "crawford" uses the 95th percentile for the first factor and the mean afterwards (based on Crawford et al., 2010).
ekc_type: Accepted and ignored. It used to select between two ways to compute the efa_ekc() reference values. The "AM2019" reference values do not depend on the observed eigenvalues. They therefore skip the empirical correction that defines the criterion, so they are no longer computed.
n_datasets_nest: numeric. Passed to efa_nest(). The number of datasets to simulate. Default is 1000.
alpha_nest: numeric. Passed to efa_nest(). The alpha level to use. The reference values are the eigenvalues at the (1 - alpha_nest) percentile. Default is .05.
show_progress: logical. Whether a progress bar should be shown in the console. Default is FALSE.
...: Further arguments passed on to the efa_fit() fits, including the estimation tuning knobs (type, init_comm, criterion, criterion_type, abs_eigen, start_method), which are repacked into an estimate_control() object so that they tune the fits exactly as they always did. The estimator is selected with method; max_iter is taken by the max_iter_CD argument (R matches an abbreviated name against the arguments before ...) and so does not reach the fits.

Description

Usage

Value

Arguments

See Also