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N_FACTORS() has been superseded by efa_retain(), which is the recommended interface going forward.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 = c("BvA2017"),
  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. Dataframe or matrix of raw data or matrix with correlations. 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 the 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 with no empty-cell continuity correction). CD, PARALLEL, NEST, and HULL compare against simulated continuous data, and SMT relies on a normal-theory chi-square test; none of these support "poly" / "tetra", so they are skipped in that case. 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 maximum of this number and the number of factors suggested by efa_parallel() plus one will be used in the Hull method.
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(). On what the eigenvalues should be found in the parallel analysis. Can be one of "SMC", "PCA", or "EFA". If using "SMC" (default), the diagonal of the correlation matrices is replaced by the squared multiple correlations (SMCs) of the indicators. If using "PCA", the diagonal values of the correlation matrices are left to be 1. If using "EFA", eigenvalues are found on the correlation matrices with the final communalities of an EFA solution as diagonal.
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 will use 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: character. Passed to the type argument of efa_ekc(). Either "BvA2017" for the original implementation by Braeken and van Assen (2017), or "AM2019" for the adapted implementation by Auerswald and Moshagen (2019).
n_datasets_nest: numeric. The number of datasets to simulate in efa_nest(). Default is 1000.
alpha_nest: numeric. The alpha level to use in efa_nest() (i.e., 1-alpha percentile of eigenvalues is used for reference values).
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