Among the most important decisions for an exploratory factor analysis (EFA) is the choice of the number of factors to retain. Several factor retention criteria have been developed for this. With this function, various factor retention criteria can be performed simultaneously. Additionally, the data can be checked for their suitability for factor analysis.
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,
...
)A list of class N_FACTORS containing
A list with the results from BARTLETT() and
KMO() (bartlett and kmo), or NULL if
suitability = FALSE.
A named list with one efa_retention object per factor
retention criterion that was run (see, e.g., EKC()).
A named numeric vector with the suggested number of factors
per criterion and, where a criterion has several variants, per variant
(e.g. EKC_BvA2017 or PARALLEL_SMC). Criteria without a numeric
suggestion (the scree plot) are not included.
A named character vector with the criteria that were skipped
or failed and the reason, or NULL if all requested criteria ran.
A list of the settings used.
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.
character. A vector with the factor retention methods to
perform. Possible inputs are: "CD", "EKC", "HULL",
"KGC", "MAP", "NEST","PARALLEL", "SCREE", and "SMT"
(see details). By default, a subset of often used, well-performing methods are performed.
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.
numeric. The number of observations. Only needed if x is a correlation matrix.
character. Passed to stats::cor() if raw
data is given as input. Default is "pairwise.complete.obs".
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".
numeric. Passed to 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.
numeric. Passed to CD(). Size of finite populations
of comparison data. Default is 10000.
numeric. Passed to CD(). Number of samples drawn
from each population. Default is 500.
numeric. Passed to CD(). The alpha level used to test
the significance of the improvement added by an additional factor.
Default is .30.
numeric. Passed to CD(). The maximum number of
iterations to perform after which the iterative PAF procedure is halted.
Default is 50.
numeric. Passed to HULL(). Theoretical number
of factors to retain. The maximum of this number and the number of factors
suggested by PARALLEL plus one will be used in the Hull method.
character. Passed to EFA() in HULL(),
KGC(), SCREE(), PARALLEL(), and NEST(). The
estimation method to use. One of "PAF", "ULS", or "ML",
for principal axis factoring, unweighted least squares, and maximum
likelihood, respectively. In KGC(), SCREE(), and PARALLEL() it only
takes effect when the respective eigen_type includes "EFA".
character. Passed to HULL(). The goodness of fit index
to use. Either "CAF", "CFI", or "RMSEA", or any
combination of them. If method = "PAF" is used, only
the CAF can be used as goodness of fit index. For details on the CAF, see
Lorenzo-Seva, Timmerman, and Kiers (2011).
character. Passed to PARALLEL() in
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.
character. Passed to KGC(),
SCREE(), and PARALLEL(). The same as eigen_type_HULL,
but multiple inputs are possible here (any combination of "PCA", "SMC",
and "EFA"). Default is "SMC".
numeric. Passed to PARALLEL() (also within
HULL()), KGC(), and SCREE(). Number of
factors to extract if "EFA" is included in eigen_type_HULL or
eigen_type_other. Default is 1.
numeric. Passed to PARALLEL() (also within
HULL()). The number of datasets to simulate. Default is 1000.
numeric. Passed to PARALLEL() (also within
HULL()). The percentile to take from the simulated eigenvalues.
Default is 95.
character. Passed to PARALLEL() (also within
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).
character. Passed to the type argument of EKC().
Either "BvA2017" for the original implementation by Braeken and van Assen
(2017), or "AM2019" for the adapted implementation by Auerswald and Moshagen
(2019).
numeric. The number of datasets to simulate in NEST(). Default is 1000.
numeric. The alpha level to use in NEST() (i.e., 1-alpha percentile of eigenvalues is used for reference values).
logical. Whether a progress bar should be shown in the console. Default is FALSE.
Further arguments passed to EFA() in
PARALLEL() (also within HULL()) and KGC().
By default, the entered data are checked for suitability for factor analysis using the following methods (see respective documentations for details):
Bartlett's test of sphericity (see BARTLETT())
Kaiser-Meyer-Olkin criterion (see KMO())
The available factor retention criteria are the following (see respective documentations for details):
Comparison data (see CD())
Empirical Kaiser criterion (see EKC())
Hull method (see HULL())
Kaiser-Guttman criterion (see KGC())
Parallel analysis (see PARALLEL())
Next Eigenvalue Sufficiency Test, NEST (see NEST())
Scree plot (see SCREE())
Sequential chi-square model tests, RMSEA lower bound, and AIC
(see SMT())
# \donttest{
# Default criteria, with correlation matrix and fit method "ML" (where needed)
# This will throw a warning for CD, as no raw data were specified
nfac_all <- N_FACTORS(test_models$baseline$cormat, N = 500, method = "ML")
# The same as above, but without "CD"
nfac_wo_CD <- N_FACTORS(test_models$baseline$cormat, criteria = c("EKC",
"HULL", "PARALLEL", "NEST"), N = 500,
method = "ML")
# Use PAF instead of ML (this will take longer). For this, gof has
# to be set to "CAF" for the Hull method.
nfac_PAF <- N_FACTORS(test_models$baseline$cormat, criteria = c("EKC",
"HULL", "PARALLEL", "NEST"), N = 500,
method = "PAF", gof = "CAF")
# Do KGC and PARALLEL with only "PCA" type of eigenvalues
nfac_PCA <- N_FACTORS(test_models$baseline$cormat, criteria = c("EKC",
"HULL", "PARALLEL", "NEST"), N = 500,
method = "ML", eigen_type_other = "PCA")
# Use raw data, such that CD can also be performed
nfac_raw <- N_FACTORS(GRiPS_raw, method = "ML")
# }
Run the code above in your browser using DataLab