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EFAtools (version 1.1.0)

SCREE: Scree plot

Description

[Superseded]

SCREE() has been superseded by efa_scree(), which is the recommended interface going forward.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

Usage

SCREE(
  x,
  eigen_type = c("PCA", "SMC", "EFA"),
  use = c("pairwise.complete.obs", "all.obs", "complete.obs", "everything",
    "na.or.complete"),
  cor_method = c("pearson", "spearman", "kendall", "poly", "tetra"),
  n_factors = 1,
  ...
)

Value

An object of class efa_retention, identical to the value of efa_scree(); see there for the components.

Arguments

x

data.frame or matrix. Dataframe or matrix of raw data or matrix with correlations.

eigen_type

character. On what the eigenvalues should be found. Can be either "PCA", "SMC", or "EFA", or some combination of them. If using "PCA", the diagonal values of the correlation matrices are left to be 1. If using "SMC", the diagonal of the correlation matrices is replaced by the squared multiple correlations (SMCs) of the indicators. If using "EFA", eigenvalues are found on the correlation matrices with the final communalities of an exploratory factor analysis solution (default is principal axis factoring extracting 1 factor) as diagonal. Default is c("PCA", "SMC", "EFA"), i.e. all three; "EFA" is the only one that fits a model.

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 of ordinal / binary data (a two-step estimator). Default is "pearson".

n_factors

numeric. Number of factors to extract if "EFA" is included in eigen_type. Default is 1.

...

Further arguments passed on to the efa_fit() fit. For example, estimator, to change the estimator (PAF is default), or one of the estimation tuning knobs (type, init_comm, criterion, criterion_type, max_iter, abs_eigen, start_method), which are repacked into an estimate_control() object so that they tune the fit exactly as they always did.

See Also

efa_scree()