esem (version 2.0.0)

esem_efa: Exploratory factor analysis (EFA) for ESEM

Description

Exploratory factor analysis (EFA) for ESEM

Usage

esem_efa(
  data,
  nfactors,
  fm = "ML",
  rotate = "geominT",
  scores = "regression",
  residuals = TRUE,
  Target = NULL,
  missing = TRUE
)

Value

Eigen values of the common factor solution and reporting results for EFA stage

Arguments

data

is a raw data matrix.

nfactors

is number of factors to extract

fm

is the factoring method.

rotate

is the rotation method to be used. The suggested methods are available in psych::fa()

scores

is the factor scores to be used in EFA estimation. The default scores are estimated using regression as set in "regression".

residuals

is set to FALSE by default. In case the residual matrix is required in the output, this parameter should be set to TRUE

Target

is the target rotation matrix to be used. In case no target matrix is provided, EFA proceeds with alternative approach. The list of target rotations are available from GPArotation

missing

is used with scores set to TRUE. The default is missing=TRUE which imputes missing values using either the median or the mean.

Examples

sdq_lsac<-sdq_lsac
esem_efa(data=sdq_lsac,
nfactors=5,
fm = 'ML',
rotate="geominT",
scores="regression",
residuals=TRUE,
missing=TRUE)