esem_c {esem}

Exploratory Structural Equiation Modeling ESEM (ESEM)

Description

Exploratory Structural Equiation Modeling ESEM (ESEM)

Usage

esem_c(
  data,
  nfactors,
  fm = "ML",
  rotate = "geominT",
  scores = "regression",
  residuals = TRUE,
  Target = NULL,
  missing = TRUE,
  mimic = c("MPlus"),
  std.lv = TRUE,
  ordered = TRUE
)

Arguments

data

is a raw data matrix.

nfactors

is number of factors to extract

fm

is factoring method to be used in factor estimation. The suggested methods are available in psych::fa()

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.

mimic

allows to mimic the final output results (i.e. CFA stage) to MPLUS to allow the user to compare the output.

std.lv

is set to TRUE by default to provide standardized latent variables.

ordered

is set to TRUE by default to allow the use of categorical variables.

Value

An object of class lavaan::lavaan-class, for which several methods are available, including a summary method.