CFA: Confirmatory Factor Analysis (CFA).

In bruceR: Broadly Useful Convenient and Efficient R Functions

Confirmatory Factor Analysis (CFA).

Description

An extension of lavaan::cfa().

Usage

CFA(
  data,
  model = "A =~ a[1:5]; B =~ b[c(1,3,5)]; C =~ c1 + c2 + c3",
  estimator = "ML",
  highorder = "",
  orthogonal = FALSE,
  missing = "listwise",
  digits = 3,
  nsmall = digits,
  file = NULL
)

Arguments

data	Data frame.
model	Model formula. See examples.
estimator	The estimator to be used (for details, see lavaan options). Default is "ML". Can be one of the following: "ML" Maximum Likelihood (can be extended to "MLM", "MLMV", "MLMVS", "MLF", or "MLR" for robust standard errors and robust test statistics) "GLS" Generalized Least Squares "WLS" Weighted Least Squares "ULS" Unweighted Least Squares "DWLS" Diagonally Weighted Least Squares "DLS" Distributionally-weighted Least Squares
highorder	High-order factor. Default is "".
orthogonal	Default is FALSE. If TRUE, all covariances among latent variables are set to zero.
missing	Default is "listwise". Alternative is "fiml" ("Full Information Maximum Likelihood").
digits, nsmall	Number of decimal places of output. Default is 3.
file	File name of MS Word (.doc).

Value

A list of results returned by lavaan::cfa().

See Also

Alpha, EFA, lavaan_summary

Examples

data.cfa=lavaan::HolzingerSwineford1939
CFA(data.cfa, "Visual =~ x[1:3]; Textual =~ x[c(4,5,6)]; Speed =~ x7 + x8 + x9")
CFA(data.cfa, model="
    Visual =~ x[1:3]
    Textual =~ x[c(4,5,6)]
    Speed =~ x7 + x8 + x9
    ", highorder="Ability")

data.bfi = na.omit(psych::bfi)
CFA(data.bfi, "E =~ E[1:5]; A =~ A[1:5]; C =~ C[1:5]; N =~ N[1:5]; O =~ O[1:5]")

bruceR documentation built on March 7, 2023, 5:41 p.m.