CFA {bruceR}

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,
  file = NULL
)

Arguments

data

Data frame.

model

Model formula. See examples.

estimator

The estimator to be used (for details, see lavaan options). Defaults to "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. Defaults to "".

orthogonal

Defaults to FALSE. If TRUE, all covariances among latent variables are set to zero.

missing

Defaults to "listwise". Alternative is "fiml" ("Full Information Maximum Likelihood").

digits

Number of decimal places of output. Defaults to 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]")