# CFA: Confirmatory factor analysis (CFA). In bruceR: Broadly Useful Convenient and Efficient R Functions

## Description

An extension of `jmv::cfa()` and `lavaan::cfa()`.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10``` ```CFA( data, model = "A =~ a[1:5]; B =~ b[c(1,3,5)]; C =~ c1 + c2 + c3", highorder = "", orthogonal = FALSE, missing = "listwise", style = "lavaan", CI = FALSE, MI = FALSE ) ```

## Arguments

 `data` Data frame. `model` Model formula. See examples. `highorder` High-order factor. Default is `""`. `orthogonal` Default is `FALSE`. If `TRUE`, all covariances among latent variables are set to zero, and only "lavaan" style will be output. `missing` Default is `"listwise"`. Alternative is `"fiml"` (using "Full Information Maximum Likelihood" method to estimate the model). `style` `"jmv"`, `"lavaan"` (default), or both `c("jmv", "lavaan")`. If the model has high-order factors, only "lavaan" style will be output. `CI` `TRUE` or `FALSE` (default), provide confidence intervals for the model estimates. `MI` `TRUE` or `FALSE` (default), provide modification indices for the parameters not included in the model.

## Value

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

`jmv::cfa()`
`lavaan::cfa()`
 ``` 1 2 3 4 5 6 7 8 9 10``` ```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]") ```