resid_fit: Residual fit indices

In glvmfit: Methods to Assess Generalized Latent Variable Model Fit

Description

Computes the RMR, SRMR, and CRMR.

Usage

resid_fit(
  S = NULL,
  Sigma = NULL,
  ybar = NULL,
  mu = NULL,
  lavaan_object = NULL,
  exo = TRUE
)

Arguments

S	sample covariance matrix
Sigma	model-implied covariance matrix
ybar	sample mean vector
mu	model-implied mean vector
lavaan_object	is a fitted model of class lavaan
exo	boolean argument indicating if model has exogenous covariates

Value

An S4 object

Details

S, Sigma, ybar, and mu must be of the same dimensions.

If the sum of the diagonal elements of S equals the sum of the diagonal elements of Sigma the variance component of SRMR is not included

If the sum of the sample means yhat equals the sum of the model-implied means mu the mean component of SRMR is not included

Examples

Sigma <- matrix(c(1.022, .550,  .622, .550, .928, .783, .622, .783, 1.150), 
                    nrow = 3)
S <- matrix(c(.770, .545, .515, .545, 1.003, .890, .515, .890, 1.211), 
            nrow = 3)
ybar <- c(2.516, 4.041, 5.021)
mu <- c(2.825, 3.877, 4.929)

resid_fit(S = S,  Sigma = Sigma, ybar = ybar, mu = mu)

glvmfit documentation built on Aug. 6, 2021, 9:07 a.m.