Package 'glvmfit'

glvmfit: Methods to Assess Generalized Latent Variable Model Fit

Description

Provides residual global fit indices for generalized latent variable models.

---

Subset of 221 children from the 1979 National Longitudinal Survey of Youth

Description

These data are wave-based such that each child’s Peabody Individual Assessment Test (PIAT) reading and antisocial behavior scores were measured at four waves in two-year intervals.

Usage

nlsy

Format

A data frame with 221 rows and 14 variables:

id

Unique identifier

mom_age

Mother’s age when the child was born

home_cog

Measure of cognitive stimulation provided at home

home_emo

Measure of emotional support provided at home

read0

PIAT reading score at wave 1

read1

PIAT reading score at wave 2

read2

PIAT reading score at wave 3

read3

PIAT reading score at wave 4

anti0

Antisocial behavior score at wave 1

anti1

Antisocial behavior score at wave 2

anti2

Antisocial behavior score at wave 3

anti3

Antisocial behavior score at wave 4

Source

https://github.com/MultiLevelAnalysis/Datasets-third-edition-Multilevel-book/tree/master/chapter%205/Curran

---

Residual fit indices

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)

---

An S4 class to represent a residual fit indices.

Description

An S4 class to represent a residual fit indices.

Slots

type

A length-one numeric vector

resid

A length-one numeric vector

ssr

A length-one numeric vector

size

A length-one numeric vector

index

---

An S4 class to represent the set of residual fit indices

Description

An S4 class to represent the set of residual fit indices

Usage

details(object, comp = c("Total", "Covariance", "Variance", "Mean", "Total"))

## S4 method for signature 'ResidualFitIndices'
details(object, comp = c("Total", "Covariance", "Variance", "Mean", "Total"))

Arguments

object	R object of type ResidualFitIndices.
comp	Character indicating the components to include.

Slots

sampleMoments

impliedMoments

RMR

SRMR

CRMR

Note

comp can be "Total" for overall fit indices, "Cov" for covariance elements (off diagonals), "Var" for variance components (diagonal), and "Mean" means.

---