rsem (version 0.5.1)

Robust Structural Equation Modeling with Missing Data and Auxiliary Variables

Description

A robust procedure is implemented to estimate means and covariance matrix of multiple variables with missing data using Huber weight and then to estimate a structural equation model.

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Version

Install

install.packages('rsem')

Monthly Downloads

482

Version

0.5.1

License

GPL-2

Maintainer

Zhiyong Zhang

Last Published

August 26th, 2023

Functions in rsem (0.5.1)

semdiag.combinations

Enumerate the Combinations of the Elements of a Vector

rsem.weight

Calculate weight for each subject

rsem.vech

Stacking lower triange of a matrix to a vector

rsem.indexvc

rsem.indexvc function

semdiag.read.eqs

Import of EQS outputs into R

semdiag.run.eqs

Run EQS from R

rsem.lavaan

Conduct robust SEM analysis using lavaan

rsem.fit

Calculate robust test statistics

rsem

The main function for robust SEM analysis

rsem.Ascov

Sandwich-type covariance matrix

rsem.se

Calculate robust standard errors

rsem.gname

Internal function

rsem.vec

Stacking a matrix to a vector

rsem.emmusig

Robust mean and covariance matrix using Huber-type weight

Generate a duplication matrix

Calculate the squared sum of a matrix

rsem-package

Robust Structural Equation Modeling with Missing Data and Auxiliary Variables

rsem.pattern

Obtaining missing data patterns

rsem.indexv

rsem.indexv function

rsem.print

Organize the output for Lavaan with robust s.e. and test statistics