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rsem (version 0.4.6)

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

353

Version

0.4.6

License

GPL-2

Maintainer

Zhiyong Zhang

Last Published

May 15th, 2015

Functions in rsem (0.4.6)

rsem-package

Robust Structural Equation Modeling with Missing Data and Auxiliary

rsem.fit

Calculate robust test statistics

rsem.index

rsem.index function

rsem.pattern

Obtaining missing data patterns

rsem.weight

Calculate weight for each subject

rsem.indexvc

rsem.indexvc function

rsem.Ascov

Sandwich-type covariance matrix

semdiag.read.eqs

Import of EQS outputs into R

rsem.se

Calculate robust standard errors

rsem

The main function for robust SEM analysis

Calculate the squared sum of a matrix

rsem.lavaan

Conduct robust SEM analysis using lavaan

rsem.print

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

rsem.vec

Stacking a matrix to a vector

semdiag.run.eqs

Run EQS from R

rsem.vech

Stacking lower triange of a matrix to a vector

semdiag.combinations

Enumerate the Combinations of the Elements of a Vector

rsem.indexv

rsem.indexv function

rsem.DP

Generate a duplication matrix

mardiamv25

Simulated data

rsem.emmusig

Robust mean and covariance matrix using Huber-type weight

rsem.switch.gamma

Internal function