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nlpsem (version 0.3)

Linear and Nonlinear Longitudinal Process in Structural Equation Modeling Framework

Description

Provides computational tools for nonlinear longitudinal models, in particular the intrinsically nonlinear models, in four scenarios: (1) univariate longitudinal processes with growth factors, with or without covariates including time-invariant covariates (TICs) and time-varying covariates (TVCs); (2) multivariate longitudinal processes that facilitate the assessment of correlation or causation between multiple longitudinal variables; (3) multiple-group models for scenarios (1) and (2) to evaluate differences among manifested groups, and (4) longitudinal mixture models for scenarios (1) and (2), with an assumption that trajectories are from multiple latent classes. The methods implemented are introduced in Jin Liu (2023) .

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Install

install.packages('nlpsem')

Monthly Downloads

185

Version

0.3

License

GPL (>= 3.0)

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Repository

https://github.com/Veronica0206/nlpsem

Maintainer

Jin Liu

Last Published

September 12th, 2023

Functions in nlpsem (0.3)

Extract Point Estimates and Standard Errors of Latent Growth Curve Model with Time-invariant Covariates (If Any)

getLCSM.mxModel

Construct An Object of mxModel for Latent Change Score Model with Time-invariant Covariates (If Any) To Be Evaluated

Perform Bootstrap Likelihood Ratio Test for Comparing Full and Reduced Models

Compute Latent Kappa Coefficient for Agreement between Two Latent Label Sets

getLGCM.mxModel

Construct An Object of mxModel for Latent Growth Curve Model with Time-invariant Covariates (If Any) To Be Evaluated

Fit a Latent Change Score Model with a Time-invariant Covariate (If Any)

Extract Point Estimates And Standard Errors of Latent Change Score Model with Time-invariant Covariates (If Any)

Compute Initial Values for Parameters of Longitudinal Mediation Models

Fit a Latent Growth Curve Model with Time-invariant Covariate (If Any)

Derive Individual Factor Scores for Each Latent Variable Included in Model

Extract Point Estimates And Standard Errors of Multivariate Latent Growth Curve Models Or Multivariate Latent Change Score Models

Fit a Multivariate Latent Growth Curve Model or Multivariate Latent Change Score Model

Extract Point Estimates And Standard Errors of Longitudinal Mediation Model

Construct An Object of mxModel for Multivariate Latent Growth Curve Models or Multivariate Latent Change Score Models To Be Evaluated

getMGroup.initial

Compute Initial Values for Parameters of Multiple-group Models

Fit a Longitudinal Multiple Group Model

getMGroup.output

Extract Point Estimates And Standard Errors of Longitudinal Multiple Group Models

getMGroup.mxModel

Construct An Object of mxModel for Longitudinal Multiple Group Models To Be Evaluated

getMED.loadings

Get Factor Loadings for a Longitudinal Mediation Model with Specified Functional Curves

Construct An Object of mxModel for Longitudinal Mediation Models To Be Evaluated

getMIX_MED.loadings

Get Factor Loadings for a Mixture Model or Multiple Group Model with Longitudinal Mediation Model with Specified Functional Curves as Submodels

Fit a Longitudinal Mixture Model

getMIX_MULTI.addpara

Get Additional Parameters Related to Interval-specific Slopes, Interval-specific Changes and Values of Change-from- baseline for Mixture Model with Multivariate Latent Change Score Models as Submodels

Compute Initial Values for Parameters of Mixture Models

getMIX_UNI.addpara

Get Additional Parameters Related to Interval-specific Slopes, Interval-specific Changes and Values of Change-from- baseline for a Mixture Model or Multiple Group Model with Latent Change Score Models for Longitudinal Outcome

getMIX_TVC.info

Get the Time-Varying Covariate (TVC) Information for a Mixture Model or Multiple Group Model with a Time-varying Covariate

Extract Point Estimates And Standard Errors of Longitudinal Mixture Models

getMIX_UNI.loadings

Get Factor Loadings for a Mixture Model or Multiple Group Model with Univariate Longitudinal Outcome

getMIX_MULTI.loadings

Get Factor Loadings for a Mixture Model with MGM as Submodels

Construct An Object of mxModel for Longitudinal Mixture Models To Be Evaluated

Construct An Object of mxModel for Latent Growth Curve Models or Latent Change Score Models with a Time Varying Covariate and Time-invariant Covariates (If Any) To Be Evaluated

Extract Point Estimates And Standard Errors of Latent Growth Curve Model Or Latent Change Score Model with a Time-varying Covariate and Time-invariant Covariates (If Any)

getMULTI.addpara

Get Additional Parameters Related to Interval-specific Slopes, Interval-specific Changes and Values of Change-from- baseline for Multivariate Latent Change Score Models

Get the Time-Varying Covariate (TVC) Information for a One-group Longitudinal Model with Time-varying Covariate

Summarize Model Fit Statistics for Fitted Models

Compute Posterior Probabilities, Cluster Assignments, and Model Entropy for a Longitudinal Mixture Model

Fit a Longitudinal Mediation Model

getMULTI.initial

Compute Initial Values for Parameters of Multivariate Latent Growth Curve Models or Latent Change Score Models

Compute Initial Values for Parameters of Latent Growth Curve Models or Latent Change Score Models with a Time-varying Covariate and Time-invariant Covariates (if any)

getMULTI.loadings

Get Factor Loadings for a Multivariate Longitudinal Outcomes with Specified Functional Curves

getUNI.loadings

Get Factor Loadings for a Univariate Longitudinal Outcome with Specified Functional Curves

Fit a Latent Growth Curve Model or Latent Change Score Model with Time-varying and Time-invariant Covariates

Compute Initial Values for Parameters of Latent Growth Curve Models or Latent Change Score Models with Time-invariant Covariates (If Any)

Get Additional Parameters Related to Interval-specific Slopes, Interval-specific Changes and Values of Change-from-baseline for Latent Change Score Models for Longitudinal Outcome

Define Latent Change Score Models as Class-specific Models (Submodels) for a Longitudinal Multiple Group Model

Define Latent Growth Curve Models as Class-specific Models (Submodels) for a Longitudinal Mixture Model

Define Longitudinal Mediation Models as Class-specific Models (Submodels) for a Longitudinal Mixture Model

Derive Individual Growth Factors for Latent Growth Curve Models or Latent Change Score Models with Time-Invariant Covariates (If Any)

Define Latent Change Score Models as Class-specific Models (Submodels) for a Longitudinal Mixture Model

Define Latent Growth Curve Models as Class-specific Models (Submodels) for a Longitudinal Multiple Group Model

printTable,KappaOutput-method

S4 Method for printing kappa statistic with $95%$ CI and judgement for agreement.

printTable,FSOutput-method

S4 Method for printing estimated factor scores and their standard errors

Define Multivariate Latent Growth Curve Models or Multivariate Latent Change Score Models as Class-specific Models (Submodels) for a Longitudinal Mixture Model

printTable,StatsOutput-method

S4 Method for printing p values and confidence intervals (when applicable)

myMxOutput-class

Standard Methods (S4) for the package

postOutput-class

S4 Class for posterior probabilities, membership, entropy, and accuracy (when applicable)

Define a Latent Growth Curve Model or Latent Change Score Model with a Time-varying Covariate as Class-specific Models (Submodels) for a Longitudinal Mixture Model.

Define Longitudinal Mediation Models as Class-specific Models (Submodels) for a Longitudinal Multiple Group Model

Define Multivariate Latent Growth Curve Models or Multivariate Latent Change Score Models as Class-specific Models (Submodels) for a Longitudinal Multiple Group Model

Define a Latent Growth Curve Model or Latent Change Score Model with a Time-varying Covariate as Class-specific Models (Submodels) for a Longitudinal Multiple Group Model.

S4 Generic for displaying output in a table format.

show,figOutput-method

S4 Method for displaying figures.

printTable,myMxOutput-method

S4 Method for printing point estimates with standard errors

printTable,postOutput-method

S4 Method for printing posterior probabilities, membership, entropy, and accuracy.

StatsOutput-class

S4 Class for p values and confidence intervals (when specified).

KappaOutput-class

S4 Class for kappa statistic with confidence interval and judgment.

Generate Visualization for Fitted Model

ModelSummary,myMxOutput-method

S4 Method for summarizing an optimized MxModel.

S4 Class for estimated factor scores and their standard errors.

Helper Function to Generate Visualization for a Fitted Model

getEstimateStats

Calculate p-Values and Confidence Intervals of Parameters for a Fitted Model

figOutput-class

S4 Class for displaying figures

S4 Generic for summarizing an optimized MxModel.

ECLS-K (2011) Sample Dataset for Demonstration