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lavaSearch2 (version 2.0.1)

Tools for Model Specification in the Latent Variable Framework

Description

Tools for model specification in the latent variable framework (add-on to the 'lava' package). The package contains three main functionalities: Wald tests/F-tests with improved control of the type 1 error in small samples, adjustment for multiple comparisons when searching for local dependencies, and adjustment for multiple comparisons when doing inference for multiple latent variable models.

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Install

install.packages('lavaSearch2')

Monthly Downloads

3,745

Version

2.0.1

License

GPL-3

Issues

Pull Requests

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Repository

https://github.com/bozenne/lavaSearch2

Maintainer

Brice Ozenne

Last Published

April 11th, 2023

Functions in lavaSearch2 (2.0.1)

Add a New Link Between Two Variables in a LVM

Adjust the p.values Using the Quantiles of the Max Statistic

Simulation Study Assessing Bias and Type 1 Error

autoplot.intDensTri

2D-display of the Domain Used to Compute the Integral

calcType1postSelection

Compute the Type 1 Error After Selection [EXPERIMENTAL]

Simplify a lvm object

Model Coefficients With Small Sample Correction

Check that Validity of the Dataset

autplot-modelsearch2

Display the Value of a Coefficient across the Steps.

autoplot_calibrateType1

Graphical Display of the Bias or Type 1 Error

Create Rownames for a Contrast Matrix

convFormulaCharacter

formula character conversion

Create Contrast matrix

Combine formula

Extract the Coefficient by Type

Extract the Type of Each Coefficient

Confidence Intervals With Small Sample Correction

Form all Unique Combinations Between two Vectors

Create a Mesh for the Integration

Test Linear Hypotheses With Small Sample Correction

gaussian_weight

Estimate LVM With Weights

defineCategoricalLink

Identify Categorical Links in LVM

Effects Through Pathways With Small Sample Correction

evalInParentEnv

Find Object in the Parent Environments

.dinformation2-internal

Compute the First Derivative of the Expected Information Matrix

Degree of Freedom for the Chi-Square Test

Satterthwaite Correction and Small Sample Correction

General Linear Hypothesis Testing With Small Sample Correction

Jackknife iid Decomposition from Model Object

Find all New Links Between Variables

Extract Data From a Latent Variable Model

Display the i.i.d. Decomposition

Influence Function With Small Sample Correction.

Hessian With Small Sample Correction.

Identify the Endogenous Variables

information2-internal

Compute the Expected Information Matrix From the Conditional Moments

Integrate a Gaussian/Student Density over a Triangle

Extract the Model that Has Been Retains by the modelsearch2.

Extract the Links that Have Been Found by the modelsearch2.

Normalize var1 and var2

Expected Information With Small Sample Correction.

Compute Key Quantities of a Latent Variable Model

hessian2-internal

Compute the Hessian Matrix From the Conditional Moments

score2-internal

Compute the Corrected Score.

Score With Small Sample Correction

Tools for Model Specification in the Latent Variable Framework

Depreciated Method For Small Sample Correction

Leverage With Small Sample Correction.

Simulate Repeated Measurements over time

Residual Variance-Covariance Matrix With Small Sample Correction.

Latent Variable Model Summary After Small Sample Correction

Extract one Step From the Sequential Procedure

summary.modelsearch2

summary Method for modelsearch2 Objects

Response Variable of a Formula

selectRegressor

Regressor of a Formula.

Set a Link to a Value

Symmetrize a Matrix

Pre-computation for the Score

tryWithWarnings

Run an Expression and Catch Warnings and Errors

Variance-Covariance With Small Sample Correction

Inverse the Information Matrix

Convert Variable Names to Dummy Variables Names.

Find the Number of Steps Performed During the Sequential Testing

Outcome of Linear Hypothesis Testing

summary.calibrateType1

Display the Type 1 Error Rate

Data-driven Extension of a Latent Variable Model

Power of a Matrix

Residuals With Small Sample Correction.

Effective Sample Size.

transformSummaryTable

Apply Transformation to Summary Table