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lavaSearch2 (version 2.0.3)

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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Version

Install

install.packages('lavaSearch2')

Monthly Downloads

2,706

Version

2.0.3

License

GPL-3

Issues

Pull Requests

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Repository

https://github.com/bozenne/lavaSearch2

Maintainer

Brice Ozenne

Last Published

February 23rd, 2024

Functions in lavaSearch2 (2.0.3)

Create Contrast matrix

Combine formula

Confidence Intervals With Small Sample Correction

convFormulaCharacter

formula character conversion

Extract the Type of Each Coefficient

Extract Data From a Latent Variable Model

Create Rownames for a Contrast Matrix

Test Linear Hypotheses With Small Sample Correction

Create a Mesh for the Integration

Effects Through Pathways With Small Sample Correction

.dinformation2-internal

Compute the First Derivative of the Expected Information Matrix

evalInParentEnv

Find Object in the Parent Environments

Find all New Links Between Variables

Jackknife iid Decomposition from Model Object

Display the i.i.d. Decomposition

Satterthwaite Correction and Small Sample Correction

Identify the Endogenous Variables

gaussian_weight

Estimate LVM With Weights

Extract one Step From the Sequential Procedure

Extract the Links that Have Been Found by the modelsearch2.

Extract the Model that Has Been Retains by the modelsearch2.

Degree of Freedom for the Chi-Square Test

Residual Variance-Covariance Matrix With Small Sample Correction.

Power of a Matrix

Data-driven Extension of a Latent Variable Model

defineCategoricalLink

Identify Categorical Links in LVM

Hessian With Small Sample Correction.

Set a Link to a Value

Pre-computation for the Score

Normalize var1 and var2

Influence Function With Small Sample Correction.

Inverse the Information Matrix

Integrate a Gaussian/Student Density over a Triangle

Latent Variable Model Summary After Small Sample Correction

Depreciated Method For Small Sample Correction

selectRegressor

Regressor of a Formula.

Variance-Covariance With Small Sample Correction

Response Variable of a Formula

Simulate Repeated Measurements over time

summary.modelsearch2

summary Method for modelsearch2 Objects

transformSummaryTable

Apply Transformation to Summary Table

Symmetrize a Matrix

Effective Sample Size.

information2-internal

Compute the Expected Information Matrix From the Conditional Moments

Expected Information With Small Sample Correction.

Tools for Model Specification in the Latent Variable Framework

General Linear Hypothesis Testing With Small Sample Correction

Leverage With Small Sample Correction.

Convert Variable Names to Dummy Variables Names.

Residuals With Small Sample Correction.

tryWithWarnings

Run an Expression and Catch Warnings and Errors

hessian2-internal

Compute the Hessian Matrix From the Conditional Moments

score2-internal

Compute the Corrected Score.

Compute Key Quantities of a Latent Variable Model

Find the Number of Steps Performed During the Sequential Testing

summary.calibrateType1

Display the Type 1 Error Rate

Score With Small Sample Correction

Outcome of Linear Hypothesis Testing

calcType1postSelection

Compute the Type 1 Error After Selection [EXPERIMENTAL]

autoplot_calibrateType1

Graphical Display of the Bias or Type 1 Error

Model Coefficients With Small Sample Correction

Simulation Study Assessing Bias and Type 1 Error

autplot-modelsearch2

Display the Value of a Coefficient across the Steps.

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

Simplify a lvm object

autoplot.intDensTri

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

Check that Validity of the Dataset

Add a New Link Between Two Variables in a LVM

Form all Unique Combinations Between two Vectors

Extract the Coefficient by Type