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lavaSearch2 (version 1.2.0)

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

1,807

Version

1.2.0

License

GPL-3

Issues

Pull Requests

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Repository

https://github.com/bozenne/lavaSearch2

Maintainer

Brice Ozenne

Last Published

March 16th, 2018

Functions in lavaSearch2 (1.2.0)

autoplot.intDensTri

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

Extract Empirical Estimating Functions (lvmfit Object)

Extract the Links that Have Been Found by the modelsearch2.

evalInParentEnv

Find Object in the Parent Environments

Extract one Step From the Sequential Procedure

Power of a Matrix

Merge two modelsearch Objects

selectRegressor

Regressor of a Formula.

Response Variable of a Formula

Add a New Link Between Two Variables in a LVM

Test Linear Hypotheses with small sample correction

Compute Bias Corrected Quantities.

Compare Methods to Identify Missing Local Dependencies in a LVM

getVarCov2-internal

Reconstruct the Marginal Variance Covariance Matrix from a nlme Model

Reconstruct the Marginal Variance Covariance Matrix from a nlme Model

Extract the Type of Each Coefficient

Jackknife iid Decomposition from Model Object

Form all Unique Combinations Between two Vectors

information2-internal

Compute the Expected Information Matrix From the Conditional Moments

conditionalMoment

Prepare the Computation of score2

Set a Link to a Value

Create Rownames for a Contrast Matrix

getCluster2-internal

Reconstruct the Cluster Variable from a nlme Model

Pre-computation for the Score

Symmetrize a Matrix

tryWithWarnings

Run an Expression and Catch Warnings and Errors

Check that Validity of the Dataset

calcType1postSelection

Compute the Type 1 Error After Selection

getIndexOmega2-internal

Extract the name of the endogenous variables

Create Contrast matrix

Create a Mesh for the Integration

Extract Leverage Values

Tools for Model Specification in the Latent Variable Framework

score2-internal

Compute the Corrected Score.

Extract Data From a Model

Extract The Individual Score

Check Arguments of a function.

Convert Variable Names to Dummy Variables Names.

Find all New Links Between Variables

Export Mean and Variance Coefficients

Extract the Coefficient by Type

dInformation2-internal

Compute the First Derivative of the Expected Information Matrix

General Linear Hypothesis

defineCategoricalLink

Identify Categorical Links in LVM

Extract corrected i.i.d. decomposition

Normalize var1 and var2

Data-driven Extension of a Latent Variable Model

Integrate a Gaussian/Student Density over a Triangle

Extract Corrected Residuals

Testing the Relevance of Additional Links Using a Likelihood Ratio Test

Compute the Derivative of the Information Matrix

Testing the Relevance of Additional Links Using the Max Statistic

summary.modelsearch2

summary Method for modelsearch2 Objects

Summary with Small Sample Correction

Find the Number of Steps Performed During the Sequential Testing

Extract the Variance Covariance Matrix of the Model Parameters

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