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lavaSearch2 (version 1.3.5)

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

1,807

Version

1.3.5

License

GPL-3

Issues

Pull Requests

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Repository

https://github.com/bozenne/lavaSearch2

Maintainer

Brice Ozenne

Last Published

July 17th, 2018

Functions in lavaSearch2 (1.3.5)

autoplot_calibrateType1

Graphical Display of the Bias or Type 1 Error

Extract the Links that Have Been Found by the modelsearch2.

Create a Mesh for the Integration

dInformation2-internal

Compute the First Derivative of the Expected Information Matrix

getIndexOmega2-internal

Extract the name of the endogenous variables

Export Mean and Variance Coefficients

getCluster2-internal

Reconstruct the Cluster Variable from a nlme Model

Extract one Step From the Sequential Procedure

Check that Validity of the Dataset

Extract corrected i.i.d. decomposition

Extract Data From a Model

General Linear Hypothesis

getVarCov2-internal

Reconstruct the Marginal Variance Covariance Matrix from a nlme Model

Jackknife iid Decomposition from Model Object

tryWithWarnings

Run an Expression and Catch Warnings and Errors

Reconstruct the Conditional Variance Covariance Matrix

information2-internal

Compute the Expected Information Matrix From the Conditional Moments

Power of a Matrix

Check Arguments of a function.

Find all New Links Between Variables

Extract Corrected Residuals

Merge two modelsearch Objects

score2-internal

Compute the Corrected Score.

Extract The Individual Score

selectRegressor

Regressor of a Formula.

Response Variable of a Formula

Degree of Freedom for the Robust Chi-Square Test

Extract the Coefficient by Type

Satterthwaite Correction and Small Sample Correction

Extract Empirical Estimating Functions (lvmfit Object)

Extract the Type of Each Coefficient

Convert Variable Names to Dummy Variables Names.

Simulation Study Assessing Bias and Type 1 Error

Extract the Variance Covariance Matrix of the Model Parameters

Compare Methods to Identify Missing Local Dependencies in a LVM

Test Linear Hypotheses with small sample correction

Normalize var1 and var2

calcType1postSelection

Compute the Type 1 Error After Selection

conditionalMoment

Prepare the Computation of score2

Integrate a Gaussian/Student Density over a Triangle

Testing the Relevance of Additional Links Using a Likelihood Ratio Test

Form all Unique Combinations Between two Vectors

Set a Link to a Value

summary.calibrateType1

Display the Type 1 Error Rate

summary.modelsearch2

summary Method for modelsearch2 Objects

Add a New Link Between Two Variables in a LVM

Pre-computation for the Score

Compute Bias Corrected Quantities.

evalInParentEnv

Find Object in the Parent Environments

Find the Number of Steps Performed During the Sequential Testing

Data-driven Extension of a Latent Variable Model

Summary with Small Sample Correction

Extract Leverage Values

Testing the Relevance of Additional Links Using the Max Statistic

autoplot.intDensTri

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

Create Contrast matrix

Create Rownames for a Contrast Matrix

Tools for Model Specification in the Latent Variable Framework

Symmetrize a Matrix

defineCategoricalLink

Identify Categorical Links in LVM

Degree of Freedom for the Chi-Square Test

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