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lavaSearch2 (version 1.5.6)

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

2,706

Version

1.5.6

License

GPL-3

Issues

Pull Requests

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Repository

https://github.com/bozenne/lavaSearch2

Maintainer

Brice Ozenne

Last Published

July 31st, 2020

Functions in lavaSearch2 (1.5.6)

Add a New Link Between Two Variables in a LVM

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

calcType1postSelection

Compute the Type 1 Error After Selection [EXPERIMENTAL]

Export Mean and Variance Coefficients

autoplot.intDensTri

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

Simulation Study Assessing Bias and Type 1 Error

autoplot_calibrateType1

Graphical Display of the Bias or Type 1 Error

Check that Validity of the Dataset

Create Contrast matrix

Find all New Links Between Variables

Extract Leverage Values

Extract corrected i.i.d. decomposition

Jackknife iid Decomposition from Model Object

Extract Data From a Model

Tools for Model Specification in the Latent Variable Framework

Create a Mesh for the Integration

defineCategoricalLink

Identify Categorical Links in LVM

dInformation2-internal

Compute the First Derivative of the Expected Information Matrix

Extract the Coefficient by Type

Extract one Step From the Sequential Procedure

convFormulaCharacter

formula character conversion

Degree of Freedom for the Chi-Square Test

Create Rownames for a Contrast Matrix

Check Arguments of a function.

getVarCov2-internal

Reconstruct the Marginal Variance Covariance Matrix from a nlme Model

Convert Variable Names to Dummy Variables Names.

Test Linear Hypotheses with small sample correction

Extract the Individual Score

Form all Unique Combinations Between two Vectors

selectRegressor

Regressor of a Formula.

Combine formula

Extract the Links that Have Been Found by the modelsearch2.

Extract the Type of Each Coefficient

Effects from a fitted model

Extract the Model that Has Been Retains by the modelsearch2.

Data-driven Extension of a Latent Variable Model

Extract Empirical Estimating Functions (lvmfit Object)

Power of a Matrix

conditionalMoment

Prepare the Computation of score2

tryWithWarnings

Run an Expression and Catch Warnings and Errors

Degree of Freedom for the Robust Chi-Square Test

Symmetrize a Matrix

getCluster2-internal

Reconstruct the Cluster Variable from a nlme Model

Compute Bias Corrected Quantities.

Reconstruct the Conditional Variance Covariance Matrix

General Linear Hypothesis

information2-internal

Compute the Expected Information Matrix From the Conditional Moments

Extract The full Information Matrix

Normalize var1 and var2

Find the Number of Steps Performed During the Sequential Testing

Extract Corrected Residuals

getIndexOmega2-internal

Extract the name of the endogenous variables

evalInParentEnv

Find Object in the Parent Environments

Integrate a Gaussian/Student Density over a Triangle

Response Variable of a Formula

summary.calibrateType1

Display the Type 1 Error Rate

Extract the Variance Covariance Matrix of the Model Parameters

Pre-computation for the Score

summary.modelsearch2

summary Method for modelsearch2 Objects

score2-internal

Compute the Corrected Score.

Satterthwaite Correction and Small Sample Correction

Summary with Small Sample Correction

Set a Link to a Value