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lavaSearch2 (version 1.4)

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

1.4

License

GPL-3

Issues

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Repository

https://github.com/bozenne/lavaSearch2

Maintainer

Brice Ozenne

Last Published

October 5th, 2018

Functions in lavaSearch2 (1.4)

Degree of Freedom for the Chi-Square Test

Extract corrected i.i.d. decomposition

Create a Mesh for the Integration

evalInParentEnv

Find Object in the Parent Environments

Compute Bias Corrected Quantities.

Jackknife iid Decomposition from Model Object

tryWithWarnings

Run an Expression and Catch Warnings and Errors

Check Arguments of a function.

autoplot.intDensTri

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

Add a New Link Between Two Variables in a LVM

dInformation2-internal

Compute the First Derivative of the Expected Information Matrix

Extract the Type of Each Coefficient

Create Rownames for a Contrast Matrix

Create Contrast matrix

Extract Data From a Model

Test Linear Hypotheses with small sample correction

Find all New Links Between Variables

conditionalMoment

Prepare the Computation of score2

Extract one Step From the Sequential Procedure

getVarCov2-internal

Reconstruct the Marginal Variance Covariance Matrix from a nlme Model

selectRegressor

Regressor of a Formula.

Extract the Links that Have Been Found by the modelsearch2.

getCluster2-internal

Reconstruct the Cluster Variable from a nlme Model

score2-internal

Compute the Corrected Score.

getIndexOmega2-internal

Extract the name of the endogenous variables

Reconstruct the Conditional Variance Covariance Matrix

Response Variable of a Formula

Summary with Small Sample Correction

Form all Unique Combinations Between two Vectors

Symmetrize a Matrix

Extract The Individual Score

Convert Variable Names to Dummy Variables Names.

Extract the Model that Has Been Retains by the modelsearch2.

Normalize var1 and var2

information2-internal

Compute the Expected Information Matrix From the Conditional Moments

Extract the Variance Covariance Matrix of the Model Parameters

Degree of Freedom for the Robust Chi-Square Test

Extract Leverage Values

Extract Empirical Estimating Functions (lvmfit Object)

Extract Corrected Residuals

General Linear Hypothesis

Power of a Matrix

Set a Link to a Value

Integrate a Gaussian/Student Density over a Triangle

Satterthwaite Correction and Small Sample Correction

Tools for Model Specification in the Latent Variable Framework

Pre-computation for the Score

Data-driven Extension of a Latent Variable Model

Find the Number of Steps Performed During the Sequential Testing

summary.calibrateType1

Display the Type 1 Error Rate

summary.modelsearch2

summary Method for modelsearch2 Objects

autplot-modelsearch2

Display the Value of a Coefficient across the Steps.

autoplot_calibrateType1

Graphical Display of the Bias or Type 1 Error

Export Mean and Variance Coefficients

Extract the Coefficient by Type

Simulation Study Assessing Bias and Type 1 Error

Check that Validity of the Dataset

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

calcType1postSelection

Compute the Type 1 Error After Selection

defineCategoricalLink

Identify Categorical Links in LVM