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lavaSearch2 (version 1.0.0)

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

1.0.0

License

GPL-3

Issues

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Repository

https://github.com/bozenne/lavaSearch2

Maintainer

Brice Ozenne

Last Published

January 24th, 2018

Functions in lavaSearch2 (1.0.0)

calcType1postSelection

Compute the Type 1 Error After Selection

Check that Validity of the Dataset

Model Coefficients

Export Mean and Variance Coefficients from a nlme Model

autoplot.IntDensTri

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

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

Integrate a Gaussian/Student Density over a Triangle

Add a New Link Between Two Variables in a LVM

Find all New Links Between Variables

Reconstruct the Cluster Variable from a nlme Model

evalInParentEnv

Find Object in the Parent Environments

Extract Data From a Model

Tools for Model Specification in the Latent Variable Framework

Power of a Matrix

Testing the Relevance of Additional Links Using the Max Statistic

Find the Number of Steps Performed During the Sequential Testing

Compute the Score Directly from the Gaussian Density

selectRegressor

Regressor of a Formula.

Pre-computation for the Score

Summary with Small Sample Correction

Compute the Derivative of the Information Matrix

defineCategoricalLink

Identify Categorical Links in LVM

Normalize var1 and var2

Compute the Degree of Freedom of the Variance Parameters

Prepare the Computation of score2

Reconstruct the Marginal Variance Covariance Matrix from a nlme Model

General Linear Hypothesis

Data-driven Extension of a Latent Variable Model

Compute the Residuals from a lvmfit Object

Response Variable of a Formula

Set a Link to a Value

Testing the Relevance of Additional Links Using a Likelihood Ratio Test

Check Arguments of a function.

Convert Variable Names to Dummy Variables Names.

Contrast matrix for multiple latent variable models

Create a Mesh for the Integration

Find the Links that Should be Added Accroding to the Sequential Testing

Extract one Step From the Sequential Procedure

Symmetrize a Matrix

tryWithWarnings

Run an Expression and Catch Warnings and Errors

Extract the Specific Coefficient Bames or Positions in a LVM

Form all Unique Combinations Between two Vectors

Compare Methods to Identify Missing Local Dependencies in a LVM

Create Rownames for a Contrast Matrix

Extract i.i.d. decomposition from linear and latent variable models

Jacknife iid Decomposition from Model Object

Merge two modelsearch Objects

Simultaneous Inference for Multiple Models

Variance-Covariance Matrix for a Fitted Object