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lava (version 1.6.4)

Latent Variable Models

Description

A general implementation of Structural Equation Models with latent variables (MLE, 2SLS, and composite likelihood estimators) with both continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen (2013) ). Mixture latent variable models and non-linear latent variable models (Holst and Budtz-Joergensen (2018) ). The package also provides methods for graph exploration (d-separation, back-door criterion), simulation of general non-linear latent variable models, and estimation of influence functions for a broad range of statistical models.

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Install

install.packages('lava')

Monthly Downloads

163,136

Version

1.6.4

License

GPL-3

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Repository

https://github.com/kkholst/lava

Maintainer

Klaus Holst

Last Published

November 25th, 2018

Functions in lava (1.6.4)

Conformal prediction

Define variables as ordinal

Read Mplus output

Report estimates across different models

Create a Data Frame from All Combinations of Factors

Combine matrices to block diagonal structure

Longitudinal Bone Mineral Density Data (Wide format)

Longitudinal Bone Mineral Density Data

Convert to/from NA

Statistical tests

Composite Likelihood for probit latent variable models

Adds curly brackets to plot

Cross-validation

Estimate mixture latent variable model.

Missing value generator

Define constant risk difference or relative risk association for binary exposure

Generic method for finding indeces of model parameters

Estimation of parameters in a Latent Variable Model (lvm)

Identify points on plot

Trim tring of (leading/trailing/all) white spaces

Extract i.i.d. decomposition (influence function) from model object

Closed testing procedure

estimate.default

Estimation of functional of parameters

Add Confidence limits bar to plot

Generic bootstrap method

Add non-linear constraints to latent variable model

Initialize new latent variable model

Extract model summaries and GOF statistics for model object

Define range constraints of parameters

Finds elements in vector or column-names in data.frame/matrix

Plot regression lines

Create random missing data

Example data (nonlinear model)

Backdoor criterion

Newton-Raphson method

Label elements of object

Define labels of graph

Add color-bar to plot

Example SEM data (nonlinear)

Extract pathways in model graph

Add variable to (model) object

Returns device-coordinates and plot-region

Calculate bootstrap estimates of a lvm object

Calculate diagnostic tests for 2x2 table

Polychoric correlation

Create/extract 'reverse'-diagonal matrix or off-diagonal elements

Finds the unique commutation matrix

Remove variables from (model) object.

Calculate confidence limits for parameters

Create contrast matrix

Apply a Function to a Data Frame Split by Factors

Predict function for latent variable models

Generic method for extracting correlation coefficients of model object

Plot/estimate surface

Summary method for 'sim' objects

Check d-separation criterion

Generate a transparent RGB color

Model searching

Read SAS output

Univariate cumulative link regression models

Prediction in structural equation models

Time-dependent parameters

Wrapper function for mclapply

Twin menarche data

Identify candidates of equivalent models

Fix mean parameters in 'lvm'-object

Convert object to ascii suitable for org-mode

Example SEM data

Weighted K-means

Add regression association to latent variable model

Cross-validated two-stage estimator

Dose response calculation for binomial regression models

Calculate partial correlations

For internal use

measurement.error

Two-stage (non-linear) measurement error

Stack estimating equations

Missing data example

Estimate probabilities in contingency table

Generic cancel method

Extract subset of latent variable model

Extract children or parent elements of object

Add covariance structure to Latent Variable Model

Extract variable names from latent variable model

Split data into folds

Add an observed event time outcome to a latent variable model.

Organize several image calls (for visualizing categorical data)

Estimation and simulation of latent variable models

Set global options for lava

Concatenation operator

Matching operator (x not in y) oposed to the %in%-operator (x in y)

Plot path diagram

Calculate simultaneous confidence limits by Scheffe's method

Performs a rotation in the plane

Converts strings to formula

Plot method for simulation 'sim' objects

Estimate mixture latent variable model

Convert pdf to raster format

Plot method for 'estimate' objects

Wait for user input (keyboard or mouse)

Two-stage estimator

Regression model for binomial data with unkown group of immortals

twostage.lvmfit

Two-stage estimator (non-linear SEM)