LAM-package {LAM}

Some Latent Variable Models

Description

Includes some procedures for latent variable modeling with a particular focus on multilevel data. The 'LAM' package contains mean and covariance structure modelling for multivariate normally distributed data (mlnormal(); Longford, 1987; <doi:10.1093/biomet/74.4.817>), a general Metropolis-Hastings algorithm (amh(); Roberts & Rosenthal, 2001, <doi:10.1214/ss/1015346320>) and penalized maximum likelihood estimation (pmle(); Cole, Chu & Greenland, 2014; <doi:10.1093/aje/kwt245>).

Details

The LAM package contains the following main functions:

• A general fitting method for mean and covariance structure for multivariate normally distributed data is the mlnormal function. Prior distributions or regularization methods (lasso penalties) are also accommodated. Missing values on dependent variables can be treated by applying the full information maximum likelihood method implemented in this function.

• A general (but experimental) Metropolis-Hastings sampler for Bayesian analysis based on MCMC is implemented in the amh function. Deterministic optimization of the posterior distribution (maximum posterior estimation or penalized maximum likelihood estimation) can be conduction with the pmle function which is based on stats::optim.

Author(s)

Alexander Robitzsch [aut,cre]

Maintainer: Alexander Robitzsch <[e-mail]>

References

Cole, S. R., Chu, H., & Greenland, S. (2013). Maximum likelihood, profile likelihood, and penalized likelihood: a primer. American Journal of Epidemiology, 179(2), 252-260. doi:10.1093/aje/kwt245

Longford, N. T. (1987). A fast scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested random effects. Biometrika, 74(4), 817-827. doi:10.1093/biomet/74.4.817

Roberts, G. O., & Rosenthal, J. S. (2001). Optimal scaling for various Metropolis-Hastings algorithms. Statistical Science, 16(4), 351-367. doi:10.1214/ss/1015346320

Examples

  ##  > library(LAM)
  ##  ## LAM 0.0-4 (2017-03-03 16:53:46)
  ##
  ##   __         ______     __    __
  ##  /\ \       /\  __ \   /\ "-./  \
  ##  \ \ \____  \ \  __ \  \ \ \-./\ \
  ##   \ \_____\  \ \_\ \_\  \ \_\ \ \_\
  ##    \/_____/   \/_/\/_/   \/_/  \/_/
  ##