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lsm: Estimation of the log Likelihood of the Saturated Model

When the values of the outcome variable Y are either 0 or 1, the function lsm() calculates the estimation of the log likelihood in the saturated model. This model is characterized by Llinas (2006, ISSN:2389-8976) in section 2.3 through the assumptions 1 and 2. The function LogLik() works (almost perfectly) when the number of independent variables K is high, but for small K it calculates wrong values in some cases. For this reason, when Y is dichotomous and the data are grouped in J populations, it is recommended to use the function lsm() because it works very well for all K.

Version: 0.2.1.4
Depends: R (≥ 3.5.0)
Imports: stats, dplyr (≥ 1.0.0), ggplot2 (≥ 1.0.0)
Published: 2024-06-08
DOI: 10.32614/CRAN.package.lsm
Author: Jorge Villalba ORCID iD [aut, cre], Humberto Llinas ORCID iD [aut], Omar Fabregas ORCID iD [aut]
Maintainer: Jorge Villalba <jvillalba at utb.edu.co>
License: MIT + file LICENSE
NeedsCompilation: yes
Citation: lsm citation info
Materials: README
CRAN checks: lsm results

Documentation:

Reference manual: lsm.pdf

Downloads:

Package source: lsm_0.2.1.4.tar.gz
Windows binaries: r-devel: lsm_0.2.1.4.zip, r-release: lsm_0.2.1.4.zip, r-oldrel: lsm_0.2.1.4.zip
macOS binaries: r-release (arm64): lsm_0.2.1.4.tgz, r-oldrel (arm64): lsm_0.2.1.4.tgz, r-release (x86_64): lsm_0.2.1.4.tgz, r-oldrel (x86_64): lsm_0.2.1.4.tgz
Old sources: lsm archive

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.