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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:	0.2.1.5
Depends:	R (≥ 3.5.0)
Imports:	stats,dplyr (≥ 1.0.0),ggplot2 (≥ 1.0.0)
Published:	2025-06-02
DOI:	10.32614/CRAN.package.lsm
Author:	Jorge Villalba [aut, cre], Humberto Llinas [aut], Omar Fabregas [aut]
Maintainer:	Jorge Villalba <jvillalba at utb.edu.co>
License:	MIT + fileLICENSE
NeedsCompilation:	no
Citation:	lsm citation info
Materials:	README
CRAN checks:	lsm results

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