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: 0.2.0
Depends: R (≥ 3.5.0)
Imports: stats
Published: 2020-03-07
Author: Humberto Llinas [aut], Omar Fabregas [aut], Jorge Villalba [aut, cre]
Maintainer: Jorge Villalba <jlvia1191 at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
CRAN checks: lsm results

Downloads:

Reference manual: lsm.pdf
Package source: lsm_0.2.0.tar.gz
Windows binaries: r-devel: lsm_0.2.0.zip, r-devel-gcc8: lsm_0.1.9.zip, r-release: lsm_0.2.0.zip, r-oldrel: lsm_0.2.0.zip
OS X binaries: r-release: lsm_0.2.0.tgz, r-oldrel: lsm_0.2.0.tgz
Old sources: lsm archive

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