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