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 
[aut, cre] (<https://orcid.org/0000-0002-2888-9660>),
Humberto Llinas
[aut] (<https://orcid.org/0000-0002-2976-5109>),
Omar Fabregas
[aut] (<https://orcid.org/0000-0001-6853-6280>) |
| Maintainer: |
Jorge Villalba <jvillalba at utb.edu.co> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
yes |
| Citation: |
lsm citation info |
| Materials: |
README |
| CRAN checks: |
lsm results |
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