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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.
Getting started
Browse package contents
Package details |
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| 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@utb.edu.co> |
| License | MIT + file LICENSE |
| Version | 0.2.1.4 |
| Package repository | View on CRAN |
| Installation |
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