glsm: Saturated Model Log-Likelihood for Multinomial Outcomes
When the response variable Y takes one of R > 1 values, the function 'glsm()' computes the maximum likelihood estimates (MLEs) of the parameters under four models: null, complete, saturated, and logistic. It also calculates the log-likelihood values for each model. This method assumes independent, non-identically distributed variables. For grouped data with a multinomial outcome, where observations are divided into J populations, the function 'glsm()' provides estimation for any number K of explanatory variables.
| Version: |
0.0.0.6 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
stats, dplyr (≥ 1.0.0), ggplot2 (≥ 1.0.0), VGAM (≥ 1.0.0), plyr |
| Published: |
2025-07-14 |
| Author: |
Jorge Villalba 
[aut, cre],
Humberto Llinas
[aut],
Jorge Borja [aut],
Jorge Tilano
[aut] |
| Maintainer: |
Jorge Villalba <jvillalba at utb.edu.co> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| Citation: |
glsm citation info |
| Materials: |
README |
| CRAN checks: |
glsm results |
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