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 |
| DOI: | 10.32614/CRAN.package.glsm |
| 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 |
Documentation:
| Reference manual: | glsm.pdf |
Downloads:
| Package source: | glsm_0.0.0.6.tar.gz |
| Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: not available |
| macOS binaries: | r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available |
Linking:
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