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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.0.0.6 |
rolling source/ R- | glsm_0.0.0.6.tar.gz |
17.6 KiB |
0.0.0.6 |
rolling linux/jammy R-4.5 | glsm_0.0.0.6.tar.gz |
51.5 KiB |
0.0.0.6 |
rolling linux/noble R-4.5 | glsm_0.0.0.6.tar.gz |
51.5 KiB |
0.0.0.6 |
latest source/ R- | glsm_0.0.0.6.tar.gz |
17.6 KiB |
0.0.0.6 |
latest linux/jammy R-4.5 | glsm_0.0.0.6.tar.gz |
51.5 KiB |
0.0.0.6 |
latest linux/noble R-4.5 | glsm_0.0.0.6.tar.gz |
51.5 KiB |
0.0.0.6 |
2026-04-23 source/ R- | glsm_0.0.0.6.tar.gz |
17.6 KiB |
0.0.0.6 |
2026-04-09 windows/windows R-4.5 | glsm_0.0.0.6.zip |
54.6 KiB |