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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2.1.5 |
rolling linux/jammy R-4.5 | lsm_0.2.1.5.tar.gz |
138.4 KiB |
0.2.1.5 |
rolling linux/noble R-4.5 | lsm_0.2.1.5.tar.gz |
138.2 KiB |
0.2.1.5 |
rolling source/ R- | lsm_0.2.1.5.tar.gz |
88.1 KiB |
0.2.1.5 |
latest linux/jammy R-4.5 | lsm_0.2.1.5.tar.gz |
138.4 KiB |
0.2.1.5 |
latest linux/noble R-4.5 | lsm_0.2.1.5.tar.gz |
138.2 KiB |
0.2.1.5 |
latest source/ R- | lsm_0.2.1.5.tar.gz |
88.1 KiB |
0.2.1.5 |
2026-04-26 source/ R- | lsm_0.2.1.5.tar.gz |
88.1 KiB |
0.2.1.5 |
2026-04-23 source/ R- | lsm_0.2.1.5.tar.gz |
88.1 KiB |
0.2.1.5 |
2026-04-09 windows/windows R-4.5 | lsm_0.2.1.5.zip |
141.5 KiB |
0.2.1.4 |
2025-04-20 source/ R- | lsm_0.2.1.4.tar.gz |
88.1 KiB |