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.
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