Estimation of the log Likelihood of the Saturated Model
c(person("Humberto", "Llinas", role = c("aut")),
person("Omar", "Fabregas", role = c("aut")),
person("Jorge", "Villalba", email = "jlvia1191@gmail.com", role = c("aut", "cre")))
Humberto Llinas [aut],
Omar Fabregas [aut],
Jorge Villalba [aut, cre]
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.

c(person("Humberto", "Llinas", email = "hllinas@uninorte.edu.co", role = c("aut"), comment = c(ORCID = "0000-0002-2976-5109")),
person("Omar", "Fabregas", email = "ofabregas@uninorte.edu.co", role = c("aut"), comment = c(ORCID = "0000-0001-6853-6280")),
person("Jorge", "Villalba", email = "jlvia1191@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-2888-9660")))
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.

Russell V. Lenth, The University of Iowa
This package provides the function \code{\link{lsmeans}}
for outputting so-called least-squares means for factor
combinations in an 'lm', 'lme', or 'lmer' object. It can also
compute contrasts or linear combinations of these least-squares
means, and several standard contrast families are provided.