jlvia1191/lsm: Estimation of the log Likelihood of the Saturated Model
Version 0.1.7

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.

Getting started

Package details

AuthorHumberto Llinas [aut], Omar Fabregas [aut], Jorge Villalba [aut, cre]
MaintainerJorge Villalba <[email protected]>
LicenseMIT + file LICENSE
Version0.1.7
URL https://github.com/jlvia1191/lsm
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("jlvia1191/lsm")
jlvia1191/lsm documentation built on Aug. 30, 2018, 11:16 p.m.