CRAN/E | lsm

lsm

Estimation of the log Likelihood of the Saturated Model

Installation

About

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.

Citation lsm citation info

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Version 0.2.1.2
R ≥ 3.5.0
Published 2022-02-04 953 days ago
Needs compilation? yes
License MIT
License File
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Maintainer

Maintainer

Jorge Villalba

Authors

Humberto Llinas

aut

Omar Fabregas

aut

Jorge Villalba

aut / cre

Material

README
Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

lsm archive

Depends

R ≥ 3.5.0

Imports

stats
dplyr ≥ 1.0.0
ggplot2 ≥ 1.0.0