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lsm  

Estimation of the log Likelihood of the Saturated Model
View on CRAN: Click here


Download and install lsm package within the R console
Install from CRAN:
install.packages("lsm")

Install from Github:
library("remotes")
install_github("cran/lsm")

Install by package version:
library("remotes")
install_version("lsm", "0.2.1.4")



Attach the package and use:
library("lsm")
Maintained by
Jorge Villalba
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-04-09
Latest Update: 2022-02-04
Description:
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.
How to cite:
Jorge Villalba (2018). lsm: Estimation of the log Likelihood of the Saturated Model. R package version 0.2.1.4, https://cran.r-project.org/web/packages/lsm. Accessed 06 Nov. 2024.
Previous versions and publish date:
0.1.6 (2018-04-09 15:52), 0.1.8 (2018-08-30 10:12), 0.1.9 (2020-01-15 07:30), 0.2.0 (2020-03-07 22:00), 0.2.1.2 (2022-02-04 04:10)
Other packages that cited lsm R package
View lsm citation profile
Other R packages that lsm depends, imports, suggests or enhances
Complete documentation for lsm
Functions, R codes and Examples using the lsm R package
Some associated functions: chdage . confint.lsm . icu . lowbwt . lsm . predict.lsm . pros . summary.lsm . survey . uis . 
Some associated R codes: chdage.R . confint.lsm.R . icu.R . lowbwt.R . lsm.R . lsm_package.R . predict.lsm.R . pros.R . summary.lsm.R . survey.R . uis.R . zzz.R .  Full lsm package functions and examples
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