Package: lsm 0.2.1.4

Jorge Villalba

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.

Authors:Jorge Villalba [aut, cre], Humberto Llinas [aut], Omar Fabregas [aut]

lsm_0.2.1.4.tar.gz
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lsm.pdf |lsm.html
lsm/json (API)

# Install 'lsm' in R:
install.packages('lsm', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

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2.68 score 16 scripts 392 downloads 6 mentions 1 exports 31 dependencies

Last updated 6 months agofrom:2401bb9f81. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 06 2024
R-4.5-linuxOKDec 06 2024

Exports:lsm

Dependencies:clicolorspacedplyrfansifarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalestibbletidyselectutf8vctrsviridisLitewithr