Package: lsm 0.2.1.5
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:
lsm_0.2.1.5.tar.gz
lsm_0.2.1.5.tar.gz(r-4.7-any)lsm_0.2.1.5.tar.gz(r-4.6-any)
lsm_0.2.1.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
lsm/json (API)
| # Install 'lsm' in R: |
| install.packages('lsm', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:3d29648852. Checks:4 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 139 | ||
| source / vignettes | OK | 166 | ||
| linux-release-x86_64 | OK | 136 | ||
| wasm-release | OK | 106 |
Exports:lsm
Dependencies:clicpp11dplyrfarvergenericsggplot2gluegtableisobandlabelinglifecyclemagrittrpillarpkgconfigR6RColorBrewerrlangS7scalestibbletidyselectutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Coronary Heart Disease Study | chdage |
| Confidence Intervals for 'lsm' Objects | confint.lsm |
| icu | icu |
| lowbwt | lowbwt |
| Estimation of the log Likelihood of the Saturated Model | lsm |
| Graphics Method for 'lsm' Objects | plot.lsm |
| Predictions and Confidence intervals | predict.lsm |
| pros | pros |
| Summarizing Method for 'lsm' Objects | summary.lsm |
| survey | survey |
| uis | uis |