Package: prLogistic 2.0.2
prLogistic: Estimation of Prevalence Ratios via Logistic Regression Models
Estimates adjusted prevalence ratios (PR) and their confidence intervals from logistic regression models, addressing the well-known limitation of odds ratios (OR) as approximations to PR in cross-sectional studies with common outcomes. Supports independent observations (glm()), clustered/multilevel data (glmer() from 'lme4'), longitudinal data via Generalised Estimating Equations (geeglm() from 'geepack'), and complex survey designs (svyglm() from 'survey'). Inference is available via the delta method (conditional and marginal standardisation) and via bootstrap (normal-approximation and percentile intervals). Continuous covariates are handled through user-specified or median-based reference values; flexible baseline specification allows any reference category to be chosen for factor predictors. Based on the methodology described in Amorim & Ospina (2021) <doi:10.1590/0001-3765202120190316>.
Authors:
prLogistic_2.0.2.tar.gz
prLogistic_2.0.2.zip(r-4.7)prLogistic_2.0.2.zip(r-4.6)prLogistic_2.0.2.zip(r-4.5)
prLogistic_2.0.2.tgz(r-4.6-any)prLogistic_2.0.2.tgz(r-4.5-any)
prLogistic_2.0.2.tar.gz(r-4.7-any)prLogistic_2.0.2.tar.gz(r-4.6-any)
prLogistic_2.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
prLogistic/json (API)
NEWS
| # Install 'prLogistic' in R: |
| install.packages('prLogistic', repos = c('https://raydonal.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/raydonal/prlogistic/issues
Pkgdown/docs site:https://raydonal.github.io
Last updated from:21fe1f02df. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 196 | ||
| source / vignettes | OK | 233 | ||
| linux-release-x86_64 | OK | 156 | ||
| macos-release-arm64 | OK | 91 | ||
| macos-oldrel-arm64 | OK | 97 | ||
| windows-devel | OK | 93 | ||
| windows-release | OK | 110 | ||
| windows-oldrel | OK | 101 | ||
| wasm-release | OK | 120 |
Exports:prLogisticBootCondprLogisticBootMargprLogisticDeltaprLogisticGEEprLogisticSurvey
Dependencies:bootlatticelme4MASSMatrixminqanlmenloptrrbibutilsRcppRcppEigenRdpackreformulasrlang
Estimating Prevalence Ratios with prLogistic
Rendered fromprLogistic-intro.Rmdusingknitr::rmarkdownon Jun 20 2026.Last update: 2026-06-05
Started: 2026-06-05
Reproducing the Examples from Amorim & Ospina (2021)
Rendered fromarticle-examples.Rmdusingknitr::rmarkdownon Jun 20 2026.Last update: 2026-06-05
Started: 2026-06-05
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Extract prevalence ratio point estimates | coef.prLogistic |
| Extract confidence intervals for prevalence ratios | confint.prLogistic |
| Downer Cow Survival Data | downer |
| Low Birth Weight - Longitudinal Study (Salvador, Brazil) | LBW |
| Forest plot of prevalence ratios | plot.prLogistic |
| Print a prLogistic object | print.prLogistic |
| Bootstrap CI for Prevalence Ratios - Conditional Standardisation | prLogisticBootCond |
| Bootstrap CI for Prevalence Ratios - Marginal Standardisation | prLogisticBootMarg |
| Estimate Prevalence Ratios via Logistic Regression - Delta Method | prLogisticDelta |
| Prevalence Ratios for Longitudinal Data - GEE Models | prLogisticGEE |
| Prevalence Ratios for Complex Survey Data | prLogisticSurvey |
| Summarise a prLogistic object | summary.prLogistic |
| Thailand Education Study - Clustered Binary Data | Thailand |
| Titanic Passenger Survival | titanic |
| Toenail Infection Trial - Longitudinal Binary Outcome | Toenail |
| UIS Drug Treatment Study | UIS |
