Package: logistic4p 1.6
logistic4p: Logistic Regression with Misclassification in Dependent Variables
Error in a binary dependent variable, also known as misclassification, has not drawn much attention in psychology. Ignoring misclassification in logistic regression can result in misleading parameter estimates and statistical inference. This package conducts logistic regression analysis with misspecification in outcome variables.
Authors:
logistic4p_1.6.tar.gz
logistic4p_1.6.zip(r-4.7-any)logistic4p_1.6.zip(r-4.6-any)logistic4p_1.6.zip(r-4.5-any)
logistic4p_1.6.tgz(r-4.6-any)logistic4p_1.6.tgz(r-4.5-any)
logistic4p_1.6.tar.gz(r-4.7-any)logistic4p_1.6.tar.gz(r-4.6-any)
logistic4p_1.6.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
logistic4p/json (API)
| # Install 'logistic4p' in R: |
| install.packages('logistic4p', repos = c('https://johnnyzhz.r-universe.dev', 'https://cloud.r-project.org')) |
- nlsy - An example data set
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:8736039aaf. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 102 | ||
| source / vignettes | OK | 156 | ||
| linux-release | OK | 116 | ||
| macos-release | OK | 91 | ||
| macos-oldrel | OK | 98 | ||
| windows-devel | OK | 51 | ||
| windows-release | OK | 56 | ||
| windows-oldrel | OK | 59 | ||
| wasm-release | OK | 74 |
Exports:logisticlogistic4plogistic4p.elogistic4p.fnlogistic4p.fplogistic4p.fp.fn
Dependencies:MASS
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Logistic Regression with Misclassification in Dependent Variables | logistic4p-package |
| Logistic Regression | logistic |
| Logistic Regressions with Misclassification Correction | logistic4p |
| Logistic regressions with constrained FP and FN misclassifications | logistic4p.e |
| Logistic Regression Model with FN Misclassification Correction | logistic4p.fn |
| Logistic Regression with FP Misclassification Correction | logistic4p.fp |
| Logistic Regression with both FP and FN Misclassification Correction | logistic4p.fp.fn |
| An example data set | nlsy |
| Printing Outputs of Logistic Regression with Misclassification Parameters | print.logistic4p |