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.
| Version: | 1.6 |
| Depends: | R (≥ 2.10),MASS |
| Published: | 2023-10-21 |
| DOI: | 10.32614/CRAN.package.logistic4p |
| Author: | Haiyan Liu and Zhiyong Zhang |
| Maintainer: | Zhiyong Zhang <johnnyzhz at gmail.com> |
| License: | GPL-2 |GPL-3 [expanded from: GPL] |
| NeedsCompilation: | no |
| CRAN checks: | logistic4p results |
| Reference manual: | logistic4p.html ,logistic4p.pdf |
| Package source: | logistic4p_1.6.tar.gz |
| Windows binaries: | r-devel:logistic4p_1.6.zip, r-release:logistic4p_1.6.zip, r-oldrel:logistic4p_1.6.zip |
| macOS binaries: | r-release (arm64):logistic4p_1.6.tgz, r-oldrel (arm64):logistic4p_1.6.tgz, r-release (x86_64):logistic4p_1.6.tgz, r-oldrel (x86_64):logistic4p_1.6.tgz |
| Old sources: | logistic4p archive |
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