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logistic4p  

Logistic Regression with Misclassification in Dependent Variables
View on CRAN: Click here


Download and install logistic4p package within the R console
Install from CRAN:
install.packages("logistic4p")

Install from Github:
library("remotes")
install_github("cran/logistic4p")

Install by package version:
library("remotes")
install_version("logistic4p", "1.6")



Attach the package and use:
library("logistic4p")
Maintained by
Zhiyong Zhang
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-05-31
Latest Update: 2017-05-31
Description:
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.
How to cite:
Zhiyong Zhang (2017). logistic4p: Logistic Regression with Misclassification in Dependent Variables. R package version 1.6, https://cran.r-project.org/web/packages/logistic4p. Accessed 06 Nov. 2024.
Previous versions and publish date:
1.5 (2017-05-31 08:20)
Other packages that cited logistic4p R package
View logistic4p citation profile
Other R packages that logistic4p depends, imports, suggests or enhances
Complete documentation for logistic4p
Functions, R codes and Examples using the logistic4p R package
Some associated functions: logistic . logistic4p-package . logistic4p.e . logistic4p.fn . logistic4p.fp.fn . logistic4p.fp . logistic4p . nlsy . print.logistic4p . 
Some associated R codes: logistic4p.R .  Full logistic4p package functions and examples
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