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logistic4p (version 1.6)

Logistic Regression with Misclassification in Dependent Variables

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.

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Version

Install

install.packages('logistic4p')

Monthly Downloads

158

Version

1.6

License

GPL

Maintainer

Zhiyong Zhang

Last Published

October 21st, 2023

Functions in logistic4p (1.6)

logistic

Logistic Regression
logistic4p.fn

Logistic Regression Model with FN Misclassification Correction
logistic4p.fp.fn

Logistic Regression with both FP and FN Misclassification Correction
logistic4p.fp

Logistic Regression with FP Misclassification Correction
logistic4p

Logistic Regressions with Misclassification Correction
print.logistic4p

Printing Outputs of Logistic Regression with Misclassification Parameters
nlsy

An example data set
logistic4p-package

tools:::Rd_package_title("logistic4p")
logistic4p.e

Logistic regressions with constrained FP and FN misclassifications