logistic4p-package {logistic4p}

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.

Details

The DESCRIPTION file:

Package:	logistic4p
Type:	Package
Title:	Logistic Regression with Misclassification in Dependent Variables
Version:	1.6
Date:	2023-10-20
Depends:	R (>= 2.10), MASS
Author:	Haiyan Liu and Zhiyong Zhang
Maintainer:	Zhiyong Zhang <[e-mail]>
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.
License:	GPL
LazyLoad:	yes

Index of help topics:

logistic                Logistic Regression
logistic4p              Logistic Regressions with Misclassification
                        Correction
logistic4p-package      Logistic Regression with Misclassification in
                        Dependent Variables
logistic4p.e            Logistic regressions with constrained FP and FN
                        misclassifications
logistic4p.fn           Logistic Regression Model with FN
                        Misclassification Correction
logistic4p.fp           Logistic Regression with FP Misclassification
                        Correction
logistic4p.fp.fn        Logistic Regression with both FP and FN
                        Misclassification Correction
nlsy                    An example data set
print.logistic4p        Printing Outputs of Logistic Regression with
                        Misclassification Parameters

Author(s)

Haiyan Liu and Zhiyong Zhang

Maintainer: Zhiyong Zhang <[e-mail]>

References

Liu, H. and Zhang, Z. (2016) Logistic Regression with Misclassification in Dependent Variables: Method and Software.(In preparation.)

Examples

## Not run: 
data(nlsy)
x=nlsy[, -1]
y=nlsy[,1]
mod=logistic4p(x, y, model='fn')

## End(Not run)