logistic4p-package: Logistic Regression with Misclassification in Dependent...

In logistic4p: Logistic Regression with Misclassification in Dependent Variables

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: This package was not yet installed at build time.
Index: This package was not yet installed at build time.

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)

logistic4p documentation built on Oct. 21, 2023, 5:07 p.m.