logistic4p: Logistic Regression with Misclassification in Dependent Variables

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.

Author
Haiyan Liu and Zhiyong Zhang
Date of publication
2016-05-16 20:22:19
Maintainer
Zhiyong Zhang <johnnyzhz@gmail.com>
License
GPL
Version
1.4

View on R-Forge

Man pages

logistic
Logistic Regression
logistic4p
Logistic Regressions with Misclassification Correction
logistic4p.e
Logistic regressions with constrained FP and FN...
logistic4p.fn
Logistic Regression Model with FN Misclassification...
logistic4p.fp
Logistic Regression with FP Misclassification Correction
logistic4p.fp.fn
Logistic Regression with both FP and FN Misclassification...
logistic4p-package
\Sexpr[results=rd,stage=build]{tools:::Rd_package_title("logistic4p")}
nlsy
An example data set
print.logistic4p
Printing Outputs of Logistic Regression with...

Files in this package

logistic4p/DESCRIPTION
logistic4p/NAMESPACE
logistic4p/R
logistic4p/R/logistic4p.R
logistic4p/build
logistic4p/build/partial.rdb
logistic4p/data
logistic4p/data/nlsy.RData
logistic4p/man
logistic4p/man/logistic.Rd
logistic4p/man/logistic4p-package.Rd
logistic4p/man/logistic4p.Rd
logistic4p/man/logistic4p.e.Rd
logistic4p/man/logistic4p.fn.Rd
logistic4p/man/logistic4p.fp.Rd
logistic4p/man/logistic4p.fp.fn.Rd
logistic4p/man/nlsy.Rd
logistic4p/man/print.logistic4p.Rd