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 <firstname.lastname@example.org>|
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: Logistic Regression with Misclassification in Dependent...
nlsy: An example data set
print.logistic4p: Printing Outputs of Logistic Regression with...
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