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R package: "misclassGLM"

Description

Estimates models that extend the standard GLM to take misclassification into account. The models require side information from a secondary data set on the misclassification process, i.e. some sort of misclassification probabilities conditional on some common covariates. A detailed description of the algorithm can be found in Dlugosz, Mammen and Wilke (2017) Computational Statistics & Data Analysis 110:145-159 http://dx.doi.org/10.1016/j.csda.2017.01.003.

Installation

From CRAN

The easiest way to use any of the functions in the misclassGLM package is to install the CRAN version. It can be installed from within R using the command:

#!R

install.packages("misclassGLM")

From bitbucket

The devtools package contains functions that allow you to install R packages directly from bitbucket or github. If you've installed and loaded the devtools package, the installation command is

#!R

install_bitbucket("sdlugosz/misclassGLM")

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Version

Install

install.packages('misclassGLM')

Monthly Downloads

147

Version

0.3.5

License

GPL-3

Maintainer

Last Published

November 19th, 2023

Functions in misclassGLM (0.3.5)

boot.misclassMlogit

Compute Bootstrapped Standard Errors for misclassMlogit Fits
mfx.misclassGLM

Compute Marginal Effects for misclassGLM Fits
mfx.misclassMlogit

Compute Marginal Effects for 'misclassMlogit' Fits
predict.misclassGLM

Predict Method for misclassGLM Fits
boot.misclassGLM

Compute Bootstrapped Standard Errors for misclassGLM Fits
misclassGLM

GLM estimation under misclassified covariate
simulate_mlogit_dataset

Simulate a Data Set to Use With misclassMlogit
misclassGLM-package

misclassGLM: Computation of Generalized Linear Models with Misclassified Covariates Using Side Information
predict.misclassMlogit

Predict Method for misclassMlogit Fits
simulate_GLM_dataset

Simulate a Data Set to Use With misclassGLM
misclassMlogit

Mlogit estimation under misclassified covariate