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augSIMEX (version 3.7.4)

Analysis of Data with Mixed Measurement Error and Misclassification in Covariates

Description

Implementation of the augmented Simulation-Extrapolation (SIMEX) algorithm proposed by Yi et al. (2015) for analyzing the data with mixed measurement error and misclassification. The main function provides a similar summary output as that of glm() function. Both parametric and empirical SIMEX are considered in the package.

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Version

Install

install.packages('augSIMEX')

Monthly Downloads

199

Version

3.7.4

License

GPL (>= 2)

Maintainer

Qihuang Zhang

Last Published

April 23rd, 2020

Functions in augSIMEX (3.7.4)

GeneRepeat

Example data for univariate error-prone covariates in repeated measurements case
ToyUni

Toy example data for univariate error-prone covariates
GeneUni

Example of genetic data for univariate error-prone covariates
ToyMult

Toy example data for multivariate error-prone covariates
residuals.augSIMEX

Residuals of the Fits Made by augSIMEX
glmscore

Score Value in Generalized Linear Model
ToyRepeat

Toy example data for univariate error-prone covariates in repeated measurements case
augSIMEX-package

R package for Analysis of Data with Mixed Measurement Error and Misclassification in Covariates
summary.augSIMEX

Summarizing the Adjusted Fits of Generalized Linear Model
vcov.augSIMEX

Extract the variance-covariance matrix from the fitted augSIMEX object
coef.augSIMEX

Extract the coefficients from the fitted augSIMEX object
augSIMEX

Analysis of Data with Mixed Measurement Error and Misclassification in Covariates
logLik.augSIMEX

Extract the likelihood from the fitted augSIMEX object
plot.augSIMEX

Plot of Extrapolation
predict.augSIMEX

Predict Method for the model fits by augSIMEX