R package for analysis of data with mixed measurement error and misclassification in covariates: augSIMEX

References

• Carroll RJ, Ruppert D, Crainiceanu CM. Measurement error in nonlinear models: a modern perspective. New York(NY): Chapman and Hall/CRC; 2006.
   Google Scholar
• Yi GY. Statistical analysis with measurement error or misclassification. New York(NY): Springer-Verlag; 2017.
   Google Scholar
• P.T.C. ASME, et al. Test uncertainty; 2005.
   Google Scholar
• Spiegelman D, Rosner B, Logan R. Estimation and inference for logistic regression with covariate misclassification and measurement error in main study/validation study designs. J Am Stat Assoc. 2000;95(449):51–61. doi: 10.1080/01621459.2000.10473898
   Web of Science ®Google Scholar
• Yi GY, Ma Y, Spiegelman D, et al. Functional and structural methods with mixed measurement error and misclassification in covariates. J Am Stat Assoc. 2015;110(510):681–696. doi: 10.1080/01621459.2014.922777
   PubMed Web of Science ®Google Scholar
• Devanarayan V, Stefanski LA. Empirical simulation extrapolation for measurement error models with replicate measurements. Stat Probab Lett. 2002;59(3):219–225. doi: 10.1016/S0167-7152(02)00098-6
   Web of Science ®Google Scholar
• Lederer W, Seibold H, Küchenhoff H. simex: SIMEX- And MCSIMEX-algorithm for measurement error models; 2017. Available from: https://CRAN.R-project.org/package=simex. R package version 1.7.
   Google Scholar
• Cook JR, Stefanski LA. Simulation-extrapolation estimation in parametric measurement error models. J Am Stat Assoc. 1994;89(428):1314–1328. doi: 10.1080/01621459.1994.10476871
   Web of Science ®Google Scholar
• Küchenhoff H, Mwalili SM, Lesaffre E. A general method for dealing with misclassification in regression: the misclassification simex. Biometrics. 2006;62(1):85–96. doi: 10.1111/j.1541-0420.2005.00396.x
   PubMed Web of Science ®Google Scholar
• Lederer W, Küchenhoff H. A short introduction to the simex and mcsimex. The Newsletter of the R Project. 6/4;2006:26.
   Google Scholar
• He W, Xiong J, Yi GY. Simex R package for accelerated failure time models with covariate measurement error. J Stat Softw. 2012;46(1):1–14.
   PubMedGoogle Scholar
• Wang X-F. NPsimex: nonparametric smoothing for contaminated data using simulation-extrapolation; 2011. Available from: https://CRAN.R-project.org/package=NPsimex. R package version 0.2-1.
   Google Scholar
• Wang X-F, Sun J, Fan Z. Deconvolution density estimation with heteroscedastic errors using simex. arXiv preprint arXiv:0902.2117; 2009.
   Google Scholar
• R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2018. Available from: https://www.R-project.org/.
   Google Scholar
• Venables WN, Ripley BD. Modern applied statistics with S. 4th ed. New York: Springer-Verlag; 2002. Available from: http://www.stats.ox.ac.uk/pub/MASS4. ISBN 0-387-95457-0.
   Google Scholar
• Soetaert K. rootSolve: nonlinear root finding, equilibrium and steady-state analysis of ordinary differential equations. R package version, 1; 2009. Available from: https://rdrr.io/rforge/rootSolve/.
   Google Scholar
• Soetaert K, Herman PMJ. A practical guide to ecological modelling: using R as a simulation platform. New York(NY): Springer-Verlag Science & Business Media; 2008.
   Google Scholar
• Hasselman B. nleqslv: solve systems of nonlinear equations; 2018. Available from: https://CRAN.R-project.org/package=nleqslv. R package version 3.3.2.
   Google Scholar
• Zeileis A, Croissant Y. Extended model formulas in R: multiple parts and multiple responses. J Stat Softw. 2010;34(1):1–13.doi:10.18637/jss.v034.i01.
   Web of Science ®Google Scholar
• Eddelbuettel D. Seamless R and C++ integration with Rcpp. New York(NY): Springer-Verlag; 2013.
   Google Scholar
• Eddelbuettel D, François R, Allaire J, et al. Rcpp: seamless R and C++ integration. J Stat Softw. 2011;40(8):1–18. doi: 10.18637/jss.v040.i08
   Web of Science ®Google Scholar
• Parker CC, Gopalakrishnan S, Carbonetto P, et al. Data from: genome-wide association study of behavioral, physiological and gene expression traits in outbred cfw mice; 2016a. Available from: https://doi.org/10.5061/dryad.2rs41.
   Google Scholar
• Parker CC, Gopalakrishnan S, Carbonetto P, et al. Genome-wide association study of behavioral, physiological and gene expression traits in outbred cfw mice. Nat Genet. 2016b;48(8):919. doi: 10.1038/ng.3609
   PubMed Web of Science ®Google Scholar