We discuss implementation of a profile likelihood method for estimating a Pearson correla tion coefficient from bivariate data with censoring and/or missing values. The method is implemented in an R package clikcorr which calculates maximum likelihood estimates of the correlation coefficient when the data are modeled with either a Gaussian or a Student t-distribution, in the presence of left, right, or interval censored and/or missing data. The R package includes functions for conducting inference and also provides graphical functions for visualizing the censored data scatter plot and profile log likelihood function. The performance of clikcorr in a variety of circumstances is evaluated through extensive simulation studies. We illustrate the package using two dioxin exposure datasets.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-040.zip
ClinicalTrials, Distributions, Econometrics, Finance, Multivariate, SocialSciences, Survival
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For attribution, please cite this work as
Li, et al., "The R Journal: Profile Likelihood Estimation of the Correlation Coefficient in the Presence of Left, Right or Interval Censoring and Missing Data", The R Journal, 2018
BibTeX citation
@article{RJ-2018-040,
author = {Li, Yanming and Gillespie, Brenda W. and Shedden, Kerby and Gillespie, John A.},
title = {The R Journal: Profile Likelihood Estimation of the Correlation Coefficient in the Presence of Left, Right or Interval Censoring and Missing Data},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-040},
doi = {10.32614/RJ-2018-040},
volume = {10},
issue = {2},
issn = {2073-4859},
pages = {159-179}
}