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We consider a Bayesian analysis using WinBUGS to estimate the distribution of usual intake for episodically consumed foods and energy (calories). The model uses measures of nutrition and energy intakes via a food frequency questionnaire (FFQ) along with repeated 24 hour recalls and adjusting covariates. In order to estimate the usual intake of the food, we phrase usual intake in terms of person-specific random effects, along with day-to-day variability in food and energy consumption. Three levels are incorporated in the model. The first level incorporates information about whether an individual in fact reported consumption of a particular food item. The second level incorporates the amount of intake from those individuals who reported consumption of the food, and the third level incorporates the energy intake. Estimates of posterior means of parameters and distributions of usual intakes are obtained by using Markov chain Monte Carlo calculations. This R function reports to users point estimates and credible intervals for parameters in the model, samples from their posterior distribution, samples from the distribution of usual intake and usual energy intake, trace plots of parameters and summary statistics of usual intake, usual energy intake and energy adjusted usual intake.

journal of statistical software

<tt>Intake_epis_food()</tt>: An<i>R</i>Function for Fitting a Bivariate Nonlinear Measurement Error Model to Estimate Usual and Energy Intake for Episodically Consumed Foods

Intake_epis_food(): AnRFunction for Fitting a Bivariate Nonlinear Measurement Error Model to Estimate Usual and Energy Intake for Episodically Consumed Foods

`Intake_epis_food()`: AnRFunction for Fitting a Bivariate Nonlinear Measurement Error Model to Estimate Usual and Energy Intake for Episodically Consumed Foods