                                Package ‘mcount’
                                          October 13, 2022
Type Package
Title Marginalized Count Regression Models
Version 1.0.0
Author Zhengyang Zhou [aut, cre]
      Dateng Li [aut]
      David Huh [aut]
      Eun-Young Mun [aut]
Depends R (>= 3.6)
Maintainer Zhengyang Zhou <zhengyang.zhou@unthsc.edu>
Description Implementation of marginalized models for zero-inflated count data. This package pro-
      vides a tool to implement an estimation algorithm for the marginalized count models, which
      directly makes inference on the effect of each covariate on the marginal mean of the outcome.
      The method involves the marginalized zero-inflated Poisson model described
      in Long et al. (2014) <doi:10.1002/sim.6293>.
License GPL-3
Encoding UTF-8
LazyData true
Imports bbmle, stats
NeedsCompilation no
RoxygenNote 7.1.2
Repository CRAN
Date/Publication 2022-03-11 10:30:05 UTC
R topics documented:
         dat.pfi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
         mzip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  2
Index                                                                                                    4
                                                     1
2                                                                                                   mzip
  dat.pfi                       Example Data
Description
    A data set from White et al. (2008), which is also described in Mun et al. (2015, 2022)
Usage
    data(dat.pfi)
Format
    The data fram contains 194 rows and 5 columns:
    m0 the number of standard alcohol drinks consumed at baseline
    int_PF 1: received personalized feedback interventions (PFI); 0: did not receive PFI
    year_new 1: first-year college student; 0: otherwise
    race_new 1: white; 0: non-white
    y the number of standard alcohol drinks consumed at post-intervention; the response variable
References
    Mun, E.-Y., Zhou, Z., Huh, D., Tan, L., Li, D., Tanner-Smith, E. E., Walters, S. T., & Larimer, M.E.
    (2022). Brief alcohol interventions are effective through six months: Findings from marginalized
    zero-inflated Poisson and negative binomial models in a two-step IPD meta-analysis. Prevention
    Science. (under review)
    Mun, E. Y., De La Torre, J., Atkins, D. C., White, H. R., Ray, A. E., Kim, S. Y., ... & The
    Project INTEGRATE Team. (2015). Project INTEGRATE: An integrative study of brief alcohol
    interventions for college students. Psychology of Addictive Behaviors, 29(1), 34-48.
    White, H. R., Mun, E.-Y., & Morgan, T. J. (2008). Do brief personalized feedback interventions
    work for mandated students or is it just getting caught that works? Psychology of Addictive Behav-
    iors, 22 (1), 107–116. https://doi.org/10.1037/0893-164X.22.1.107.
  mzip                          Estimating marginalized zero-inflated Poisson model
Description
    Function to estimate a marginalized zero-inflated Poisson model
mzip                                                                                                  3
Usage
    mzip(formula, data)
Arguments
    formula             an object of class "formula" (or one that can be coerced to that class): a sym-
                        bolic description of the model to be fitted. A typical formula has the form
                        response ~ terms where response is the count response vector and terms is a
                        series of terms that predict response. For example, formula = y ~ x1 + x2 + x3.
                        Do not write intercept in the formula; intercept will be automatically added in
                        model fitting.
    data                a data frame containing variables in the model.
Details
    Function returns an object of class "mle2" from bbmle R package. Apply summary function to the
    resulting object from the function to obtain more estimation information.
Value
    Suffix _zero corresponds to the parameters associated with the structrual zero rate part of a model.
    Suffix _mean corresponds to the parameters associated with the overall mean, which evaluate the
    effects of covariates on the overall mean.
References
    Long, D. L., Preisser, J. S., Herring, A. H., & Golin, C. E. (2014). A marginalized zero-inflated
    Poisson regression model with overall exposure effects. Statistics in Medicine, 33(29), 5151-5165.
Examples
    head(dat.pfi)
    #Fit a marginalized zero-inflated Poisson model
    res = mzip(formula = y ~ m0 + int_PF + year_new + race_new, data = dat.pfi)
    #Obtain estimation results
    bbmle::summary(res)
Index
∗ datasets
     dat.pfi, 2
dat.pfi, 2
mzip, 2
                4
