Bayes_BPREM {BEND}

Bayesian Bivariate Piecewise Random Effects Model (BPREM)

Description

Estimates a Bayesian bivariate piecewise random effects models (BPREM) for longitudinal data with two interrelated outcomes. See Peralta et al. (2022) for more details.

Usage

Bayes_BPREM(
  data,
  id_var,
  time_var,
  y1_var,
  y2_var,
  iters_adapt = 5000,
  iters_burn_in = 1e+05,
  iters_sampling = 50000,
  thin = 15,
  save_full_chains = FALSE,
  save_conv_chains = FALSE,
  verbose = TRUE
)

Arguments

data

Data frame in long format, where each row describes a measurement occasion for a given individual. It is assumed that each individual has the same number of assigned timepoints (a.k.a., rows). There can be missingness in the outcomes (y1_var and y2_var), but there cannot be missingness in time (time_var).

id_var

Name of column that contains ids for individuals with repeated measures in a longitudinal dataset.

time_var

Name of column that contains the time variable. This column cannot contain any missing values.

y1_var

Name of column that contains the first outcome variable. Missing values should be denoted by NA.

y2_var

Name of column that contains the second outcome variable. Missing values should be denoted by NA.

iters_adapt

(optional) Number of iterations for adaptation of jags model (default = 5000).

iters_burn_in

(optional) Number of iterations for burn-in (default = 100000).

iters_sampling

(optional) Number of iterations for posterior sampling (default = 50000).

thin

(optional) Thinning interval for posterior sampling (default = 15).

save_full_chains

Logical indicating whether the MCMC chains from rjags should be saved (default = FALSE). Note, this should not be used regularly as it will result in an object with a large file size.

save_conv_chains

Logical indicating whether the MCMC chains from rjags should be saved but only for the parameters monitored for convergence (default = FALSE). This would be useful for plotting traceplots for relevant model parameters to evaluate convergence behavior. Note, this should not be used regularly as it will result in an object with a large file size.

verbose

Logical controlling whether progress messages/bars are generated (default = TRUE).

Details

For more information on the model equation and priors implemented in this function, see Peralta et al. (2022).

Value

A list (an object of class BPREM) with elements:

Convergence

Potential scale reduction factor (PSRF) for each parameter (parameter_psrf), Gelman multivariate scale reduction factor (multivariate_psrf), and mean PSRF (mean_psrf) to assess model convergence.

Model_Fit

Deviance (deviance), effective number of parameters (pD), and Deviance information criterion (dic) to assess model fit.

Fitted_Values

Vector giving the fitted value at each timepoint for each individual (same length as long data).

Parameter_Estimates

Data frame with posterior mean and 95% credible intervals for each model parameter.

Run_Time

Total run time for model fitting.

Full_MCMC_Chains

If save_full_chains=TRUE, raw MCMC chains from rjags.

Convergence_MCMC_Chains

If save_conv_chains=TRUE, raw MCMC chains from rjags but only for the parameters monitored for convergence.

Author(s)

Corissa T. Rohloff, Yadira Peralta

References

Peralta, Y., Kohli, N., Lock, E. F., & Davison, M. L. (2022). Bayesian modeling of associations in bivariate piecewise linear mixed-effects models. Psychological Methods, 27(1), 44–64. https://doi.org/10.1037/met0000358

Examples

# load simulated data
data(SimData_BPREM)
# plot observed data
plot_BEND(data = SimData_BPREM,
          id_var = "id",
          time_var = "time",
          y_var = "y1",
          y2_var = "y2")
# fit Bayes_BPREM()
results_bprem <- Bayes_BPREM(data = SimData_BPREM,
                             id_var = "id",
                             time_var = "time",
                             y1_var = "y1",
                             y2_var = "y2")
# result summary
summary(results_bprem)
# plot fitted results
plot_BEND(data = SimData_BPREM,
          id_var = "id",
          time_var = "time",
          y_var = "y1",
          y2_var = "y2",
          results = results_bprem)