bmrvarx_simulations: Generated datasets for bmrvarx simulations

In nickseedorff/bmrarm: Bayesian Mixed Response Autoregressive Models

Generated datasets for bmrvarx simulations

Description

A list of data frames used in the simulated bmrvarx examples. Data frame 167 from bmrvarx_simulations is the randomly selected dataset used to assess convergence.

Usage

bmrvarx_simulations

Format

Two lists of data frames. One list contains data generated with an autoregressive term, the other without:

obs_num

Time since baseline

y1

true continuous values for the ordinal outcome, unused for modeled

y_ord

ordinal outcome, obtained from discretizing y1

y2

observed continuous outcome

pat_idx

patient identifier, integer

time

time since baseline, integer

data_type

identifies if data was used for training or testing. Only applicable to ar_data_list data frames as all data was training data for the slope_data_list data frames.

Examples

## Not run: 
# Assess convergence by fitting four separate chains
burn <- 5000; sims <- 25000; mod_list <- list()

for(i in 1:4) {
  mod_list[[i]] <- bmrvarx(formula = cbind(y_ord, y2) ~ time,
    data = bmrvarx_simulations$ar_data_list[[167]],
    ordinal_outcome = "y_ord", patient_var = "pat_idx",
    random_slope = T, time_var = "time", ar_cov = T,
    burn_in = burn, nsim = sims, thin = 5, seed = i,
    sd_vec = c(0.14, 0.14, 0.35, 0.1, 0.23, 0.09))
}

# Fit a bmrvarx to each of the 400 AR datasets (\eqn{rho = 0.35})
for(i in 1:400){
  samps <- bmrvarx(formula = cbind(y_ord, y2) ~ time,
    data = bmrvarx_simulations$ar_data_list[[i]],
    ordinal_outcome = "y_ord", patient_var = "pat_idx",
    random_slope = T, time_var = "time", ar_cov = T,
    burn_in = burn, nsim = sims, thin = 5, seed = i,
    sd_vec = c(0.14, 0.15, 0.35, 0.1, 0.2, 0.09))
  mDIC <- get_DIC(samps)
  cDIC <- get_DIC(samps, marginal = FALSE)
  f_out <- paste0("./ar_model_ar_data", i, ".RDS")
  saveRDS(list(samps = samps, data = sim_data), file = f_out)
}

## End(Not run)

bmrvarx_simulations

nickseedorff/bmrarm documentation built on April 17, 2025, 9:43 p.m.