Plotting Bayesian B-spline model — plot

function for plotting Bayesian B-spline model for estimating reference series and/or assessing aging bias

Usage

plot_fit(
  df,
  ext,
  probs = c(0.05, 0.5, 0.95),
  post.den = TRUE,
  legend = TRUE,
  min.BY,
  max.BY,
  inset.control = list(width = 0.003, height = 0.125, sig_low = 0.3, adj_low = 0.05,
    xbump = 0, lab_adj = 0.18)
)

Arguments

df: data list object from data_prep and used to estimate the STAN model
ext: posterior draws extraction list from extract_draws function
probs: vector of three probabilities for lower CI, centrality, upper CI
post.den: logical flag to plot posterior densities of certain parameters
legend: logical flag to plot legend
inset.control: a list with values of width (width of the inset posterior in proportions of xlim), height (height of the inset posterior in proportions of ylim), sig_low (lower position of sigma posterior in proportions of ylim), adj_low (lower position of BY_adj posterior in proportions of ylim), xbump (translation of posteriors along x-axis in proportions of xlim).

Value

figure only, invisible

Examples

#REFERENCE-ONLY
df <- data_prep(sim_ref)
#> [1] "B-spline model indicated, adding additional data values for spline control"
#> No values provided in df_unk, skipping validation and estimating reference series only

#estimate model
fit <- est_model(df, show_messages = FALSE, show_exceptions = FALSE)

#extract draws
draws <- extract_draws(fit)

#plot fit
plot_fit(df, draws)


#INTEGRATED
if (FALSE) { # \dontrun{
df_int <- data_prep(sim_ref, sim_unk)
fit_int <- est_model(df_int)
draws_int <- extract_draws(fit_int)
plot_fit(df_int, draws_int)
} # }