plot.optimal_exposure: Plot posterior distribution of optimal effect exposure values

In bdlnm: Bayesian Distributed Lag Non-Linear Models (B-DLNM)

Plot posterior distribution of optimal effect exposure values

Description

It plots a histogram of the posterior distribution of the optimal effect exposure values returned by optimal_exposure().

Usage

## S3 method for class 'optimal_exposure'
plot(x, show_median = TRUE, vline.arg = NULL, ...)

Arguments

x	An object of class "optimal_exposure" returned by optimal_exposure().
show_median	Logical. If TRUE (default) the function draws a vertical line for the median of all the posterior samples. If FALSE it doesn't draw any additional line.
vline.arg	Optional list of graphical arguments passed to graphics::abline() when drawing the median vertical line.
...	Optional graphical parameters passed to graphics::hist().

Details

The histogram uses the original prediction grid in attr(object, "xvar") as the x-axis values, ensuring that the bars align with prediction exposure values. The function plots the posterior distribution of the optimal exposure values (stored in x$est) and highlights the posterior median across samples (stored in x$summary[["0.5quant"]]) with a vertical line if show_median = TRUE. Use vline.arg to change the appearance of that line passed to graphics::abline() and ... to change the graphical parameters of the histogram passed to graphics::hist() (to control axis labels, title, colours, etc.). See the original functions for a complete list of the arguments. Some arguments, if not specified, are set to different default values than the original functions.

Value

No return value, called for side effects.

Author(s)

Pau Satorra, Marcos Quijal-Zamorano.

References

Quijal-Zamorano M., Martinez-Beneito M.A., Ballester J., Marí-Dell'Olmo M. (2024). Spatial Bayesian distributed lag non-linear models (SB-DLNM) for small-area exposure-lag-response epidemiological modelling. International Journal of Epidemiology, 53(3), dyae061. doi:10.1093/ije/dyae061.

Gasparrini A. (2011). Distributed lag linear and non-linear models in R: the package dlnm. Journal of Statistical Software, 43(8), 1-20. doi:10.18637/jss.v043.i08.

Armstrong B. Models for the relationship between ambient temperature and daily mortality. Epidemiology. 2006;17(6):624-31. doi:10.1097/01.ede.0000239732.50999.8f.

See Also

optimal_exposure() to estimate exposure values that optimize the predicted effect for a "bdlnm" object.

Examples

# Set exposure-response and lag-response spline parameters
 dlnm_var <- list(
   var_prc = c(10, 75, 90),
   var_fun = "ns",
   lag_fun = "ns",
   max_lag = 21,
   lagnk = 3
 )


# Set cross-basis parameters
 argvar <- list(fun = dlnm_var$var_fun,
                knots = stats::quantile(london$tmean,
                                 dlnm_var$var_prc/100, na.rm = TRUE),
                Bound = range(london$tmean, na.rm = TRUE))

 arglag <- list(fun = dlnm_var$lag_fun,
                knots = dlnm::logknots(dlnm_var$max_lag, nk = dlnm_var$lagnk))

 # Create crossbasis
 cb <- dlnm::crossbasis(london$tmean, lag = dlnm_var$max_lag, argvar, arglag)

 # Seasonality of mortality time series
 seas <- splines::ns(london$date, df = round(8 * length(london$date) / 365.25))

 # Prediction values (equidistant points)
 temp <- round(seq(min(london$tmean), max(london$tmean), by = 0.1), 1)
 # Ensure it falls inside the range of temperatures after rounding:
 temp <- temp[temp >= min(london$tmean) & temp <= max(london$tmean)]

if (check_inla()) {
 # Fit the model
 mod <- bdlnm(mort_75plus ~ cb + factor(dow) + seas, data = london,
 family = "poisson", sample.arg = list(n = 1000, seed = 432))

 # Find minimum risk exposure value
 mmt <- optimal_exposure(mod, exp_at = temp)

 # Plot
 plot(mmt, xlab = "Temperature (ºC)",
 main = paste0("MMT (Median = ", round(mmt$summary[["0.5quant"]], 1), "ºC)"))

}

bdlnm documentation built on June 20, 2026, 1:06 a.m.