ridgePlot: Calculate and plot posterior densities of the projected estimates

Description

The function ridgePlot replaces the previous function name getSmoothedDensity (before version 1.0.0).

Usage

ridgePlot(
  x = NULL,
  nsim = 1000,
  draws = NULL,
  year_plot = NULL,
  strata_plot = NULL,
  by.year = TRUE,
  ncol = 4,
  scale = 2,
  per1000 = FALSE,
  order = 0,
  direction = 1,
  linewidth = 0.5,
  results = NULL,
  save.density = FALSE,
  ...
)
getSmoothedDensity(
  x = NULL,
  nsim = 1000,
  draws = NULL,
  year_plot = NULL,
  strata_plot = NULL,
  by.year = TRUE,
  ncol = 4,
  scale = 2,
  per1000 = FALSE,
  order = 0,
  direction = 1,
  linewidth = 0.5,
  results = NULL,
  save.density = FALSE,
  ...
)

Value

ridge plot of the density, and if save.density = TRUE, also a data frame of the calculated densities

Arguments

x: output from smoothDirect for the smoothed direct estimates, or smoothCluster for the cluster-level estimates.
nsim: number of posterior draws to take. Only used for cluster-level models when draws is NULL. Otherwise the posterior draws in draws will be used instead without resampling.
draws: Output of getSmoothed with save.draws set to TRUE. This argument allows the previously sampled draws (by setting save.draws to be TRUE) be used in new aggregation tasks. This argument is only used for cluster-level models.
year_plot: A vector indicate which years to plot
strata_plot: Name of the strata to plot. If not specified, the overall is plotted.
by.year: logical indicator for whether the output uses years as facets.
ncol: number of columns in the output figure.
scale: numerical value controlling the height of the density plots.
per1000: logical indicator to multiply results by 1000.
order: order of regions when by.year is set to TRUE. Negative values indicate regions are ordered from high to low posterior medians from top to bottom. Positive values indicate from low to high. 0 indicate alphabetic orders.
direction: Direction of the color scheme. It can be either 1 (smaller values are darker) or -1 (higher values are darker). Default is set to 1.
linewidth: width of the ridgeline.
results: output from ridgePlot returned object with save.density = TRUE. This argument can be specified to avoid calculating densities again when only the visualization changes.
save.density: Logical indicator of whether the densities will be returned with the ggplot object. If set to TRUE, the output will be a list consisting of (1) a data frame of computed densities and (2) a ggplot object of the plot.
...: additional configurations passed to inla.posterior.sample.

Author

Zehang Richard Li

Examples

Run this code

if (FALSE) {
years <- levels(DemoData[[1]]$time)

data <- getDirectList(births = DemoData, 
years = years,  
regionVar = "region", timeVar = "time", 
clusterVar = "~clustid+id", 
ageVar = "age", weightsVar = "weights", 
geo.recode = NULL)
# obtain direct estimates
data_multi <- getDirectList(births = DemoData, years = years,
  regionVar = "region",  timeVar = "time", clusterVar = "~clustid+id",
  ageVar = "age", weightsVar = "weights", geo.recode = NULL)
data <- aggregateSurvey(data_multi)

#  national model
years.all <- c(years, "15-19")
fit1 <- smoothDirect(data = data, geo = NULL, Amat = NULL, 
  year_label = years.all, year_range = c(1985, 2019), 
  rw = 2, m = 5)
## Plot marginal posterior densities over time
ridgePlot(fit1, year_plot = years.all, 
          ncol = 4, by.year = FALSE)

#  subnational model
fit2 <- smoothDirect(data = data, geo = DemoMap$geo, Amat = DemoMap$Amat, 
  year_label = years.all, year_range = c(1985, 2019), 
  rw = 2, m = 5, type.st = 1)

# Plot marginal posterior densities over time (regions are ordered alphabetically)
ridgePlot(fit2, year_plot = years.all, ncol = 4)

# Re-order the regions and save the density to avoid re-compute later
density <- ridgePlot(fit2, year_plot = years.all,
 ncol = 4, per1000 = TRUE, order = -1, save.density = TRUE)
density$g

# Show each region (instead of each year) in a panel 
## Instead of recalculate the posteriors, we can use previously calculated densities as input 
ridgePlot(results = density, year_plot = years.all, 
ncol = 4, by.year=FALSE, per1000 = TRUE)

# Show more years
ridgePlot(results = density, year_plot = c(1990:2019), 
ncol = 4, by.year=FALSE, per1000 = TRUE)



}