plot.apc: Plot apc object

In bamp: Bayesian Age-Period-Cohort Modeling and Prediction

Plot apc object

Description

Plot apc object

Usage

## S3 method for class 'apc'
plot(
  x,
  quantiles = c(0.05, 0.5, 0.95),
  convention = c("age", "period", "cohort", "none"),
  combined = FALSE,
  ...
)

Arguments

x	apc object
quantiles	quantiles to plot. Default: c(0.05,0.5,0.95) is median and 90% credible interval.
convention	display-layer gauge convention for the linear trend (drift) in a full age-period-cohort model; one of "age" (default), "period", "cohort" or "none". In a full APC model the three effects are identifiable only up to a shared linear trend; this fixes that single degree of freedom, removing the run-to-run drift that is the dominant source of non-reproducibility in the plotted curves (residual curvature and Monte-Carlo noise remain). "age" shows the age effect as curvature about a zero trend and puts the drift in the period and cohort effects; "none" plots the raw, un-gauged effects. It is display-only (the fitted rates and predictions are unchanged) and is ignored for models that are not full APC. See effects.apc.
combined	logical. For heterogeneity models, if TRUE plot the full effect (smooth + iid heterogeneity); default FALSE plots the smooth component only. Ignored for models without heterogeneity.
...	Additional arguments will be ignored

Details

Plot of age, period and cohort effects from apc objects. If covariates have been used for period/cohort, a second plot with covariate, absolute effect and relative effect is created. Absolute effect is relative effect times covariate.

Value

plot

Examples

## Not run: 
data(apc)
model <- bamp(cases, population, age="rw1", period="rw1", cohort="rw1", periods_per_agegroup = 5)
plot(model)

## End(Not run)

bamp documentation built on Sept. 1, 2026, 1:08 a.m.