plot.baclava {baclava} | R Documentation |
Convenience function to facilitate exploration of posterior distributions through trace plots, autocorrelations, and densities, as well as plotting the estimated hazard for transitioning to the preclinical compartment.
## S3 method for class 'baclava'
plot(
x,
y,
...,
type = c("density", "trace", "acf", "hazard"),
burnin = 0L,
max_age = 90L,
trace_var = c("psi", "rate_H", "rate_P", "beta")
)
x |
An object of class |
y |
Ignored |
... |
Ignored |
type |
A character object. One of {"density", "trace", "acf", "hazard"}. The type of plot to generate |
burnin |
An integer object. Optional. The number of burn-in samples.
Used only for |
max_age |
A numeric object. For |
trace_var |
A character object. The parameter for which trace plots are to be generated. Must be one of {"psi", "rate_H", "rate_P", "beta"} |
A gg object
data(screen_data)
theta_0 <- list("rate_H" = 7e-4, "shape_H" = 2.0,
"rate_P" = 0.5 , "shape_P" = 1.0,
"beta" = 0.9, psi = 0.4)
prior <- list("rate_H" = 0.01, "shape_H" = 1,
"rate_P" = 0.01, "shape_P" = 1,
"a_psi" = 1/2 , "b_psi" = 1/2,
"a_beta" = 38.5, "b_beta" = 5.8)
# This is for illustration only -- the number of Gibbs samples should be
# significantly larger and the epsilon values should be tuned.
example <- fit_baclava(data.assess = data.screen,
data.clinical = data.clinical,
t0 = 30.0,
theta_0 = theta_0,
prior = prior)
plot(example)
plot(example, type = "trace", trace_var = "psi", burnin = 0L)
plot(example, type = "trace", trace_var = "rate_H", burnin = 0L)
plot(example, type = "trace", trace_var = "rate_P", burnin = 0L)
plot(example, type = "trace", trace_var = "beta", burnin = 0L)
plot(example, type = "acf")
plot(example, type = "hazard", max_age = 70)