brmSurvPlot: Function to visualize brms survival models specified using...

In pcvr: Plant Phenotyping and Bayesian Statistics

Function to visualize brms survival models specified using growthSS.

Description

Models fit using growthSS inputs by fitGrowth (and similar models made through other means) can be visualized easily using this function. This will generally be called by growthPlot.

Usage

brmSurvPlot(
  fit,
  form,
  df = NULL,
  groups = NULL,
  timeRange = NULL,
  facetGroups = TRUE
)

Arguments

fit	A brmsfit object, similar to those fit with growthSS outputs.
form	A formula similar to that in growthSS inputs specifying the outcome, predictor, and grouping structure of the data as outcome ~ predictor|individual/group.
df	An optional dataframe to use in plotting observed growth curves on top of the model.
groups	An optional set of groups to keep in the plot. Defaults to NULL in which case all groups in the model are plotted.
timeRange	An optional range of times to use. This can be used to view predictions for future data if the available data has not reached some point (such as asymptotic size), although prediction using splines outside of the observed range is not necessarily reliable.
facetGroups	logical, should groups be separated in facets? Defaults to TRUE.

Value

Returns a ggplot showing a brms model's credible intervals and optionally the individual growth lines.

Examples

set.seed(123)
df <- growthSim("exponential",
  n = 20, t = 50,
  params = list("A" = c(1, 1), "B" = c(0.15, 0.1))
)
ss1 <- growthSS(
  model = "survival weibull", form = y > 100 ~ time | id / group,
  df = df, start = c(0, 5)
)
fit1 <- fitGrowth(ss1, iter = 600, cores = 2, chains = 2, backend = "cmdstanr")
brmSurvPlot(fit1, form = ss1$pcvrForm, df = ss1$df)

# note that using the cumulative hazard to calculate survival is likely to underestimate
# survival in these plots if events do not start immediately.
ss2 <- growthSS(
  model = "survival binomial", form = y > 100 ~ time | id / group,
  df = df, start = c(-4, 3)
)
fit2 <- fitGrowth(ss2, iter = 600, cores = 2, chains = 2, backend = "cmdstanr")
brmSurvPlot(fit2, form = ss2$pcvrForm, df = ss2$df)

pcvr documentation built on April 16, 2025, 5:12 p.m.