brmPlot: Function to visualize brms models similar to those made using...

In pcvr: Plant Phenotyping and Bayesian Statistics

Function to visualize brms models similar to those made using growthSS outputs.

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

brmPlot(
  fit,
  form,
  df = NULL,
  groups = NULL,
  timeRange = NULL,
  facetGroups = TRUE,
  hierarchy_value = NULL,
  vir_option = "plasma"
)

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.
hierarchy_value	If a hierarchical model is being plotted, what value should the hierarchical predictor be? If left NULL (the default) the mean value is used. If this is >1L then the x axis will use the hierarchical variable from the model at the mean of the timeRange (mean of x values in the model if timeRange is not specified).
vir_option	Viridis color scale to use for plotting credible intervals. Defaults to "plasma".

Value

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

Examples

simdf <- growthSim(
  "logistic",
  n = 20, t = 25,
  params = list("A" = c(200, 160), "B" = c(13, 11), "C" = c(3, 3.5))
)
ss <- growthSS(
  model = "logistic", form = y ~ time | id / group, sigma = "spline",
  list("A" = 130, "B" = 10, "C" = 3),
  df = simdf, type = "brms"
)
fit <- fitGrowth(ss, backend = "cmdstanr", iter = 500, chains = 1, cores = 1)
growthPlot(fit = fit, form = y ~ time | group, groups = "a", df = ss$df)

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