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Show a model fit with pcvr in a ggplot layer

Source: R/stat_brms_model.R, R/stat_growthSS.R, R/stat_nlme_model.R, and 2 more
stat_growthss.Rd

Add a model fit with growthSS and fitGrowth to a ggplot object. The exact geom used depends on the model class (see details).

Usage

stat_brms_model(
  mapping = NULL,
  data = NULL,
  fit = NULL,
  ss = NULL,
  CI = 0.95,
  hierarchy_value = NULL,
  inherit.aes = TRUE,
  ...
)

stat_growthss(
  mapping = NULL,
  data = NULL,
  fit = NULL,
  ss = NULL,
  inherit.aes = TRUE,
  ...
)

stat_nlme_model(
  mapping = NULL,
  data = NULL,
  fit = NULL,
  ss = NULL,
  inherit.aes = TRUE,
  ...
)

stat_lme_model(
  mapping = NULL,
  data = NULL,
  fit = NULL,
  ss = NULL,
  inherit.aes = TRUE,
  ...
)

stat_nlrq_model(
  mapping = NULL,
  data = NULL,
  fit = NULL,
  ss = NULL,
  inherit.aes = TRUE,
  ...
)

stat_nls_model(
  mapping = NULL,
  data = NULL,
  fit = NULL,
  ss = NULL,
  inherit.aes = TRUE,
  ...
)

Arguments

mapping

Set of aesthetic mappings created by ggplot2::aes(). If specified and ‘inherit.aes = TRUE’ (the default), it is combined with the default mapping at the top level of the plot. If there is no mapping then it is filled in by default using the pcvrss object.

data

The data to be displayed in this layer. This behaves per normal ggplot2 expectations except that if data is missing (ie, not inherited or specified) then the data from ss is used.

fit

A model object returned from fitGrowth.

ss

A pcvrss-class object. Only the "pcvrForm" and "df" elements are used.

CI

A vector of credible intervals to plot, defaults to 0.95. Only used with brms models.

hierarchy_value

Value for the hierarchical variable, if applicable. Only used for hierarchical brms models. If left NULL (the default) the mean value is used.

inherit.aes

Logical, should aesthetics be inherited from top level? Defaults to TRUE.

...

Additional arguments passed to the ggplot layer.

Details

These layers will behave largely like output from growthPlot, although growthPlot has more arguments that directly control the plot since this stat only makes one layer. The geometries used for each type of model are:

  • brms: geom_ribbon for longitudinal plots, geom_rect for others.

  • nlrq: geom_line, replicated per each quantile.

  • nlme: geom_smooth, with ribbon based on the heteroskedastic term.

  • nls: geom_line.

  • nlrq: geom_smooth.

See also

growthPlot for a self-contained plotting function

Examples

library(ggplot2)
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,
  df = simdf, start = NULL, type = "nls"
)
#> Individual is not used with type = 'nls'.
fit <- fitGrowth(ss)
ggplot() +
  stat_growthss(fit = fit, ss = ss)