rstanarm: plot.predict.stanjm – R documentation

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plot.predict.stanjm

Plot the estimated subject-specific or marginal longitudinal trajectory

Description

This generic plot method for predict.stanjm objects will plot the estimated subject-specific or marginal longitudinal trajectory using the data frame returned by a call to posterior_traj. To ensure that enough data points are available to plot the longitudinal trajectory, it is assumed that the call to posterior_traj would have used the default interpolate = TRUE, and perhaps also extrapolate = TRUE (the latter being optional, depending on whether or not the user wants to see extrapolation of the longitudinal trajectory beyond the last observation time).

Usage

## S3 method for class 'predict.stanjm'
plot(
  x,
  ids = NULL,
  limits = c("ci", "pi", "none"),
  xlab = NULL,
  ylab = NULL,
  vline = FALSE,
  plot_observed = FALSE,
  facet_scales = "free_x",
  ci_geom_args = NULL,
  grp_overlay = FALSE,
  ...
)

Arguments

`x`	A data frame and object of class `predict.stanjm` returned by a call to the function `posterior_traj`. The object contains point estimates and uncertainty interval limits for the fitted values of the longitudinal response.
`ids`	An optional vector providing a subset of subject IDs for whom the predicted curves should be plotted.
`limits`	A quoted character string specifying the type of limits to include in the plot. Can be one of: `"ci"` for the Bayesian posterior uncertainty interval for the estimated mean longitudinal response (often known as a credible interval); `"pi"` for the prediction interval for the estimated (raw) longitudinal response; or `"none"` for no interval limits.
`xlab, ylab`	An optional axis label passed to `labs`.
`vline`	A logical. If `TRUE` then a vertical dashed line is added to the plot indicating the event or censoring time for the individual. Can only be used if each plot within the figure is for a single individual.
`plot_observed`	A logical. If `TRUE` then the observed longitudinal measurements are overlaid on the plot.
`facet_scales`	A character string passed to the `scales` argument of `facet_wrap` when plotting the longitudinal trajectory for more than one individual.
`ci_geom_args`	Optional arguments passed to `geom_ribbon` and used to control features of the plotted interval limits. They should be supplied as a named list.
`grp_overlay`	Only relevant if the model had lower level units clustered within an individual. If `TRUE`, then the fitted trajectories for the lower level units will be overlaid in the same plot region (that is, all lower level units for a single individual will be shown within a single facet). If `FALSE`, then the fitted trajectories for each lower level unit will be shown in a separate facet.
`...`	Optional arguments passed to `geom_smooth` and used to control features of the plotted longitudinal trajectory.

Value

A ggplot object, also of class plot.predict.stanjm. This object can be further customised using the ggplot2 package. It can also be passed to the function plot_stack_jm.

Examples

# Run example model if not already loaded
  if (!exists("example_jm")) example(example_jm)
  
  # For a subset of individuals in the estimation dataset we will
  # obtain subject-specific predictions for the longitudinal submodel 
  # at evenly spaced times between 0 and their event or censoring time.
  pt1 <- posterior_traj(example_jm, ids = c(7,13,15), interpolate = TRUE)
  plot(pt1)                  # credible interval for mean response
  plot(pt1, limits = "pi")   # prediction interval for raw response
  plot(pt1, limits = "none") # no uncertainty interval
  
  # We can also extrapolate the longitudinal trajectories.
  pt2 <- posterior_traj(example_jm, ids = c(7,13,15), interpolate = TRUE,
                        extrapolate = TRUE)
  plot(pt2)
  plot(pt2, vline = TRUE)    # add line indicating event or censoring time
  plot(pt2, vline = TRUE, plot_observed = TRUE)  # overlay observed longitudinal data
 
  # We can change or add attributes to the plot
  plot1 <- plot(pt2, ids = c(7,13,15), xlab = "Follow up time",
                     vline = TRUE, plot_observed = TRUE, 
                     facet_scales = "fixed", color = "blue", linetype = 2,
                     ci_geom_args = list(fill = "red"))
  plot1
       
  # Since the returned plot is also a ggplot object, we can
  # modify some of its attributes after it has been returned
  plot1 + 
    ggplot2::theme(strip.background = ggplot2::element_blank()) +
    ggplot2::labs(title = "Some plotted longitudinal trajectories")

rstanarm

Bayesian Applied Regression Modeling via Stan

v2.21.1

GPL (>= 3)

Authors

Jonah Gabry [aut], Imad Ali [ctb], Sam Brilleman [ctb], Jacqueline Buros Novik [ctb] (R/stan_jm.R), AstraZeneca [ctb] (R/stan_jm.R), Trustees of Columbia University [cph], Simon Wood [cph] (R/stan_gamm4.R), R Core Deveopment Team [cph] (R/stan_aov.R), Douglas Bates [cph] (R/pp_data.R), Martin Maechler [cph] (R/pp_data.R), Ben Bolker [cph] (R/pp_data.R), Steve Walker [cph] (R/pp_data.R), Brian Ripley [cph] (R/stan_aov.R, R/stan_polr.R), William Venables [cph] (R/stan_polr.R), Paul-Christian Burkner [cph] (R/misc.R), Ben Goodrich [cre, aut]

Initial release

2020-07-20