Creates a ggplot2 plot with predictions and observations, split by subject

Usage

luna_ind(
  id,
  folder = NULL,
  data = NULL,
  theme = ggplot2::theme_classic,
  smooth_method = "loess",
  exponentiate = FALSE,
  show = list(dv = TRUE, ipred = TRUE, pred = TRUE, doses = TRUE, se = TRUE),
  se_factor = 1,
  ltbs = FALSE,
  ncol = 3,
  nrow = 3,
  page = 1,
  scales = NULL,
  verbose = TRUE
)

Arguments

id: run id, e.g. run1. This will be the folder in which the NONMEM model is run.
folder: path to folder containing the model file. Default is current directory.
data: instead of getting an output table from a run, supply a data.frame with the relevant data columns.
smooth_method: ggplot2-supported smooth method, e.g. "loess"
exponentiate: exponentiate DV, PRED, and IPRED columns?
se_factor: when plotting uncertainty for IPRED, factor to multiply the SE with. Default is 1, i.e. show the standard error. If set to 1.96, it will show the 95% CI. (Use qnorm() to find relevant factor for CI)
ltbs: log-transform-both-sides error model? If TRUE, will exponentiate DV, PRED, and IPRED.
ncol: number of faceting columns to show
nrow: number of faceting rows to show
page: page number of plots to show
scales: passed to facet_wrap() function as the scales argument, can be "free", "free_x", or "free_y".
subject_ids: list of subject IDs to be plotted.