xpose.xtras (version 0.2.2)

catdv_vs_occ: Longitudinal binned observed vs. predicted plot for categorical DVs

Description

A longitudinal alternative to catdv_vs_ipred() and to xpose's own xpose::dv_preds_vs_idv() for categorical outcomes. Rather than binning by predicted probability (as catdv_vs_ipred() does) or plotting raw per-subject values against a continuous independent variable, this bins observations by a discrete, typically ordered grouping variable (eg an occ-typed occasion column) and plots the observed proportion meeting the cutpoint condition alongside the mean predicted probability, one point/line per bin.

Usage

catdv_vs_occ(
  xpdb,
  mapping = NULL,
  bin = NULL,
  cutpoint = 1,
  type = "pl",
  title = "Observed and predicted probability vs. @x | @run",
  subtitle = "Ofv: @ofv, Number of individuals: @nind",
  caption = "@dir",
  tag = NULL,
  facets,
  .problem,
  quiet,
  ...
)

Value

The desired plot

Arguments

xpdb

<xp_xtras> or <xpose_data> object

mapping

ggplot2 style mapping

bin

<tidyselect> Column to bin/group by. Defaults to the first occ-typed column (see set_var_types()). If that column has defined levels (see set_var_levels()), those labels (and their order) are used; otherwise raw values are coerced to a factor as-is.

cutpoint

<numeric> Of defined probabilities, which one to use in plots.

type

String setting the type of plot to be used: point p, line l, and smooth s, or any combination thereof. See xplot_binned().

title

Plot title

subtitle

Plot subtitle

caption

Plot caption

tag

Plot tag

facets

Additional facets

.problem

Problem number

quiet

Silence extra debugging output

...

Any additional aesthetics.

See Also

catdv_vs_ipred(), catdv_vs_dvprobs()

Examples

# Derive an occasion column (TIME is in hours here) and level it in
# visit order
vismo_xpdb <- vismo_pomod %>%
  set_var_types(.problem = 1, catdv = DV, dvprobs = matches("^P\\d+$")) %>%
  set_dv_probs(.problem = 1, 0~P0, 1~P1, ge(2)~P23) %>%
  xpose::mutate(OCC = ceiling((TIME + 1) / 24), .problem = 1) %>%
  set_var_types(.problem = 1, occ = OCC) %>%
  set_var_levels(.problem = 1, OCC = lvl_inord(paste("Day", 1:12)))

vismo_xpdb %>%
  catdv_vs_occ()

vismo_xpdb %>%
  catdv_vs_occ(cutpoint = 3)