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Longitudinal binned observed vs. predicted plot for categorical DVs

Source: R/categorical.R
catdv_vs_occ.Rd

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,
  ...
)

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.

Value

The desired plot

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)