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Plot Choice Model Fit

Source: R/plot_data.R
plot_fit_choice.Rd

Plot observed and predicted fit for choice-only response models.

Usage

plot_fit_choice(
  input,
  post_predict = NULL,
  prior_predict = NULL,
  subject = NULL,
  quants = c(0.025, 0.975),
  functions = NULL,
  factors = NULL,
  signalFactor = "S",
  n_cores = 1,
  n_post = 50,
  layout = NA,
  style = c("prob", "cumulative", "roc"),
  to_plot = c("data", "posterior", "prior")[1:2],
  legendpos = "topright",
  posterior_args = list(),
  prior_args = list(),
  zROC = FALSE,
  qfun = qnorm,
  lim = NULL,
  ...
)

Arguments

input

Either an emc object or a data frame, or a list of such objects.

post_predict

Optional posterior predictive data (matching columns) or list thereof.

prior_predict

Optional prior predictive data (matching columns) or list thereof.

subject

Subset the data to a single subject (by index or name).

quants

Numeric vector of credible interval bounds (e.g. c(0.025, 0.975)).

functions

A function (or list of functions) that create new columns in the datasets or predictives

factors

Character vector of factor names to aggregate over; defaults to plotting full data set ungrouped by factors if NULL.

signalFactor

The factor defining signal and noise classes for ROC plots.

n_cores

Number of CPU cores to use if generating predictives from an emc object.

n_post

Number of posterior draws to simulate if needed for predictives.

layout

Numeric vector used in par(mfrow=...); use NA for auto-layout.

style

A string indicating which choice fit plot to draw: "prob", "cumulative", or "roc".

to_plot

Character vector: any of "data", "posterior", "prior".

legendpos

Character vector controlling the positions of the legends

posterior_args

Optional list of graphical parameters for posterior lines/ribbons.

prior_args

Optional list of graphical parameters for prior lines/ribbons.

zROC

Boolean; if TRUE, plot a z-transformed ROC.

qfun

Quantile function used when zROC = TRUE.

lim

Optional common limits for ROC or zROC plots.

...

Other graphical parameters for the real data lines.

Value

Invisibly returns a list with the plotted summaries for each source.

Details

The default style = "prob" compares observed response probabilities to posterior and/or prior predictive intervals. For ordered response models, style = "cumulative" plots cumulative response probabilities. For SDT-like two-signal designs, style = "roc" plots observed and predictive ROC or zROC curves.

Examples

# dmnl <- design(
#   Rlevels = c("left", "right", "up"),
#   factors = list(subjects = 1, S = c("left", "right", "up")),
#   formula = list(utility ~ lM),
#   contrasts = list(utility = list(lM = matrix(c(-1/2, 1/2), ncol = 1))),
#   matchfun = function(d) d$S == d$lR,
#   model = multinomial_logit
# )
# dat <- make_data(c(utility = 0, utility_lM1 = 2), dmnl, n_trials = 40)
# plot_fit_choice(dat, style = "prob", factors = "S")