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Diagnostic and analytical plots for hierarchical SDT models

Source: R/plot.R
plot.hsdt.Rd

Generates diagnostic visualizations for a fitted hierarchical SDT model, including observed versus latent regression (H3), shrinkage patterns, participant-level caterpillar intervals, and model-implied ROC curves.

Usage

# S3 method for class 'hsdt'
plot(
  x,
  type = c("regression", "shrinkage", "caterpillar", "roc"),
  subject_id = NULL,
  band = TRUE,
  population_reference = TRUE,
  observed_se = NULL,
  nrow = NULL,
  caption = TRUE,
  ...
)

Arguments

x

An hsdt object fitted by hsdt(), frequentist or Bayesian.

type

Character string indicating the plot type:

  • "regression": Compares the observed OLS regression with the latent regression line (H3).

  • "shrinkage": Connects observed \(d'\) to model-implied \(d'\) for each participant across bivariate contours.

  • "caterpillar": Compares observed \(d'\) and model-implied \(d'\) with confidence intervals for each participant alongside zero.

  • "roc": Shows model-implied ROC curves with estimated criteria.

subject_id

Identifier for a specific participant when type = "roc". If omitted, displays population-level curves.

band

Logical. Show confidence bands around regression lines or ROC curves (default is TRUE).

population_reference

Logical. When plotting an individual ROC curve, add population-average curves as dashed reference lines.

observed_se

Variance formulation for empirical intervals in "caterpillar" plots: "gg" (Gourevitch & Galanter, 1967, default) or "miller" (Miller, 1996).

nrow

Number of rows of the two panels when type = "regression": 1 (default) places them side by side as squares, 2 stacks the observed panel above the model one, each as wide as the figure.

caption

Logical. Show the caption that explains the plot under it (default TRUE). The shrinkage plot has none.

...

Additional arguments passed to underlying plotting methods.

Value

A ggplot object. Its underlying data frame is stored in $data.

Regression plot (type = "regression")

Compares observed and latent associations across two panels sharing axes, side by side or, with nrow = 2, one above the other. The first panel shows observed \(d'\) values and an ordinary least-squares line. When awareness task reliability is low, trial-level sampling noise attenuates this observed slope toward zero. The second panel plots the latent regression line (\(d'_I\) on \(d'_A\)) from H3, correcting for measurement error. The value of each line at \(d'_A = 0\) marks the intercept testing for unconscious processing, and both panels display it the same way: an open circle at the point estimate with a vertical line spanning its confidence interval. The observed marker is the least-squares intercept and the latent marker is its measurement-error-corrected counterpart, so the two panels place the same hypothesis side by side. Confidence bands are computed via the delta method or bootstrap replicates when usdt_boot() is present.

Shrinkage plot (type = "shrinkage")

Connects each participant's observed \(d'\) (from sdt_moments() with Hautus correction) to their model-implied \(d'\). The lower the reliability of the measures, the higher the shrinkage of observed estimates toward the group-level mean.

Caterpillar plot (type = "caterpillar")

Plots observed \(d'\) and model-implied \(d'\) with confidence intervals for every participant. Empirical intervals use normal approximations based on observed_se. Model-implied intervals incorporate uncertainty from population means, variance components, and participant random effects.

ROC plot (type = "roc")

Displays model-implied ROC curves for an average participant or a specific individual, with points marking the estimated response criteria.

Bayesian fits

For a fit with estimation = "bayesian", every model-estimated value is a posterior mean and every model band is a central credible interval, computed draw by draw: the latent line and its band, each subject's sensitivity in the shrinkage and caterpillar plots, and the ROC curves and operating points. The latent p-values are the two-sided posterior p-values of the hypothesis table. Under unequal variances the ROC curves bend with the signal standard deviation of each draw.

See also

hsdt(), sdt_moments(), usdt_boot()

Examples

# Contextual cuing data from Vadillo et al. (2025)
d <- usdt_data_tasks(
  awareness = vadillo_awareness,
  influence = vadillo_cuing,
  subject_col      = "subj",
  condition_col    = "condition",
  condition_levels = c(signal = "old", noise = "new"),
  response_col     = list(awareness = "judged.old", influence = "rt"),
  response_levels  = list(awareness = c(signal = 1, noise = 0),
                          influence = c(signal = "faster",
                                        noise = "slower")),
  dichotomize      = list(awareness = FALSE, influence = TRUE)
)

fit <- hsdt(d)

# 1. Observed vs. latent regression (H3), side by side or stacked
plot(fit, type = "regression")

plot(fit, type = "regression", nrow = 2)


# 2. Bivariate shrinkage toward the group mean
plot(fit, type = "shrinkage")


# 3. Participant-level intervals (observed vs. model-implied)
plot(fit, type = "caterpillar")


# 4. Model-implied ROC curves
plot(fit, type = "roc")

plot(fit, type = "roc", subject_id = "2001", population_reference = TRUE)