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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,
  ...
)

Arguments

x

An hsdt object fitted by hsdt().

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).

...

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. The left panel shows observed \(d'\) values and an ordinary least-squares line. When direct task reliability is low, trial-level sampling noise attenuates this observed slope toward zero. The right panel plots the latent regression line (\(d'_I\) on \(d'_D\)) from H3, correcting for measurement error. The value of each line at \(d'_D = 0\) 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.

See also

hsdt(), sdt_moments(), usdt_boot()

Examples

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

fit <- hsdt(d)

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


# 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)