Autoplot method for neuralGAM objects (epistemic-only)

Produce effect/diagnostic plots from a fitted neuralGAM model. Supported panels:

which = "response": fitted response vs. index, with optional epistemic confidence intervals (CI).
which = "link": linear predictor (link scale) vs. index, with optional CI.
which = "terms": single per-term contribution \(g_j(x_j)\) on the link scale, with optional CI band for the smooth (epistemic).

# S3 method for class 'neuralGAM'
autoplot(
  object,
  newdata = NULL,
  which = c("response", "link", "terms"),
  interval = c("none", "confidence"),
  level = 0.95,
  forward_passes = 150,
  term = NULL,
  rug = TRUE,
  ...
)

Arguments

object: A fitted neuralGAM object.
newdata: Optional data.frame/list of covariates. If omitted, training data are used.
which: One of c("response","link","terms"). Default "response".
interval: One of c("none","confidence"). Default "confidence".
level: Coverage level for confidence intervals (e.g., 0.95). Default 0.95.
forward_passes: Integer. Number of MC-dropout forward passes used when uncertainty_method %in% c("epistemic","both").
term: Single term name to plot when which = "terms".
rug: Logical; if TRUE (default), add rugs to continuous term plots.
...: Additional arguments passed to predict.neuralGAM.

Value

A single ggplot object.

Details

Uncertainty semantics (epistemic only)

CI: Uncertainty about the fitted mean.
For the response, SEs are mapped via the delta method;
For terms, bands are obtained as \(\hat g_j \pm z \cdot SE(\hat g_j)\) on the link scale.

Author

Ines Ortega-Fernandez, Marta Sestelo

Examples

# \dontrun{

library(neuralGAM)
dat <- sim_neuralGAM_data()
train <- dat$train
test  <- dat$test

ngam <- neuralGAM(
  y ~ s(x1) + x2 + s(x3),
  data = train, family = "gaussian", num_units = 128,
  uncertainty_method = "epistemic", forward_passes = 10
)
#> [1] "Initializing neuralGAM..."
#> [1] "BACKFITTING Iteration 1 - Current Err =  0.00458219467442825 BF Threshold =  0.001 Converged =  FALSE"
#> [1] "BACKFITTING Iteration 2 - Current Err =  0.00411412517264065 BF Threshold =  0.001 Converged =  FALSE"
#> [1] "BACKFITTING Iteration 3 - Current Err =  0.00340417064742161 BF Threshold =  0.001 Converged =  FALSE"
#> [1] "BACKFITTING Iteration 4 - Current Err =  0.00240860428484452 BF Threshold =  0.001 Converged =  FALSE"
#> [1] "BACKFITTING Iteration 5 - Current Err =  0.00259785245231315 BF Threshold =  0.001 Converged =  FALSE"
#> [1] "BACKFITTING Iteration 6 - Current Err =  0.00219516502643102 BF Threshold =  0.001 Converged =  FALSE"
#> [1] "BACKFITTING Iteration 7 - Current Err =  0.00102537364592285 BF Threshold =  0.001 Converged =  FALSE"
#> [1] "BACKFITTING Iteration 8 - Current Err =  0.000572346003114787 BF Threshold =  0.001 Converged =  TRUE"
#> [1] "Computing CI/PI using uncertainty_method =  epistemic  at alpha =  0.05"
## --- Autoplot (epistemic-only) ---
# Per-term effect with CI band
autoplot(ngam, which = "terms", term = "x1", interval = "confidence")  +
  ggplot2::xlab("x1") + ggplot2::ylab("Partial effect")


# Request a different number of forward passes or CI level:
autoplot(ngam, which = "terms", term = "x1", interval = "confidence",
forward_passes = 15, level = 0.7)

# Response panel
autoplot(ngam, which = "response")


# Link panel with custom title
autoplot(ngam, which = "link")  +
  ggplot2::ggtitle("Main Title")


# }