Autoplot method for neuralGAM objects (epistemic-only)

Source: R/autoplot.neuralGAM.R

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 =  0.0226027578943881 BF Threshold =  0.001 Converged =  FALSE"
#> [1] "BACKFITTING Iteration 2 - Current Err =  0.000837886936836273 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")


# }