Predict Method for AddiVortesFit

Predicts outcomes for new data using a fitted AddiVortesFit model object. It can return mean predictions, quantiles and optionally calculate the Root Mean Squared Error (RMSE) if true outcomes are provided.

Usage

# S3 method for class 'AddiVortesFit'
predict(
  object,
  newdata,
  type = c("response", "quantile"),
  quantiles = c(0.025, 0.975),
  interval = c("credible", "prediction"),
  showProgress = TRUE,
  parallel = TRUE,
  cores = NULL,
  ...
)

Arguments

object

An object of class AddiVortesFit, typically the result of a call to AddiVortes().

newdata

A matrix of covariates for the new test set. The number of columns must match the original training data.

type

The type of prediction required. The default "response" gives the mean prediction. The alternative "quantile" returns the quantiles specified by the quantiles argument.

quantiles

A numeric vector of probabilities to compute for the predictions when type = "quantile".

interval

The type of interval calculation. The default "credible" accounts only for uncertainty in the mean (similar to lm's confidence interval). The alternative "prediction" also includes the model's error variance, producing wider intervals (similar to lm's prediction interval).

showProgress

Logical; if TRUE (default), a progress bar is shown during prediction.

parallel

Logical; if TRUE (default), predictions are computed in parallel.

cores

The number of CPU cores to use for parallel processing. If NULL (default), it defaults to one less than the total number of available cores.

...

Further arguments passed to or from other methods (currently unused).

Value

If type = "response", a numeric vector of mean predictions. If type = "quantile", a matrix where each row corresponds to an observation in newdata and each column to a quantile.

Details

This function relies on the internal helper function applyScaling_internal being available in the environment, which is used by the main AddiVortes function.

When interval = "prediction" and type = "quantile", the function samples additional Gaussian noise with variance equal to the sampled sigma squared from the posterior. This accounts for the inherent variability in individual predictions, not just uncertainty in the mean function. The noise is added in the scaled space before unscaling predictions.

Examples

# \donttest{
# Fit a model
set.seed(123)
X <- matrix(rnorm(100), 20, 5)
Y <- rnorm(20)
fit <- AddiVortes(Y, X, m = 5, totalMCMCIter = 50, mcmcBurnIn = 10)

# New data for prediction
X_new <- matrix(rnorm(25), 5, 5)

# Mean predictions
pred_mean <- predict(fit, X_new, type = "response")

# Credible intervals (uncertainty in mean only)
pred_conf <- predict(fit, X_new, type = "quantile", 
                    interval = "credible",
                    quantiles = c(0.025, 0.975))

# Prediction intervals (includes error variance)
pred_pred <- predict(fit, X_new, type = "quantile",
                    interval = "prediction",
                    quantiles = c(0.025, 0.975))

# Prediction intervals are wider than credible intervals
mean(pred_pred[, 2] - pred_pred[, 1]) > mean(pred_conf[, 2] - pred_conf[, 1])
#> [1] TRUE
# }