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.
Arguments
- object
An object of class
AddiVortesFit, typically the result of a call toAddiVortes().- 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 thequantilesargument.- quantiles
A numeric vector of probabilities with values in 0, 1 to compute for the predictions when
type = "quantile".- interval
The type of interval calculation. The default
"confidence"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)
#> Fitting AddiVortes model to input data...
#> Input dimensions: 20 observations, 5 covariates
#> Model configuration: 5 tessellations, 50 total iterations (10 burn-in)
#>
#> Phase 1: Burn-in sampling (10 iterations)
#>
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#>
#> Phase 2: Posterior sampling (40 iterations)
#>
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#>
#> MCMC sampling completed.
#>
# New data for prediction
X_new <- matrix(rnorm(25), 5, 5)
# Mean predictions
pred_mean <- predict(fit, X_new, type = "response")
#> Generating predictions for 5 observations using 40 posterior samples...
#>
#> Prediction generation completed.
#>
# Confidence intervals (uncertainty in mean only)
pred_conf <- predict(fit, X_new, type = "quantile",
interval = "confidence",
quantiles = c(0.025, 0.975))
#> Error in match.arg(interval): 'arg' should be one of “credible”, “prediction”
# Prediction intervals (includes error variance)
pred_pred <- predict(fit, X_new, type = "quantile",
interval = "prediction",
quantiles = c(0.025, 0.975))
#> Generating predictions for 5 observations using 40 posterior samples...
#>
#> Prediction generation completed.
#>
# Prediction intervals are wider than confidence intervals
mean(pred_pred[, 2] - pred_pred[, 1]) > mean(pred_conf[, 2] - pred_conf[, 1])
#> Error: object 'pred_conf' not found
# }