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Split Conformal Prediction Intervals

Source: R/conformal_split.R
conformal_split.Rd

Constructs prediction intervals using split conformal inference. The data is split into training and calibration sets; nonconformity scores are computed on the calibration set and used to form intervals on new data.

Usage

conformal_split(
  x,
  y,
  model,
  x_new,
  alpha = 0.1,
  cal_fraction = 0.5,
  score_type = c("absolute", "normalized"),
  scale_model = NULL,
  seed = NULL
)

Arguments

x

A numeric matrix or data frame of predictor variables.

y

A numeric vector of response values.

model

A fitted model object (e.g., from lm()), a make_model() specification, or a formula (which will fit a linear model).

x_new

A numeric matrix or data frame of new predictor variables for which to compute prediction intervals.

alpha

Miscoverage level. Default 0.10 gives 90 percent prediction intervals.

cal_fraction

Fraction of data used for calibration. Default 0.5.

score_type

Type of nonconformity score. "absolute" (default) uses absolute residuals and produces constant-width intervals. "normalized" divides residuals by a local scale estimate from scale_model, producing locally-adaptive interval widths.

scale_model

A make_model() specification for predicting absolute residuals (used only when score_type = "normalized"). Must return positive predictions. If NULL and score_type = "normalized", a default model is fitted using lm() on absolute residuals.

seed

Optional random seed for reproducible data splitting.

Value

A predictset_reg object (a list) with components:

pred

Numeric vector of point predictions for x_new.

lower

Numeric vector of lower bounds.

upper

Numeric vector of upper bounds.

alpha

The miscoverage level used.

method

Character string "split".

scores

Numeric vector of calibration nonconformity scores.

quantile

The conformal quantile used to form intervals.

n_cal

Number of calibration observations.

n_train

Number of training observations.

fitted_model

The fitted model object.

model

The predictset_model specification.

References

Lei, J., G'Sell, M., Rinaldo, A., Tibshirani, R.J. and Wasserman, L. (2018). Distribution-free predictive inference for regression. Journal of the American Statistical Association, 113(523), 1094-1111. doi:10.1080/01621459.2017.1307116

See also

Other regression methods: conformal_aci(), conformal_cqr(), conformal_cv(), conformal_jackknife(), conformal_mondrian(), conformal_weighted()

Examples

set.seed(42)
n <- 200
x <- matrix(rnorm(n * 3), ncol = 3)
y <- x[, 1] * 2 + rnorm(n)
x_new <- matrix(rnorm(50 * 3), ncol = 3)

result <- conformal_split(x, y, model = y ~ ., x_new = x_new)
print(result)
#> 
#> ── Conformal Prediction Intervals (Split Conformal) ────────────────────────────
#> • Coverage target: "90%"
#> • Training: 100 | Calibration: 100 | Predictions: 50
#> • Conformal quantile: 1.4914
#> • Median interval width: 2.9828