predict_rinet: Predict statistics of the underlying reference distribution...

In rinet: Clinical Reference Interval Estimation with Reference Interval Network (RINet)

Predict statistics of the underlying reference distribution from mixture distributions using RINet

Description

Automatically detects whether input data is 1D or 2D and calls the appropriate prediction function. This is the main user-facing function. It estimates the statistics of a "healthy" reference population from a mixture of healthy and pathological measurements.

Usage

predict_rinet(
  data,
  feature_grid_range = c(-4, 4),
  feature_grid_nbins = 100,
  verbose = 0,
  log_scale = TRUE,
  percentiles = c(0.025, 0.975),
  n_bootstrap = 0,
  confidence_level = 0.95
)

Arguments

data	A numeric vector, matrix, or list. For 1D: vector or matrix with 1 column. For 2D: matrix with 2 columns. Can also be a list of such objects.
feature_grid_range	Numeric vector of length 2 specifying the range for histogram binning. Default is c(-4, 4).
feature_grid_nbins	Integer specifying the number of histogram bins. Default is 100.
verbose	Integer controlling verbosity (0 = silent). Default is 0.
log_scale	Logical indicating whether to log-transform the data before prediction. If TRUE (default), returns log-scale statistics and calculates reference intervals in the original scale. Default is TRUE.
percentiles	Numeric vector of length 2 specifying the lower and upper percentiles for the reference interval. Default is c(0.025, 0.975).
n_bootstrap	Integer specifying the number of bootstrap resamples for confidence intervals. Default is 0 (no bootstrap). When > 0, confidence intervals are computed for all predicted statistics using batch inference.
confidence_level	Numeric specifying the confidence level for bootstrap intervals. Default is 0.95.

Value

A list of predictions. Each element contains:

mean	Predicted mean(s) (log-scale if log_scale=TRUE)
std	Predicted standard deviation(s) (log-scale if log_scale=TRUE)
covariance	Predicted covariance matrix
correlation	Predicted correlation (NA for 1D)
reference_fraction	Predicted reference component fraction
reference_interval	Reference interval in original scale (if log_scale=TRUE)
log_scale	Logical indicating whether log-scaling was used
bootstrap_ci	List of bootstrap confidence intervals (if n_bootstrap > 0)

Examples

## Not run: 
  # 1D sample (using positive data for log-scale)
  sample_1d <- exp(rnorm(1000, mean = 2, sd = 0.5))
  result <- predict_rinet(sample_1d)

  # 2D sample (using positive data for log-scale)
  sample_2d <- exp(matrix(rnorm(2000, mean = 2, sd = 0.5), ncol = 2))
  result <- predict_rinet(sample_2d)

  # Multiple samples (automatically detected)
  samples <- list(exp(rnorm(1000, mean = 2, sd = 0.5)),
                  exp(rnorm(1000, mean = 2, sd = 0.5)))
  results <- predict_rinet(samples)

## End(Not run)

rinet documentation built on Feb. 23, 2026, 5:07 p.m.