rinet (version 0.1.0)

predict_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
)

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)

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.

Examples

if (FALSE) {
  # 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)
}