rinet (version 0.1.0)

predict_rinet_1d: Predict statistics of the underlying reference distribution from 1D mixture distributions using RINet

Description

Takes one or more 1D samples and predicts the underlying reference population statistics (mean, std, reference fraction) from a mixture of healthy and pathological measurements.

Usage

predict_rinet_1d(
  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 (scalar, log-scale if log_scale=TRUE)

std

Predicted standard deviation (scalar, log-scale if log_scale=TRUE)

covariance

Covariance matrix (1x1 matrix)

correlation

Always 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): mean_ci, std_ci, reference_fraction_ci, reference_interval_lower_ci, reference_interval_upper_ci

Arguments

data

A numeric vector, matrix, or list of vectors. Each sample should contain observations from a 1D mixture distribution.

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.

confidence_level

Numeric specifying the confidence level for bootstrap intervals. Default is 0.95.

Examples

if (FALSE) {
  # Single sample (using positive data for log-scale)
  sample1 <- exp(rnorm(1000, mean = 2, sd = 0.3))
  result <- predict_rinet_1d(sample1)
  print(result[[1]]$mean)

  # Multiple samples
  samples <- list(exp(rnorm(1000, 2, 0.3)), exp(rnorm(1000, 1.5, 0.4)))
  results <- predict_rinet_1d(samples)
}