predict_rinet_1d: 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 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
)

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.

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

Examples

## Not run: 
  # 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)

## End(Not run)

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