predict_rinet_2d: 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 2D mixture distributions using RINet

Description

Takes one or more 2D samples and predicts the underlying reference population statistics (means, stds, correlation, reference fraction) from a mixture of healthy and pathological measurements.

Usage

predict_rinet_2d(
  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 matrix or list of matrices. Each sample should be a matrix with 2 columns representing observations from a 2D 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 means (vector of length 2, log-scale if log_scale=TRUE)
std	Predicted standard deviations (vector of length 2, log-scale if log_scale=TRUE)
covariance	Predicted covariance matrix (2x2 matrix)
correlation	Predicted correlation coefficient (scalar)
reference_fraction	Predicted reference component fraction
reference_interval	Reference region ellipse vertices (100x2 matrix) 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 (2x2 matrix), std_ci (2x2 matrix), correlation_ci, reference_fraction_ci

Examples

## Not run: 
  # Single 2D sample (using positive data for log-scale)
  sample1 <- exp(matrix(rnorm(2000, mean = 2, sd = 0.3), ncol = 2))
  result <- predict_rinet_2d(sample1)
  print(result[[1]]$mean)
  print(result[[1]]$covariance)

  # Multiple samples
  samples <- list(exp(matrix(rnorm(2000, mean = 2, sd = 0.3), ncol = 2)),
                  exp(matrix(rnorm(2000, mean = 2, sd = 0.3), ncol = 2)))
  results <- predict_rinet_2d(samples)

## End(Not run)

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