rinet (version 0.1.0)

predict_rinet_2d: 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
)

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

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.

Examples

if (FALSE) {
  # 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)
}