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Bootstrap RRi Model Parameter Estimates

Source: R/boot_RRi_parameters.R
boot_RRi_parameters.Rd

This function performs a bootstrap procedure on a fitted RRi model (as produced by estimate_RRi_curve()) to assess the uncertainty of the parameter estimates. It resamples the original data (using either a specified number of samples or a proportion of the available data) and re-estimates the dual-logistic model parameters for each bootstrap sample. The approach leverages the speed and efficiency of the data.table package.

Usage

boot_RRi_parameters(
  fit = NULL,
  n_samples = nrow(fit$data),
  prop_of_samples = NULL,
  nboot = 100
)

Arguments

fit

An object of class "RRi_fit" produced by estimate_RRi_curve(). This fitted object must contain a data component with the original time and RRi values as well as the fitted values.

n_samples

A numeric value specifying the number of data points to sample for each bootstrap replicate. The default is nrow(fit$data) (all data points). Must not exceed the total number of rows in the data.

prop_of_samples

A numeric value (between 0 and 1) specifying the proportion of data to use in each bootstrap sample. If specified, it overrides n_samples by computing the number as floor(nrow(fit$data) * prop_of_samples).

nboot

A numeric value indicating the number of bootstrap replicates to perform. The default is 100.

Value

An object of class "boot_RRi_fit", which is a data.table with one row per bootstrap replicate. Each row contains the estimated parameters from that bootstrap sample.

Details

The bootstrap procedure returns a data.table with a row for each bootstrap replicate, containing the estimated parameters. This enables users to construct confidence intervals and assess the variability of the fitted model parameters.

The function first checks that the input fit object is not NULL and that n_samples and nboot are numeric and valid. If prop_of_samples is provided, it is used to compute the number of samples per replicate. The data from the fit object is converted to a data.table for efficient subsetting.

Bootstrap indices are generated by sampling with replacement, and for each bootstrap replicate, the function re-estimates the model parameters using estimate_RRi_curve(). The output is a data.table where each row corresponds to a bootstrap replicate.

Examples

# \donttest{
library(CardioCurveR)
library(data.table)

# Simulate an example RRi signal:
set.seed(123)
t <- seq(0, 20, by = 0.01)
true_params <- c(alpha = 800, beta = -350, c = 0.80,
                 lambda = -3, phi = -2, tau = 6, delta = 3)
RRi_true <- dual_logistic(t, true_params)
RRi_sim <- RRi_true + rnorm(n = length(t), sd = 30)

# Estimate the model parameters:
fit <- estimate_RRi_curve(time = t, RRi = RRi_sim)

# Bootstrap the parameter estimates using 50% of the data per replicate and 100 replicates
boot_fit <- boot_RRi_parameters(fit = fit, prop_of_samples = 0.5, nboot = 100)

# View the bootstrap estimates
print(boot_fit)
#> Bootstrap RRi Parameter Estimates
#> Number of bootstrap replicates: 100 
#> Preview of estimated parameters (first 6 replicates):
#>   nboot    alpha      beta         c    lambda       phi      tau    delta
#> 1     1 800.5251 -353.5449 0.8870946 -3.062566 -1.622490 5.968664 3.120885
#> 2     2 800.4201 -339.0531 0.7224601 -3.243566 -2.183843 5.952733 2.974459
#> 3     3 800.4500 -346.2715 0.7425458 -3.314643 -2.128720 5.971596 2.973739
#> 4     4 799.3835 -337.8819 0.6500205 -3.020092 -2.984357 5.951567 2.904101
#> 5     5 801.1221 -347.4734 0.7006175 -3.203447 -2.364417 5.969239 2.886365
#> 6     6 803.9053 -346.8873 0.7399056 -3.253758 -2.202961 5.961412 2.990922
# }