Calculate bootstrapped confidence intervals

assesspy.boot_ci(fun, nboot=100, alpha=0.05, **kwargs)

Calculate the non-parametric bootstrap confidence interval for a given numeric input and a chosen function.

Parameters:

• fun (function) – Function to bootstrap. Must return a single value.

• nboot (int) – Default 100. Number of iterations to use to estimate the output statistic confidence interval.

• alpha (float) – Default 0.05. Numeric value indicating the confidence interval to return. 0.05 will return the 95% confidence interval.

• kwargs (numeric) – Arguments passed on to fun.

Note

Input function should require 1 argument or be assesspy.prd().

Returns:

A two-long list of floats containing the bootstrapped confidence interval of the input vector(s).

Return type:

list[float]

Example:

# Calculate PRD confidence interval:
import assesspy as ap

ap.boot_ci(
    ap.prd,
    assessed = ap.ratios_sample().assessed,
    sale_price = ap.ratios_sample().sale_price,
    nboot = 100
    )