Calculate confidence intervals

assesspy.boot_ci(fun, estimate: list[int] | list[float] | Series, sale_price: list[int] | list[float] | Series, nboot: int = 1000, alpha: float = 0.05) → tuple[float, float]

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

Parameters:

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

• estimate (Array-like numeric values) – A list or pd.Series of estimated values. Must be the same length as sale_price.

• sale_price (Array-like numeric values) – A list or pd.Series of sale prices. Must be the same length as estimate.

• nboot (int) – Default 1000. Number of iterations to use to estimate the output statistic confidence interval.

• alpha (float) – Default 0.05. Float value indicating the significance level of the returned confidence interval. 0.05 will return the 95% confidence interval.

Returns:

A tuple of floats containing the bootstrapped confidence interval of the input values.

Return type:

tuple[float, float]

Example:

# Calculate PRD confidence interval:
import assesspy as ap

ap.boot_ci(
    ap.prd,
    estimate = ap.ccao_sample().estimate,
    sale_price = ap.ccao_sample().sale_price,
    nboot = 1000
)

assesspy.cod_ci(estimate: list[int] | list[float] | Series, sale_price: list[int] | list[float] | Series, nboot: int = 1000, alpha: float = 0.05) → tuple[float, float]

Calculate the non-parametric bootstrap confidence interval for COD.

See also:

boot_ci()

assesspy.prd_ci(estimate: list[int] | list[float] | Series, sale_price: list[int] | list[float] | Series, nboot: int = 1000, alpha: float = 0.05) → tuple[float, float]

Calculate the non-parametric bootstrap confidence interval for PRD.

See also:

boot_ci()

assesspy.prb_ci(estimate: list[int] | list[float] | Series, sale_price: list[int] | list[float] | Series, nboot: int = 1000, alpha: float = 0.05) → tuple[float, float]

Calculate the closed-form confidence interval for PRB. Unlike COD and PRB, this does not use bootstrapping.

See also:

boot_ci()