Streaming Breusch\–Pagan test for large datasets

Computes the classical Breusch\–Pagan statistic using chunked cross-products so that large datasets can be evaluated without materialising the full auxiliary regression in memory. The streamed result is algebraically identical to performBPTest.

Usage

performBPTestStreaming(model, data, chunk_size = 10000, progress = interactive())

Arguments

model

A fitted stats::lm object describing the mean structure whose residual variance is to be assessed.

data

A base::data.frame containing the variables referenced in model. The data must include all observations used to fit the model.

chunk_size

Positive integer giving the number of observations processed per streaming chunk. Smaller values reduce peak memory usage at the expense of additional iteration overhead.

progress

Logical flag controlling whether a textual progress bar is displayed while chunks are processed. Defaults to interactive().

Value

A htest object mirroring performBPTest and reporting the chi-squared statistic, degrees of freedom, p-value, and metadata describing the chunked computation.

Details

Each chunk contributes to the cross-product matrices \(X'X\) and \(X'y\) for the auxiliary regression of squared residuals on the original regressors. The chunks are aggregated to recover the exact Breusch\–Pagan statistic while keeping memory usage bounded. When Matrix is installed, sparse cross-products are used automatically for large chunks.

See also

performBPTest for the standard implementation.

Examples

data(mtcars)
mod <- lm(mpg ~ wt + qsec, data = mtcars)
performBPTestStreaming(mod, mtcars, chunk_size = 16, progress = FALSE)
#> 
#> 	Breusch-Pagan test for heteroscedasticity (streaming)
#> 
#> data:  mpg ~ wt + qsec
#> X-squared = 3.1348, df = 2, p-value = 0.2086
#>