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ptestR: Permutation-Based Significance Testing for Regression Models

Source: R/ptestR-package.R
ptestR-package.Rd

ptestR wraps stats::glm(), lme4::lmer(), and binomial stats::glm() with a permutation loop to produce nonparametric p-values. For each model, a null distribution of the test statistic is built by randomly rearranging the outcome variable (permNum times); p.perm is the proportion of permuted statistics at least as extreme as the observed one.

This approach requires far fewer distributional assumptions than standard Wald or likelihood-ratio tests, making it well-suited to neuroimaging, EEG, and other biomedical datasets with repeated measures and small samples.

Main functions

FunctionModel classTest statistic
grouped_perm_glm()Generalised linear model (glm)t
grouped_perm_glmm()Linear mixed-effects model (lmer)t
grouped_perm_binoglm()Binomial logistic regression (glm)z

All three share the same call signature and return a tidy tibble.

Typical grouped usage

df |>
  dplyr::group_by(State) |>
  tidyr::nest() |>
  dplyr::mutate(
    res = purrr::map(data, ~ grouped_perm_glmm(
      .x,
      formla      = feature ~ PMA + Sex + (1 | subject),
      var_to_perm = "feature",
      permNum     = 10000
    ))
  ) |>
  tidyr::unnest(res) |>
  dplyr::filter(term == "PMA")

References

França LGS, et al. (2024). Neonatal brain dynamic functional connectivity in term and preterm infants and its association with early childhood neurodevelopment. Nature Communications, 15, 16. https://doi.org/10.1038/s41467-023-44050-z

See also

Useful links:

Author

Maintainer: Lucas G. S. França lucas.franca@kcl.ac.uk (ORCID)

Authors: