K-fold cross-validation for brs models — brs

Performs repeated k-fold cross-validation for brs models.

Usage

brs_cv(formula, data, k = 5L, repeats = 1L, ...)

Arguments

formula: Model formula passed to brs.
data: Data frame.
k: Number of folds.
repeats: Number of repeated k-fold runs.
...: Additional arguments forwarded to brs (e.g., repar, link, method).

Value

A data frame with one row per fold and columns: repeat, fold, n_train, n_test, log_score, rmse_yt, mae_yt, converged, and error. The object has class "brs_cv".

Details

The log_score is the mean log predictive contribution under the complete likelihood contribution implied by each observation's censoring type (delta).

References

Lopes, J. E. (2023). Modelos de regressao beta para dados de escala. Master's dissertation, Universidade Federal do Parana, Curitiba. URI: https://hdl.handle.net/1884/86624.

Hawker, G. A., Mian, S., Kendzerska, T., and French, M. (2011). Measures of adult pain: Visual Analog Scale for Pain (VAS Pain), Numeric Rating Scale for Pain (NRS Pain), McGill Pain Questionnaire (MPQ), Short-Form McGill Pain Questionnaire (SF-MPQ), Chronic Pain Grade Scale (CPGS), Short Form-36 Bodily Pain Scale (SF-36 BPS), and Measure of Intermittent and Constant Osteoarthritis Pain (ICOAP). Arthritis Care and Research, 63(S11), S240-S252. doi:10.1002/acr.20543

Hjermstad, M. J., Fayers, P. M., Haugen, D. F., et al. (2011). Studies comparing Numerical Rating Scales, Verbal Rating Scales, and Visual Analogue Scales for assessment of pain intensity in adults: a systematic literature review. Journal of Pain and Symptom Management, 41(6), 1073-1093. doi:10.1016/j.jpainsymman.2010.08.016

Examples

# \donttest{
dat <- data.frame(
  y = c(
    0, 5, 20, 50, 75, 90, 100, 30, 60, 45,
    10, 40, 55, 70, 85, 25, 35, 65, 80, 15
  ),
  x1 = rep(c(1, 2), 10),
  x2 = rep(c(0, 0, 1, 1), 5)
)
prep <- brs_prep(dat, ncuts = 100)
#> brs_prep: n = 20 | exact = 0, left = 1, right = 1, interval = 18
cv <- brs_cv(y ~ x1, data = prep, k = 3, repeats = 1)
cv
#>   repeat fold n_train n_test log_score rmse_yt mae_yt converged
#> 1      1    1      13      7        NA      NA     NA     FALSE
#> 2      1    2      13      7        NA      NA     NA     FALSE
#> 3      1    3      14      6        NA      NA     NA     FALSE
#>                                   error
#> 1 missing value where TRUE/FALSE needed
#> 2 missing value where TRUE/FALSE needed
#> 3 missing value where TRUE/FALSE needed
# }