K-fold cross-validation for brs models

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

Usage

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

Arguments

formula

Model formula passed to brs.

data

Data frame.

k

Number of folds.

repeats

Number of repeated k-fold runs.

seed

Optional random seed for reproducibility.

...

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

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{
set.seed(99)
d <- data.frame(x1 = rnorm(150), x2 = rnorm(150))
sim <- brs_sim(
  formula = ~ x1 + x2, data = d,
  beta = c(0.2, -0.5, 0.3), phi = 0.2, ncuts = 100, repar = 2
)

cv <- brs_cv(y ~ x1 + x2, data = sim, k = 3, repeats = 1, repar = 2)
cv
#>   repeat fold n_train n_test log_score   rmse_yt    mae_yt converged error
#> 1      1    1     100     50 -4.102422 0.3993795 0.3618229      TRUE  <NA>
#> 2      1    2     100     50 -4.436561 0.3532272 0.3135323      TRUE  <NA>
#> 3      1    3     100     50 -4.011085 0.3558378 0.3308460      TRUE  <NA>
# }