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Compare fitted brs models in a single table

Source: R/table.R
brs_table.Rd

Builds a comparison table for one or more fitted "brs" objects, summarizing fit statistics and (optionally) censoring composition.

Usage

brs_table(
  ...,
  models = NULL,
  include_censoring = TRUE,
  sort_by = c("none", "AIC", "BIC", "logLik"),
  decreasing = FALSE,
  digits = 4L
)

Arguments

...

Fitted "brs" objects passed individually.

models

Optional list of fitted "brs" objects. Use either ... or models, not both.

include_censoring

Logical; include censoring counts/proportions. Default is TRUE.

sort_by

Character; optional sort criterion: "none" (default), "AIC", "BIC", or "logLik".

decreasing

Logical; sort direction when sort_by != "none".

digits

Integer number of digits used for numeric rounding.

Value

A data frame with one row per model.

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
m1 <- brs(y ~ 1, data = prep)
#> Warning: the standard deviation is zero
m2 <- brs(y ~ x1, data = prep)
brs_table(null = m1, x1 = m2, sort_by = "AIC")
#>   model nobs npar   logLik      AIC      BIC pseudo_r2 exact left right
#> 1  null   20    2 -92.7346 189.4692 191.4606        NA     0    1     1
#> 2    x1   20    3 -92.6521 191.3041 194.2913    0.0029     0    1     1
#>   interval prop_exact prop_left prop_right prop_interval
#> 1       18          0      0.05       0.05           0.9
#> 2       18          0      0.05       0.05           0.9
# }