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Fit a beta interval regression model

Source: R/fit.R
brs.Rd

Unified interface that dispatches to brs_fit_fixed (fixed dispersion) or brs_fit_var (variable dispersion) based on the formula structure.

Usage

brs(
  formula,
  data,
  link = "logit",
  link_phi = "logit",
  ncuts = 100L,
  lim = 0.5,
  repar = 2L,
  method = c("BFGS", "L-BFGS-B"),
  hessian_method = c("numDeriv", "optim")
)

Arguments

formula

A Formula-style formula with two parts: y ~ x1 + x2 | z1 + z2.

data

Data frame.

Mean link function (default "logit").

Dispersion link function (default "logit").

ncuts

Number of scale categories (default 100).

lim

Uncertainty half-width (default 0.5).

repar

Reparameterization scheme (default 2).

method

Optimization method (default "BFGS").

hessian_method

Character: "numDeriv" or "optim".

Value

An object of class "brs".

Details

If the formula contains a | separator (e.g., y ~ x1 + x2 | z1), the variable-dispersion model is fitted; otherwise, a fixed-dispersion model is used.

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
# Fixed dispersion
fit1 <- brs(y ~ x1, data = prep)
print(fit1)
#> 
#> Call:
#> brs(formula = y ~ x1, data = prep)
#> 
#> Coefficients (mean model with logit link):
#> (Intercept)          x1 
#>      0.2551     -0.2202 
#> 
#> Phi coefficients (precision model with logit link):
#>   (phi) 
#> -0.3929 
#> 
# Variable dispersion
fit2 <- brs(y ~ x1 | x2, data = prep)
print(fit2)
#> 
#> Call:
#> brs(formula = y ~ x1 | x2, data = prep)
#> 
#> Coefficients (mean model with logit link):
#> (Intercept)          x1 
#>      0.2732     -0.2310 
#> 
#> Phi coefficients (precision model with logit link):
#> (Intercept)          x2 
#>     -0.3789     -0.0288 
#> 
# }