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Fit a fixed-dispersion beta interval regression model

Source: R/fit.R
brs_fit_fixed.Rd

Estimates the parameters of a beta regression model with a single (scalar) dispersion parameter using maximum likelihood. The log-likelihood and its gradient are evaluated by the compiled C++ backend supporting the complete likelihood with mixed censoring types.

Usage

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

Arguments

formula

Two-sided formula y ~ x1 + x2 + ....

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).

hessian_method

Character: "numDeriv" (default) or "optim". With "numDeriv" the Hessian is computed after convergence using hessian, which is typically more accurate than the built-in optim Hessian.

repar

Reparameterization scheme (default 2).

method

Optimization method: "BFGS" (default) or "L-BFGS-B".

Value

An object of class "brs".

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
fit <- brs_fit_fixed(y ~ x1 + x2, data = prep)
print(fit)
#> 
#> Call:
#> brs_fit_fixed(formula = y ~ x1 + x2, data = prep)
#> 
#> Coefficients (mean model with logit link):
#> (Intercept)          x1          x2 
#>      0.0664     -0.2268      0.3884 
#> 
#> Phi coefficients (precision model with logit link):
#>   (phi) 
#> -0.4091 
#> 
# }