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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 (Lopes, 2024, Eq. 2.24).

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

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

set.seed(42)
n <- 100
dat <- data.frame(x1 = rnorm(n), x2 = rnorm(n))
sim <- brs_sim(
  formula = ~ x1 + x2, data = dat,
  beta = c(0.2, -0.5, 0.3), phi = 1 / 5
)
fit <- brs_fit_fixed(
  formula = y ~ x1 + x2, data = sim,
  link = "logit", link_phi = "logit"
)
print(fit)
#> 
#> Call:
#> brs_fit_fixed(formula = y ~ x1 + x2, data = sim, link = "logit", 
#>     link_phi = "logit")
#> 
#> Coefficients (mean model with logit link):
#> (Intercept)          x1          x2 
#>      0.0969     -0.5117      0.1147 
#> 
#> Phi coefficients (precision model with logit link):
#>  (phi) 
#> 0.1612 
#>