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Fit a variable-dispersion beta interval regression model

Source: R/fit.R
brs_fit_var.Rd

Estimates the parameters of a beta regression model with observation-specific dispersion governed by a second linear predictor. Both submodels are estimated jointly via maximum likelihood, using the complete likelihood with mixed censoring (Lopes, 2024, Eq. 2.24).

Usage

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

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").

hessian_method

Character: "numDeriv" or "optim".

ncuts

Number of scale categories (default 100).

lim

Uncertainty half-width (default 0.5).

repar

Reparameterization scheme (default 2).

method

Optimization method (default "BFGS").

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),
  z1 = runif(n)
)
sim <- brs_sim(
  formula = ~ x1 + x2 | z1,
  data = dat,
  beta = c(0.2, -0.5, 0.3),
  zeta = c(1, 1.2)
)
fit <- brs_fit_var(
  formula = y ~ x1 + x2 | z1, data = sim,
  link = "logit", link_phi = "logit"
)
print(fit)
#> 
#> Call:
#> brs_fit_var(formula = y ~ x1 + x2 | z1, data = sim, link = "logit", 
#>     link_phi = "logit")
#> 
#> Coefficients (mean model with logit link):
#> (Intercept)          x1          x2 
#>      0.0959     -0.3912      0.2965 
#> 
#> Phi coefficients (precision model with logit link):
#> (phi)_(Intercept)          (phi)_z1 
#>            1.4876            0.0040 
#>