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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

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)
)

# Fixed dispersion
fit1 <- brs(y ~ x1 + x2, data = sim)
print(fit1)
#> 
#> Call:
#> brs(formula = y ~ x1 + x2, data = sim)
#> 
#> Coefficients (mean model with logit link):
#> (Intercept)          x1          x2 
#>      0.0959     -0.3912      0.2964 
#> 
#> Phi coefficients (precision model with logit link):
#>  (phi) 
#> 1.4895 
#> 

# Variable dispersion
fit2 <- brs(y ~ x1 + x2 | z1, data = sim)
print(fit2)
#> 
#> Call:
#> brs(formula = y ~ x1 + x2 | z1, data = sim)
#> 
#> 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 
#>