Internal coefficient table (deprecated, use brs_est() or summary())

Deprecated convenience wrapper. Use brs_est for coefficient estimates or summary.brs for a full model summary.

Usage

brs_coef(fit, alpha = 0.05)

Arguments

fit: A fitted "brs" object.
alpha: Significance level.

Value

A list with components est (from brs_est) and gof (from brs_gof).

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.

Ferrari, S. L. P., and Cribari-Neto, F. (2004). Beta regression for modelling rates and proportions. Journal of Applied Statistics, 31(7), 799–815. doi:10.1080/0266476042000214501

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)
)
prep <- brs_prep(dat, ncuts = 100)
#> brs_prep: n = 20 | exact = 0, left = 1, right = 1, interval = 18
fit <- brs(y ~ x1, data = prep)
brs_coef(fit)
#> $est
#>      variable   estimate        se    z_value   p_value   ci_lower  ci_upper
#> 1 (Intercept)  0.2550945 0.8643907  0.2951148 0.7679062 -1.4390802 1.9492691
#> 2          x1 -0.2202075 0.5411943 -0.4068917 0.6840875 -1.2809288 0.8405139
#> 3       (phi) -0.3929198 0.2762612 -1.4222765 0.1549460 -0.9343818 0.1485422
#> 
#> $gof
#>      logLik      AIC      BIC   pseudo_r2
#> 1 -92.65206 191.3041 194.2913 0.002947767
#> 
# }