Model comparison by analysis of deviance (LR test) for `brs`

Usage

# S3 method for class 'brs'
anova(object, ..., test = c("Chisq", "none"))

Arguments

object: A fitted "brs" model.
...: Additional fitted "brs" and/or "brsmm" models to compare.
test: Character; "Chisq" (default) or "none".

Value

An object of class "anova" and "data.frame" with model-wise log-likelihood, information criteria, and (optionally) LR test columns.

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

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
m1 <- brs(y ~ 1, data = prep)
#> Warning: the standard deviation is zero
m2 <- brs(y ~ x1, data = prep)
m3 <- brs(y ~ x1 + x2, data = prep)
anova(m1, m2, m3)
#>          Df  logLik    AIC    BIC  Chisq Chi Df Pr(>Chisq)
#> M1 (brs)  2 -92.735 189.47 191.46                         
#> M2 (brs)  3 -92.652 191.30 194.29 0.1650      1     0.6846
#> M3 (brs)  4 -92.393 192.79 196.77 0.5175      1     0.4719
# }