residuals.robustbetareg function

Residuals Method for robustbetareg Objects

The function provides several types of residuals for the robust beta regression models: Pearson residuals (raw residuals scaled by square root of variance function) and different kinds of weighted residuals proposed by Espinheira et al. (2008) and Espinheira et al. (2017).

## S3 method for class 'robustbetareg'
residuals(
  object,
  type = c("sweighted2", "pearson", "weighted", "sweighted", "sweighted.gamma",
    "sweighted2.gamma", "combined", "combined.projection"),
  ...
)

Arguments

• object: fitted model object of class robustbetareg.
• type: character indicating type of residuals to be used.
• ...: currently not used.

Returns

residuals returns a vector with the residuals of the type specified in the type argument.

Details

The definitions of the first four residuals are provided in Espinheira et al. (2008): equation (2) for "pearson", equation (6) for "weighted", equation (7) for "sweighted", and equation (8) for "sweighted2". For the last four residuals the definitions are described in Espinheira et al. (2017): equations (7) and (10) for the "sweighted.gamma" and "sweighted2.gamma", respectively, equation (9) for "combined", and equation (11) for "combined.projection".

Examples

get(data("HIC", package = "robustbetareg"))
fit.hic <- robustbetareg(HIC ~ URB + GDP | 1,
                         data = HIC, alpha = 0.04)
res <- residuals(fit.hic, type = "sweighted2")
#plot(res)
#abline(h = 0)

References

Maluf, Y. S., Ferrari, S. L. P., and Queiroz, F. F. (2022). Robust beta regression through the logit transformation. arXiv:2209.11315.

Espinheira, P.L., Ferrari, S.L.P., and Cribari-Neto, F. (2008). On Beta Regression Residuals. Journal of Applied Statistics, 35:407–419.

Espinheira, P.L., Santos, E.G.and Cribari-Neto, F. (2017). On nonlinear beta regression residuals. Biometrical Journal, 59:445-461.

See Also

robustbetareg