residuals.bayesbr {bayesbr} | R Documentation |
bayesbr
ObjectsA function that receives model information and calculates the residuals according to the required residual.
## S3 method for class 'bayesbr' residuals(object, type = c("", "quantile", "sweighted", "pearson","ordinary"),...)
object |
an object of the class bayesbr, containing the list returned from the |
type |
A character containing the residual type returned by the model among the possibilities. The type of residue can be quantile, sweighted, pearson or ordinary. The default is quantile. |
... |
further arguments passed to or from other methods. |
The definitions of the waste generated by the package are available in Espinheira (2008): "pearson" in Equation 2, "sweighted" in Equation 7; and in Pereira (2019): "quantile" in Equation 5;
The type of residue "response" is calculated from the difference between the estimated theta and the variable response of the model.
A vector containing the model residual according to the type of residual calculated
doi: 10.1080/0266476042000214501 Ferrari, S., & Cribari-Neto, F. (2004). Beta regression for modelling rates and proportions. Journal of applied statistics, 31(7), 799-815.
doi: 10.1080/00949650701829380 Simas, A. B., & Cordeiro, G. M. (2009). Adjusted Pearson residuals in exponential family nonlinear models. Journal of Statistical Computation and Simulation, 79(4), 411-425.
doi: 10.1080/02664760701834931 Espinheira, P. L., Ferrari, S. L., & Cribari-Neto, F. (2008). On beta regression residuals. Journal of Applied Statistics, 35(4), 407-419.
doi: 10.1080/00949655.2012.736993 Anholeto, T., Sandoval, M. C., & Botter, D. A. (2014). Adjusted Pearson residuals in beta regression models. Journal of Statistical Computation and Simulation, 84(5), 999-1014.
doi: 10.1080/03610918.2017.1381740 Pereira, G. H. (2019). On quantile residuals in beta regression. Communications in Statistics-Simulation and Computation, 48(1), 302-316.
bayesbr
,summary.bayesbr
,predict.bayesbr
data("CarTask", package = "bayesbr") bbr = bayesbr(probability~task + NFCCscale,data=CarTask, iter = 100, mean_betas = c(1, 0.5,1.2)) residuals(bbr, type = "quantile") residuals(bbr, type = "ordinary") residuals(bbr, type = "sweighted") residuals(bbr, type = "pearson")