Extract various types of residuals from beta regression models: raw response residuals (observed  fitted), Pearson residuals (raw residuals scaled by square root of variance function), deviance residuals (scaled loglikelihood contributions), and different kinds of weighted residuals suggested by Espinheira et al. (2008).
1 2 3 4 
object 
fitted model object of class 
type 
character indicating type of residuals. 
... 
currently not used. 
The definitions of all residuals are provided in Espinheira et al. (2008):
Equation 2 for "pearson"
, last equation on page 409 for "deviance"
,
Equation 6 for "weighted"
, Equation 7 for "sweighted"
, and
Equation 8 for "sweighted2"
.
Espinheira et al. (2008) recommend to use "sweighted2"
, hence this is
the default in the residuals()
method. Note, however, that these are
rather burdensome to compute because they require operations of O(n^2)
and hence might be prohibitively costly in large sample.
CribariNeto, F., and Zeileis, A. (2010). Beta Regression in R. Journal of Statistical Software, 34(2), 1–24. http://www.jstatsoft.org/v34/i02/.
Espinheira, P.L., Ferrari, S.L.P., and CribariNeto, F. (2008). On Beta Regression Residuals. Journal of Applied Statistics, 35(4), 407–419.
Ferrari, S.L.P., and CribariNeto, F. (2004). Beta Regression for Modeling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  options(digits = 4)
data("GasolineYield", package = "betareg")
gy < betareg(yield ~ gravity + pressure + temp10 + temp, data = GasolineYield)
gy_res < cbind(
residuals(gy, type = "pearson"),
residuals(gy, type = "deviance"),
residuals(gy, type = "response"),
residuals(gy, type = "weighted"),
residuals(gy, type = "sweighted"),
residuals(gy, type = "sweighted2")
)
colnames(gy_res) < c("pearson", "deviance", "response",
"weighted", "sweighted", "sweighted2")
pairs(gy_res)

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