Randomized Quantile Residuals for the Log-EVBS Regression Model

Computes randomized quantile residuals (Dunn and Smyth, 1996) for a log-EVBS regression fit. Under a correctly specified model these residuals are approximately standard normal, so departures from normality indicate lack of fit.

Usage

rqrandomized(X, t)

Arguments

X

A numeric design matrix with an intercept column.

t

A numeric vector of strictly positive responses.

Value

A numeric vector of length length(t) containing the randomized quantile residuals.

References

Dunn, P. K. and Smyth, G. K. (1996). Randomized quantile residuals. Journal of Computational and Graphical Statistics, 5, 236–244.

See also

rcoxsnell, envelope_qq, evbsreg.fit.

Examples

data(itajai)
X <- cbind(1, itajai$pressure)
r <- rqrandomized(X, itajai$wind)
shapiro.test(r)
#> 
#> 	Shapiro-Wilk normality test
#> 
#> data:  r
#> W = 0.98727, p-value = 0.3021
#>