randomized quantile residual is available to assess possible departures from the multivariate negative binomial model for fitting correlated data with overdispersion.
qMNB(par, formula, dataSet)
Arguments
par: the maximum likelihood estimates.
formula: The structure matrix of covariates of dimension n x p (in models that include an intercept x should contain a column of ones).
dataSet: data
Returns
Randomized quantile Residuals
Details
The randomized quantile residual (Dunn and Smyth, 1996), which follow a standard normal distribution is used to assess departures from the multivariate negative binomial model.
Examples
data(seizures)head(seizures)star <-list(phi=1, beta0=1, beta1=1, beta2=1, beta3=1)mod <- fit.MNB(formula=Y ~ trt + period +trt:period + offset(log(weeks)),star=star,dataSet=seizures,tab=FALSE)par <- mod$par
names(par)<-c()res.q <- qMNB(par=par,formula=Y ~ trt + period + trt:period +offset(log(weeks)),dataSet=seizures)plot(res.q,ylim=c(-3,4.5),ylab="Randomized quantile residual",xlab="Index",pch=15,cex.lab =1.5, cex =0.6, bg =5)abline(h=c(-2,0,2),lty=3)#identify(res.q)data(alzheimer)head(alzheimer)star <- list(phi=10,beta1=2, beta2=0.2)mod <- fit.MNB(formula = Y ~ trat, star = star, dataSet = alzheimer,tab=FALSE)par<- mod$par
names(par)<- c()re.q <- qMNB(par=par,formula = Y ~ trat, dataSet = alzheimer)head(re.q)
References
Dunn, P. K. and Smyth, G. K. (1996). Randomized quantile residuals. Journal of Computational and Graphical Statistics, 5, 236-244.
Fabio, L. C., Villegas, C., Carrasco, J. M. F., and de Castro, M. (2021). D Diagnostic tools for a multivariate negative binomial model for fitting correlated data with overdispersion. Communications in Statistics - Theory and Methods. https://doi.org/10.1080/03610926.2021.1939380.