| qMNB {MNB} | R Documentation |
Randomized quantile residual
Description
randomized quantile residual is available to assess possible departures from the multivariate negative binomial model for fitting correlated data with overdispersion.
Usage
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 |
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.
Value
Randomized quantile Residuals
Author(s)
Jalmar M F Carrasco <carrascojalmar@gmail.com>, Cristian M Villegas Lobos <master.villegas@gmail.com> and Lizandra C Fabio <lizandrafabio@gmail.com>
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.
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)