qMNB: Randomized quantile residual

In carrascojalmar/MNB: Diagnostic Tools for a Multivariate Negative Binomial Model

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 <[e-mail]>, Cristian M Villegas Lobos <[e-mail]> and Lizandra C Fabio <[e-mail]>

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)

carrascojalmar/MNB documentation built on May 15, 2022, 4:41 a.m.