| re.MNB {MNB} | R Documentation |
Residual analysis
Description
Weighted, standardized weighted, Pearson, standardized Pearson and standardized deviance component residuals are available to assess possible departures from the multivariate negative binomial model for fitting correlated data with overdispersion.
Usage
re.MNB(star, formula, dataSet)
Arguments
star |
Initial values for the parameters to be optimized over. |
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
Similarly to GLMs theory (Agresti, 2015; Faraway, 2016), weighted and the standardized weighted residuals are deduced trough Fisher scoring iterative process. Based in the Pearson residual, Fabio (2017) suggest the standardized Pearson residuals for the multivariate model from the random intercept Poisson-GLG model. In addition, it is available the standardized deviance component residual for the ith subject (Fabio et al., 2012).
Value
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
Agresti, A. (2015). Foundations of Linear and Generalized Linear Models. Wiley.
Faraway, F. (2016). Extending the Linear Model with R: Generalized Linear, Mixed Effects and nonparametric regression models. Taylor & Francis, New York.
Fabio, L., Paula, G. A., and de Castro, M. (2012). A Poisson mixed model with nonormal random effect distribution. Computational Statistics and Data Analysis, 56, 1499-1510.
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)
r <- re.MNB(formula=Y ~ trt + period + trt:period +
offset(weeks),star=star,dataSet=seizures)
plot(r$ij.Sweighted.residual,cex.axis = 1.2, cex.lab = 1.2,
pch = 15,cex = 0.6, bg = 5,ylab="weighted.residual")
abline(h=c(-3,0,3),lwd = 2, lty = 2)
data(alzheimer)
head(alzheimer)
star <- list(phi=10,beta1=2, beta2=0.2)
r <- re.MNB(formula = Y ~ trat,star=star,dataSet=alzheimer)
names(r)