envelope.MNB: Simulation envelope

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

Simulation envelope

Description

Simulated envelopes in normal probability plots

Usage

envelope.MNB(star, formula, dataSet, n.r, nsim, plot = TRUE)

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
n.r	Indicator which residual type graphics. 1 - weighted, 2 - Standardized weighted, 3 - Pearson, 4 - Standardized Pearson, 5 - standardized deviance component residuals and 6 - randomized quantile residuals.
nsim	Number of Monte Carlo replicates.
plot	TRUE or FALSE. Indicates if a graph should be plotted.

Details

Atkinson (1985), suggests the use of simulated envelopes in normal probability plots to facilitate the goodness of fit.

Value

L, residuals and simulation envelopes in normal probability plots

Author(s)

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

References

• Atkinson A.C. (1985). Plots, Transformations and Regression: An Introduction to Graphical Methods of Diagnostic Regression Analysis. Oxford University Press, New York.

• 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)

envelope.MNB(formula=Y ~ trt + period + trt:period +
offset(weeks),star=star,nsim=21,n.r=6,
dataSet=seizures,plot=FALSE)

data(alzheimer)
head(alzheimer)

star <- list(phi=10,beta1=2, beta2=0.2)
envelope.MNB(formula=Y ~ trat, star=star, nsim=21, n.r=6,
dataSet = alzheimer,plot=FALSE)

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