Extracting Information from Objects simplexreg
Description
Methods for extracting information from fitted simplex regression model
objects of class "simplexreg"
Usage
1 2 3 4 5 6 7 8 9 10 11 
Arguments
object 
fitted model object of class "simplexreg" 
type 
character specifying type of residuals to be included, see

... 
currently not used 
Details
These functions make it possible to extract information from objects of class
"simplexreg"
. Wald statistics as well as the pvalues of regression coefficients
are given in the summary
output. If GEE = FALSE
, based on the fitted
coefficients, a χ^2 test is performed and the pvalue is reported in the output.
Otherwise, coefficients of the autocorrelation α, ρ, (see Song
et. al (2004)), are also involved.
Model coefficients and their covariance matrix could be extracted by the coef
,
and vcov
, respectively. For simplex GLM models (GEE = FALSE
), Akaike Information
Criterion and Bayesian Information Criterion could be calculated using generic functions AIC
and BIC
, respectively.
Author(s)
Chengchun Shi
References
BarndorffNielsen, O.E. and Jorgensen, B. (1991) Some parametric models on the simplex. Journal of Multivariate Analysis, 39: 106–116
Jorgensen, B. (1997) The Theory of Dispersion Models. London: Chapman and Hall
Song, P. and Qiu, Z. and Tan, M. (2004) Modelling Heterogeneous Dispersion in Marginal Models for Longitudinal Proportional Data. Biometrical Journal, 46: 540–553
Zhang, P. and Qiu, Z. and Shi, C. (2016) simplexreg: An R Package for Regression Analysis of Proportional Data Using the Simplex Distribution. Journal of Statistical Software, 71: 1–21
See Also
simplexreg
, residuals.simplexreg
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  ## fit the model
data("sdac", package = "simplexreg")
sim.glm2 < simplexreg(rcd~ageadj+chemoage, link = "logit",
data = sdac)
data("retinal", package = "simplexreg")
sim.gee2 < simplexreg(Gas~LogT+LogT2+LevelLogT+LevelTime,
link = "logit", corr = "AR1", id = ID, data = retinal)
## extract information
summary(sim.glm2, type = "appstdPerr")
coef(sim.glm2)
vcov(sim.glm2)
AIC(sim.glm2)
BIC(sim.glm2)
summary(sim.gee2, type = "stdscor")
coef(sim.gee2)
vcov(sim.glm2)
