summary.simplexreg {simplexreg} | R Documentation |
Extracting Information from Objects simplexreg
Description
Methods for extracting information from fitted simplex regression model
objects of class "simplexreg"
Usage
## S3 method for class 'simplexreg'
## S3 method for class 'simplexreg'
summary(object, type = "stdPerr", ...)
## S3 method for class 'simplexreg'
## S3 method for class 'simplexreg'
coef(object, ...)
## S3 method for class 'simplexreg'
## S3 method for class 'simplexreg'
vcov(object, ...)
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 p-values of regression coefficients
are given in the summary
output. If GEE = FALSE
, based on the fitted
coefficients, a \chi^2
test is performed and the p-value is reported in the output.
Otherwise, coefficients of the autocorrelation \alpha
, \rho
, (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
Barndorff-Nielsen, 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
## fit the model
data("sdac", package = "simplexreg")
sim.glm2 <- simplexreg(rcd~ageadj+chemo|age, link = "logit",
data = sdac)
data("retinal", package = "simplexreg")
sim.gee2 <- simplexreg(Gas~LogT+LogT2+Level|LogT+Level|Time,
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)