Extracting Information from Objects simplexreg

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Description

Methods for extracting information from fitted simplex regression model objects of class "simplexreg"

Usage

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## 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 residuals.simplexreg

...

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 χ^2 test is performed and the p-value 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

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

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## 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)