Class of objects returned when performing approximate conditional inference for logistic and loglinear models.
a list whose elements are the spline interpolations of several first order and higher order statistics. They are used to implement the following likelihood quantities:
- the profile and modified profile log likelihoods;
- the Wald pivots from the unconditional and conditional MLEs;
- the profile and modified likelihood roots (the latter one with a suitable continuity correction);
- the Lugannani-Rice tail area approximation (with suitable continuity correction);
- the correction term used in the higher order statistics;
- the information and nuisance parameter aspects.
Method functions work mainly on this part of the object. In order to avoid errors in the calculation of confidence intervals and tail probabilities, this part of the object should not be modified.
a \(2\times 2\) matrix containing the unconditional and approximate conditional MLEs and their standard errors.
function call that created the cond object.
the model formula.
the variance function.
the covariate occurring in the model formula whose coefficient represents the parameter of interest.
diagnostics related to the decomposition of the higher order adjustments into an information and a nuisance parameters term. A value larger than 0.2 in absolute value is an index that higher order methods are needed. See Pierce and Peters (1992) for details.
number of output points that have been calculated exactly.
range of values omitted in the spline interpolation of some of the higher order statistics considered. The aim is to avoid numerical instabilities around the maximum likelihood estimate.
a logical value indicating whether there are any nuisance
parameters. If FALSE there are none.
This class of objects is returned from calls to the function
cond.glm.
Brazzale, A. R. (1999) Approximate conditional inference for logistic and loglinear models. J. Comput. Graph. Statist., 8, 653--661.
Brazzale, A. R. (2000) Practical Small-Sample Parametric Inference, Ph.D. Thesis N. 2230, Department of Mathematics, Swiss Federal Institute of Technology Lausanne.
Davison, A. C. (1988) Approximate conditional inference in generalized linear models. J. R. Statist. Soc. B, 50, 445--461.
Pierce, D. A. and Peters, D. (1992) Practical use of higher order asymptotics for multiparameter exponential families (with Discussion). J. R. Statist. Soc. B, 54, 701--737.