logLik.BinaryEPPM

This function is generic. It is a method for extracting the log-likelihood for objects of class "BinaryEPPM".

 methods 

Under- and over-dispersed binary data are modeled using an extended Poisson
process model (EPPM) appropriate for binary data. A feature of the model is that the
under-dispersion relative to the binomial distribution only needs to be greater than
zero, but the over-dispersion is restricted compared to other distributional models
such as the beta and correlated binomials. Because of this, the examples focus on
under-dispersed data and how, in combination with the beta or correlated distributions,
flexible models can be fitted to data displaying both under- and over-dispersion. Using
Generalized Linear Model (GLM) terminology, the functions utilize linear predictors for
the probability of success and scale-factor with various link functions for p, and log
link for scale-factor, to fit a variety of models relevant to areas such as bioassay.
Details of the EPPM are in Faddy and Smith (2012) <doi:10.1002/bimj.201100214> and
Smith and Faddy (2019) <doi:10.18637/jss.v090.i08>.

David Smith

BinaryEPPM

Mean and Scale-Factor Modeling of Under- And Over-Dispersed
Binary Data

logLik.BinaryEPPM function

<dl> <dt>object</dt>
<dd>fitted model object of class "BinaryEPPM".</dd> <dt>...</dt>
<dd>some methods for this generic function require additional arguments</dd></dl>

Arguments

David M. Smith &lt;dmccsmith@verizon.net&gt;

Author

Extract Log-Likelihood — logLik.BinaryEPPM

<dl>

 <dt>object</dt>
<dd>fitted model object of class "BinaryEPPM".</dd>

 <dt>...</dt>
<dd>some methods for this generic function require additional arguments</dd>

</dl>

logLik.BinaryEPPM: Extract Log-Likelihood

Description

Usage

Value

Arguments

Author

Details

See Also