separation

<p>Separation occurs in binomial response models when a combination of the predictor variables perfectly predicts the response. In such a case the estimates of the coefficients for these variables diverge to (-)infinity, and the numerical algorithms typically fail. To anticipate such a problem, the fitting functions in <code>spaMM</code> check for separation by default, except for large data sets where this may take time, in which case a message notifies this to the user. The threshold size of the data is set by <code>spaMM.options(separation_max=&lt;.&gt;)</code></p>

Inference in mixed-effect models, including generalized linear mixed models with spatial
correlations and models with non-Gaussian random effects (e.g., Beta Binomial,
or negative-binomial mixed models). Variation in residual variance is handled and can be modelled
as a linear model. Various approximations
of likelihood or restricted likelihood are implement, in particular h-likelihood and Laplace approximation.

Franois Rousset

spaMM

Mixed-Effect Models, Particularly Spatial Models

Fran<c3><a7>ois Rousset

Jean-Baptiste Ferdy

Alexandre Courtiol

GSL authors 

separation function

Checking separation in binomial-response models — separation

Checking separation in binomial-response models

separation: Checking separation in binomial-response models

Description

Arguments