sparse_precision

<p>A fitting algorithm efficient for random effects with sparse precision matrix (i.e. inverse covariance matrix) is implemented. It is used by default only in two cases: for conditional autoregressive models (with a random effect of the form <code>adjacency(1|&lt;grouping factor&gt;)</code>), and when the <code><a rd-options="" href="/link/covStruct?package=spaMM&version=2.2.0" data-mini-rdoc="spaMM::covStruct">covStruct</a></code> syntax is used to provide a fixed precision matrix (see <code><a rd-options="" href="/link/pedigree?package=spaMM&version=2.2.0" data-mini-rdoc="spaMM::pedigree">pedigree</a></code> for an example). A non-default choice of fitting algorithm can be selected in this and other models by using <code>spaMM.options(sparse_precision= &lt;TRUE|FALSE&gt;)</code> with often poor results.
A precision matrix is meaningful for a Gaussian random effect, but beyond this the algorithm works for HGLMs, i.e. the model may include another random effect with non-Gaussian distribution.</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 

sparse_precision function

<p>A fitting algorithm efficient for random effects with sparse precision matrix (i.e. inverse covariance matrix) is implemented. It is used by default only in two cases: for conditional autoregressive models (with a random effect of the form <code>adjacency(1|&lt;grouping factor&gt;)</code>), and when the <code><a rd-options='' href='covStruct'>covStruct</a></code> syntax is used to provide a fixed precision matrix (see <code><a rd-options='' href='pedigree'>pedigree</a></code> for an example). A non-default choice of fitting algorithm can be selected in this and other models by using <code>spaMM.options(sparse_precision= &lt;TRUE|FALSE&gt;)</code> with often poor results.
A precision matrix is meaningful for a Gaussian random effect, but beyond this the algorithm works for HGLMs, i.e. the model may include another random effect with non-Gaussian distribution.</p>

sparse_precision: Sparse_precision algorithm

Description

Arguments