spaMM-S3

dim.precision

print.arglist

print.predictor

print.vcov.HLfit

print.predictions

print.bootci4print

<p>These are not to be called by the user, or are waiting for documentation to be written.</p>

 internal

Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 <doi:10.1111/ecog.00566>), and Markov random field models on irregular grids (as considered in the 'INLA' package, <https://www.r-inla.org>), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted  likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) are also implemented.

Franois Rousset

spaMM

Mixed-Effect Models, with or without Spatial Random Effects

Fran<c3><a7>ois Rousset

Jean-Baptiste Ferdy

Alexandre Courtiol

GSL authors 

spaMM-S3 function

S3 methods of generics defined in other packages — spaMM-S3

S3 methods of generics defined in other packages

spaMM-S3: S3 methods of generics defined in other packages

Description

Arguments