Possible arguments

A list of values to compute the CDF for, for all fixed effects

A list of quantiles to compute for all fixed effects

The strategy used to expand factors into fixed effects based on their levels. The default strategy is us use the model.matrix-function for which NA's are not allowed (expand.factor.strategy="model.matrix") and levels are possible removed. The alternative option (expand.factor.strategy="inla") use an inla-spesific expansion which expand a factor into one fixed effects for each level, do allow for NA's and all levels are present in the model. In this case, factors MUST BE factors in the data.frame/list and NOT added as .+factor(x1)+. in the formula only.

Prior mean for all fixed effects except the intercept. Alternatively, a named list with specific means where name=default applies to unmatched names. For example control.fixed=list(mean=list(a=1, b=2, default=0)) assign 'mean=1' to fixed effect 'a' , 'mean=2' to effect 'b' and 'mean=0' to all others. (default 0.0)

Prior mean for the intercept (default 0.0)

Default precision for all fixed effects except the intercept. Alternatively, a named list with specific means where name=default applies to unmatched names. For example control.fixed=list(prec=list(a=1, b=2, default=0.01)) assign 'prec=1' to fixed effect 'a' , 'prec=2' to effect 'b' and 'prec=0.01' to all others. (default 0.001)

Default precision the intercept (default 0.0)

Compute marginals for the fixed effects ? (default TRUE)

Compute the posterior correlation matrix for all fixed effects? (default FALSE) OOPS: This option will set up appropriate linear combinations and the results are shown as the posterior correlation matrix of the linear combinations. This option will imply control.inla=list(lincomb.derived.correlation.matrix=TRUE).

A vector of names of expanded fixed effects to remove from the model-matrix. This is an expert option, and should only be used if you know what you are doing.