get_ranPars: Operations on lists of parameters

Description

get_ranPars returns various subsets of random-effect parameters. remove_from_parlist removes elements from a list of parameters, and from its type attribute.

Usage

get_ranPars(object, which=NULL, ...)
remove_from_parlist(parlist, removand=NULL, rm_names=names(unlist(removand)))

Arguments

object

An object of class HLfit, as returned by the fitting functions in spaMM.

which

For get_ranPars, the only non-null value is "corrPars", to return correlation parameters of random effects.

…

Other arguments that may be needed by some method.

parlist

A list of parameters. see Details.

removand

Optional. A list of parameters to be removed from parlist.

rm_names

Names of parameters to be removed from parlist. Mandatory if removand is not given.

Value

get_ranPars(.,which="corrPars") returns a (possibly nested) list of correlation parameters (or NULL if there is no such parameter). Top-level elements correspond to the different random effects. The list has a "type" attribute having the same nested-list structure and describing whether and how the parameters where fitted: "fix" means they where fixed, not fitted; "var" means they were fitted by HLfit's specific algorithms; "outer" means they were fitted by a generic optimization method. Note that get_ranPars does not provide random-coefficient parameters when these were fitted by HLfit's specific algorithms, but that get_inits_from_fit can do that.

remove_from_parlist returns a list of model parameters with given elements removed, and likewise for its (optional) type attribute. See Details for context of application.

Details

remove_from_parlist is designed to manipulate structured lists of parameters, such as a list with elements phi, lambda, and corrPars, the latter being itself a list structured as the return value of get_ranPars(.,which="corrPars"). remove_from_parlist may have an attribute type, also with elements phi, lambda, and corrPars... If given, removand must have the same structure (but typically not all the elements of parlist); otherwise, rm_names must have elements which match names of unlist(names(parlist)).

The bias estimated by bootstrap is one of two terms included in the get_cPredVar computation, which together account for uncertainty in fixed effects and dispersion parameters. The computation does not account for uncertainty in correlation parameters “outer-optimized” by fitme or corrHLfit. The bias computation would not be sufficient to account for the latter uncertainty, and thus the correlation parameters are fixed when the model is refitted on the bootstrap replicates. To account for uncertainty in correlation parameters, one should rather perform a parametric boostrap of the full model (typically using spaMM_boot(., type="residual")), which may take much more time.

Examples

Run this code

# NOT RUN {
data("wafers")
m1 <- HLfit(y ~X1+X2+(1|batch),
          resid.model = ~ 1 ,data=wafers,HLmethod="ML")
get_ranPars(m1,which="corrPars") # NULL since no correlated random effect     

parlist1 <- list(lambda=1,phi=2,corrPars=list("1"=list(rho=3,nu=4),"2"=list(rho=5)))
parlist2 <- list(lambda=NA,corrPars=list("1"=list(rho=NA))) # values of elements do not matter
remove_from_parlist(parlist1,parlist2) ## same result as:
remove_from_parlist(parlist1,rm_names = names(unlist(parlist2)))
# }