spaMM (version 4.1.20)

residVar: Residual variance extractor

Description

Extracts from a fit object the residual variance or, depending on the which argument, a family dispersion parameter, or a vector of values of the dispersion parameter phi (which is not the residual variance itself for gamma-response models), or further information about the residual variance model.

Usage

residVar(object, which = "var", submodel = NULL, newdata = NULL)

Value

Default which="var" (or "phi") always return a vector of residual variances (or, alternatively, phi values) of length the number of responses of the fit (or only the number of responses for a submodel, if relevant). which="fit" returns an object of class HLfit, glm, or a single scalar depending on the residual dispersion model (which="fit" is the option to be used to extract the scalar phi value). Other which values return an object of class family or formula as expected.

Arguments

object

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

which

Character: "var" for the fitted residual variances, "phi" for the fitted phi values, "fam_parm" for the dispersion parameter of COMPoisson and negbin families, "fit" for the fitted residual model (a GLM or a mixed model for residual variances, if not a simpler object), and "family" or "formula" for such properties of the residual model.

submodel

integer: the index of a submodel, if object is a multivariate-response model fitted by fitmv. This argument is mandatory for all which values except "var" and "phi".

newdata

Either NULL, a matrix or data frame, or a numeric vector. See predict.HLfit for details.

See Also

get_residVar is a alternative extractor of residual variances with different features inherited from get_predVar. In particular, it is more suited for computing the residual variances of new realizations of a fitted model, not accounting for prior weights used in fitting the model (basic examples of using the IsoriX package provide a context where this is the appropriate design decision). By contrast, residVar aims to account for prior weights.

Examples

# data preparation: simulated trivial life-history data
set.seed(123)
nind <- 20L
u <- rnorm(nind)
lfh <- data.frame(
  id=seq_len(nind), id2=seq_len(nind), 
  feco= rpois(nind, lambda = exp(1+u)), 
  growth=rgamma(nind,shape=1/0.2, scale=0.2*exp(1+u)) # mean=exp(1+u), var= 0.2*mean^2
)
# multivariate-response fit                  
fitlfh <- fitmv(submodels=list(list(feco ~ 1+(1|id), family=poisson()),
                               list(growth ~ 1+(1|id), family=Gamma(log))),
                data=lfh)
#
residVar(fitlfh)
residVar(fitlfh, which="phi") # shows fixed phi=1 for Poisson responses
residVar(fitlfh, submodel=2)
residVar(fitlfh, which="family", submodel=2)
residVar(fitlfh, which="formula", submodel=2)
residVar(fitlfh, which="fit", submodel=2) # Fit here characterized by a single scalar