Residual variance extractor
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.
residVar(object, which = "var", submodel = NULL, newdata = NULL)
object |
An object of class |
which |
Character: |
submodel |
integer: the index of a submodel, if |
newdata |
Either NULL, a matrix or data frame, or a numeric vector. See |
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. Other which values return an object of class family or formula as expected.
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.
# 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 scalarPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.