egfClass egf designates models estimated by function egf.
Objects of this class hold information about an estimated model.
Components can be accessed directly. However, as the components
are subject to change without notice, portable code will rely on
exported methods for interrogation.
An estimated model is specified by a bottom level parameter vector that is the concatenation of three segments:
betathe result of unlist(lbeta), where lbeta is a list
of numeric vectors of fixed effect coefficients,
with one vector for each top level nonlinear model parameter.
The order of top level parameters is specified by
egf_top(model).
thetathe result of unlist(ltheta), where ltheta is a list
of numeric vectors of random effect covariance parameters,
with one vector for each distinct random effect term in
formula_parameters.
Each vector parametrizes a random effect covariance matrix via
theta2cov and its inverse cov2theta.
The list Sigma mentioned in the description of egf
argument formula_priors is precisely
lapply(ltheta, theta2cov).
bthe result of unlist(lb), where lb is a list
of numeric matrices of scaled random effect coefficients,
corresponding elementwise to ltheta.
The columns of lb[[i]] (one per level of the grouping variable)
are interpreted as samples from a zero mean, unit variance multivariate
normal distribution with covariance matrix
cov2cor(theta2cov(ltheta[[i]])).
When elements of this vector are “mapped” via egf
argument map, likelihood is defined as a function of the condensed
vector that excludes mapped elements.
Methods are defined for generic functions
coef,
fixef, and
ranef
to allow users to interrogate the structure of the vector.
Currently, a legitimate egf object is a list with elements:
modela copy of the so-named argument of egf.
framea list of the form list(ts, windows, parameters, extra).
ts and windows are data frames preserving time series
and fitting window endpoints.
parameters is a list of mixed effects model frames,
with one element for each top level nonlinear model parameter.
extra is a data frame preserving additional variables
specified in call[["select_windows"]].
windows, the model frames listed in parameters,
and extra all correspond rowwise.
priorsa list of the form list(top, bottom = list(beta, theta, Sigma)),
where top, beta, theta, and Sigma are all
lists of egf_prior objects.
controla copy of the so-named argument of egf.
tmb_outthe list output of MakeADFun.
optimizer_outthe list output of the optimizer specified by control[["optimizer"]].
init, bestnumeric vectors giving the values of the condensed bottom level parameter vector used in the first and maximal likelihood evaluations.
randoma logical vector indexing the elements of the condensed bottom level
parameter vector that are not arguments of the negative log
marginal likelihood function. It indexes
all elements of segment b (random effect coefficients) and
(but only if control[["profile"]] = TRUE)
all elements of segment beta (fixed effect coefficients).
value, gradientnumeric vectors giving the value and gradient of the negative log
marginal likelihood function at best[!random].
hessiana logical flag indicating whether the Hessian matrix of the negative log
marginal likelihood function is positive definite at best[!random].
NA means that the matrix has not been computed.
coefficientsa list of the form list(fixed, random), where fixed
and random are data frames preserving interpretive information
about fixed and random effect coefficients.
contrastsa list of the form list(fixed, random), where fixed
and random are lists preserving contrasts used to construct
the fixed and random effects design matrices.
callthe call to egf, enabling updates to the object by
the default method of generic function update.
methods(class = "egf")
help.search("\\.egf$", fields = "alias", package = "epigrowthfit")
## less verbosely: alias??`\\.egf$`
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