egf-class: Description of Objects of Class 'egf'

In davidearn/epigrowthfit: Nonlinear Mixed Effects Models of Epidemic Growth

Description of Objects of Class egf

Description

Class 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.

Details

Currently, a legitimate egf object is a list with elements:

model

a copy of the so-named argument of egf.

frame

a 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.

priors

a list of the form list(top, bottom = list(beta, theta, Sigma)), where top, beta, theta, and Sigma are all lists of egf_prior objects.

control

a copy of the so-named argument of egf.

tmb_out

the list output of MakeADFun.

optimizer_out

the list output of the optimizer specified by control[["optimizer"]].

init, best

numeric vectors giving the values of the condensed bottom level parameter vector used in the first and maximal likelihood evaluations.

random

a 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, gradient

numeric vectors giving the value and gradient of the negative log marginal likelihood function at best[!random].

hessian

a 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.

coefficients

a list of the form list(fixed, random), where fixed and random are data frames preserving interpretive information about fixed and random effect coefficients.

contrasts

a 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.

call

the call to egf, enabling updates to the object by the default method of generic function update.

Bottom Level Parameter Vector

An estimated model is specified by a bottom level parameter vector that is the concatenation of three segments:

beta

the 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).

theta

the 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).

b

the 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.

Examples

methods(class = "egf")
help.search("\\.egf$", fields = "alias", package = "epigrowthfit")
## less verbosely: alias??`\\.egf$`

davidearn/epigrowthfit documentation built on Feb. 22, 2025, 12:44 p.m.