varExp
class, representing an
exponential variance function structure. Letting v denote the variance
covariate and s2(v) denote the variance function evaluated at v, the
exponential variance function is defined as s2(v) = exp(2 * t * v),
where t is the variance function coefficient. When a grouping factor
is present, a different t is used for each factor level.
varExp(value, form, fixed)
Value
must have
length one, unless a grouping factor is specified in
form
.
If
value
has length greater than one, it must have names
which identify its elements to the levels of the grouping factor
defined in
form
. If a grouping factor is present in
form
and
value
has length one, its value will be
assigned to all grouping levels. Default is
numeric(0)
, which
results in a vector of zeros of appropriate length being assigned to
the coefficients when
object
is initialized (corresponding
to constant variance equal to one).
v
and, optionally, a grouping factor
g
for the coefficients. The variance covariate must evaluate to a
numeric vector and may involve expressions using
"."
, representing a
fitted model object from which fitted values (
fitted(.)
) and
residuals (
resid(.)
) can be extracted (this allows the variance
covariate to be updated during the optimization of an objective
function). When a grouping factor is present in
form
, a different
coefficient value is used for each of its levels. Several grouping
variables may be simultaneously specified, separated by the
*
operator, like in `~ v | g1 * g2 * g3'. In this case, the levels of
each grouping variable are pasted together and the resulting factor is
used to group the observations. Defaults to `~ fitted(.)'
representing a variance covariate given by the fitted values of a
fitted model object and no grouping factor.
form
,
fixed
must have names identifying
which coefficients are to be fixed. Coefficients included in
fixed
are not allowed to vary during the optimization of an
objective function. Defaults to
NULL
, corresponding to no
fixed coefficients.
varExp
object representing an exponential variance function
structure, also inheriting from class
varFunc
.
You can use the functions
varWeights.varExp
and
coef.varExp
to extract the weights and coefficients, respectively, from the variance function structure. For more details, see the links below for the generic functions
varWeights
and
coef.varFunc
.
vf1 <- varExp(0.2, form = ~age|Sex)