Class of objects returned when calculating the maximum adjusted profile likelihood estimates of the variance parameters of a nonlinear heteroscedastic model.
the maximum adjusted profile likelihood estimates of the variance parameters.
the constrained MLEs of the regression coefficients given the maximum adjusted profile likelihood estimates of the variance parameters.
the values passed through the offset argument in the call
to mpl.nlreg that generated the mpl
object and to which the variance parameters were fixed.
the MLEs of the variance parameters.
the MLEs of the regression coefficients.
the (asymptotic) covariance matrix of the variance parameters, that is, the corresponding block in the inverse of the observed information matrix.
the (asymptotic) covariance matrix of the regression coefficients, that is, the corresponding block in the inverse of the observed information matrix.
the adjusted profile log likelihood from the fit.
the profile log likelihood from the fit.
the indicator of which higher order solution was used.
the model formula.
the formula expression of the mean function.
the formula expression of the variance function.
a list representing a summary of the original data with the following components.
'offset name'the predictor variable with no duplicated value.
replthe number of replicates available for each value of the predictor.
dupla vector of the same length than the predictor variable indicating the position of each data point in the offset name component.
t1the sum of the reponses for each design point in the offset name component.
t2the sum of the squared responses for each design point in the offset name component.
the number of observations.
the number of interations needed for convergence; only if
offset is not NULL.
an image of the call to mpl.nlreg, but with all the
arguments explicitly named.
a list containing information that is used in subsequent calculations, that is:
allParthe MLEs of all parameters.
homVara logical value indicating whether the variance function is constant.
xVara logical value indicating whether the variance function depends on the predictor variable.
hoathe value of the hoa argument in the call that
generated the nlreg object passed through the
fitted argument.
missingDataa logical value indicating whether the data
argument was missing in the call that generated the
nlreg object passed through the
fitted argument.
framethe name of the data frame if specified in the call to
nlreg that generated the fitted argument.
iterthe number of iteration required until convergence (only for non constant variance function).
mda function definition that returns the first two derivatives
of the mean function if hoa = TRUE in the function
call that generated the nlreg object passed through
the fitted argument.
vda function definition that returns the first two derivatives
of the variance function if hoa = TRUE in the
function call that generated the nlreg object passed
through the fitted argument.
This class of objects is returned by the
mpl.nlreg function. Class mpl inherits
from class nlreg.