Class of objects returned when fitting a nonlinear heteroscedastic model.
the MLEs of the regression coefficients, that is, of
the parameters appearing in the right-hand side of the
formula argument in the call that generated the
nlreg object.
the MLEs of the variance parameters appearing in the
weights argument of the call that generated the
nlreg object. If this argument was missing, the
MLE of \(\log(\sigma^2)\), the
logarithm of the constant variance, is returned.
a numerical vector with a single named element indicating the parameter of interest and the value to which it was fixed while fitting the nonlinear model.
the log likelihood from the fit.
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 fitted values, that is, the mean function evaluated at each data point.
the variance function evaluated at each data point.
the response/standardized residuals from the fit.
the starting values used to initialize the fitting routine.
an image of the call to nlreg, but with all the arguments
explicitly named.
a list containing information that is used in subsequent calculations with the following components:
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.
missingDataa logical value indicating whether the data
argument was missing in the call that generated the
nlreg object.
framethe name of the data frame if specified in the call to
nlreg.
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.
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.
This class of objects is returned by the nlreg
function to
represent a fitted nonlinear heteroscedastic model. Class
nlreg inherits from class nls, which represents a
homoscedastic nonlinear model fit.