mpl.object: Maximum Adjusted Profile Likelihood Object

In nlreg: Higher Order Inference for Nonlinear Heteroscedastic Models

Description

Class of objects returned when calculating the maximum adjusted profile likelihood estimates of the variance parameters of a nonlinear heteroscedastic model.

Arguments

The following components must be included in a mpl object:

varPar	the maximum adjusted profile likelihood estimates of the variance parameters.
coefficients	the constrained MLEs of the regression coefficients given the maximum adjusted profile likelihood estimates of the variance parameters.
offset	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.
varParMLE	the MLEs of the variance parameters.
coefMLE	the MLEs of the regression coefficients.
varParCov	the (asymptotic) covariance matrix of the variance parameters, that is, the corresponding block in the inverse of the observed information matrix.
coefCov	the (asymptotic) covariance matrix of the regression coefficients, that is, the corresponding block in the inverse of the observed information matrix.
lmp	the adjusted profile log likelihood from the fit.
lp	the profile log likelihood from the fit.
stats	the indicator of which higher order solution was used.
formula	the model formula.
meanFun	the formula expression of the mean function.
varFun	the formula expression of the variance function.
data	a list representing a summary of the original data with the following components. 'offset name' the predictor variable with no duplicated value. repl the number of replicates available for each value of the predictor. dupl a vector of the same length than the predictor variable indicating the position of each data point in the offset name component. t1 the sum of the reponses for each design point in the offset name component. t2 the sum of the squared responses for each design point in the offset name component.
nobs	the number of observations.
iter	the number of interations needed for convergence; only if offset is not NULL.
call	an image of the call to mpl.nlreg, but with all the arguments explicitly named.
ws	a list containing information that is used in subsequent calculations, that is: allPar the MLEs of all parameters. homVar a logical value indicating whether the variance function is constant. xVar a logical value indicating whether the variance function depends on the predictor variable. hoa the value of the hoa argument in the call that generated the nlreg object passed through the fitted argument. missingData a logical value indicating whether the data argument was missing in the call that generated the nlreg object passed through the fitted argument. frame the name of the data frame if specified in the call to nlreg that generated the fitted argument. iter the number of iteration required until convergence (only for non constant variance function). md a 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. vd a 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.

Generation

This class of objects is returned by the mpl.nlreg function. Class mpl inherits from class nlreg.

Methods

Objects of this class have methods for the functions print, summary, coef and param.

Note

The coefficients and variance parameters should be extracted by the generic functions of the same name, rather than by the $ operator.

The data and ws components are not intended to be directly used by users, but rather contain information used by functions such as summary.

See Also

mpl.nlreg, mpl, nlreg.object

nlreg documentation built on May 1, 2019, 9:21 p.m.