R: Maximum Adjusted Profile Likelihood Object

mpl.object {nlreg}

R Documentation

Maximum Adjusted Profile Likelihood Object

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.