| MEtaProspModInf {missreg} | R Documentation |
A sub-function called by ML2Inf to supply values and its derivatives for
the first part of the profile loglikelihood regarding to the model of interest
using the discrete partition version.
MEtaProspModInf(theta,nderivs=2,y,x,wts=1,modelfn,off.set=0, ...)
theta |
Vector of the parameter values. |
nderivs |
Number of derivatives to be calculated, ranged from 0 (loglikelihood only) to 2 (information matrix). |
y |
The response of interest, can be either a vector or matrix. |
x |
A 3-dimensional array (R*C*M) specifying the covariates values,
with R the number of observations, C the length of theta and M the number
of linear predictors used. |
wts |
An optional vector of weights (n_i) to be used in the fitting process. The
default is 1. |
modelfn |
A class of sub-functions called by MEtaProspModInf to calculate the values
and their derivatives with respect to the linear predictor (eta's) of X for the model of
interest f(Y|X; theta). |
off.set |
The offset provided in a matrix form (R*M) with R the number of observations and M the number of linear predictors used. |
... |
Further arguments passed to or from related functions. |
This sub-function is used to implement prospective regression models with a fixed number of
M linear predictors. It calculates the value and its derivatives for the first part of
the profile loglikelihood in the form of l*(theta,Q) within each s-stratum
sum_{A(s)}{n_i^(s)*log{f(y_i^{(s)}|x_i^{(s)};theta)}} ,
with respect to theta through the M linear predictors (m=1,...,M),
eta_{im} = o_{im}+x_{i(m)}^T*theta
See "Description of the missreg Library" for all details.
A list with the following components
loglk |
Log-likelihood obtained from the current theta values |
score |
Score vector obtained from the curent theta values when nderivs>=1;
NULL otherwise. |
inf |
Observed information matrix obtained from the current theta values
when nderivs=2; NULL otherwise. |
Chris Wild, Yannan Jiang
Description of the missreg Library, Wild and Jiang, 2007.