mlogl {aster}

Minus Log Likelihood for Aster Models

Description

Minus the Log Likelihood for an Aster model, and its first and second derivative. This function is called inside R function aster. Users generally do not need to call it directly.

Usage

mlogl(parm, pred, fam, x, root, modmat, deriv = 0,
    type = c("unconditional", "conditional"), famlist = fam.default(),
    origin, origin.type = c("model.type", "unconditional", "conditional"))

Arguments

parm

parameter value (vector of regression coefficients) where we evaluate the log likelihood, etc. We also refer to length(parm) as ncoef.

pred

integer vector determining the graph. pred[j] is the index of the predecessor of the node with index j unless the predecessor is a root node, in which case pred[j] == 0. We also refer to length(pred) as nnode. This argument is required to satisfy pred[j] < j for all j.

fam

an integer vector of length nnode determining the exponential family structure of the aster model. Each element is an index into the vector of family specifications given by the argument famlist.

x

the response. If a matrix, rows are individuals, and columns are variables (nodes of graphical model). So ncol(x) == nnode and we also refer to nrow(x) as nind. If not a matrix, then x must be as if it were such a matrix and then dimension information removed by x = as.numeric(x).

root

A matrix or vector like x. Data root[i, j] is the data for a root node that is the predecessor of the response x[i, j] and is ignored when pred[j] > 0.

modmat

a three-dimensional array, nind by nnode by ncoef, the model matrix. Or a matrix, nind * nnode by ncoef (when x and root are one-dimensional of length nind * nnode).

deriv

derivatives wanted: 0, 1, or 2.

type

type of model. The value of this argument can be abbreviated.

famlist

a list of family specifications (see families).

origin

Distinguished point in parameter space. May be missing, in which case an unspecified default is provided. See aster for further explanation.

origin.type

Parameter space in which specified distinguished point is located. If "conditional" then argument "origin" is a conditional canonical parameter value. If "unconditional" then argument "origin" is an unconditional canonical parameter value. If "model.type" then the type is taken from argument "type". The value of this argument can be abbreviated.

Value

a list containing some of the following components:

value

minus the log likelihood.

gradient

minus the first derivative vector of the log likelihood (minus the score).

hessian

minus the second derivative matrix of the log likelihood (observed Fisher information).