aster: Aster Models

Description

Fits Aster Models.

Usage

aster(x, ...)
## S3 method for class 'default':
aster(x, root, pred, fam, modmat, parm,
    type = c("unconditional", "conditional"), famlist = fam.default(),
    origin, origin.type = c("model.type", "unconditional", "conditional"),
    method = c("trust", "nlm", "CG", "L-BFGS-B"), fscale, maxiter = 1000,
    nowarn = TRUE, newton = TRUE, optout = FALSE, coef.names, ...)
## S3 method for class 'formula':
aster(formula, pred, fam, varvar, idvar, root,
    data, parm, type = c("unconditional", "conditional"),
    famlist = fam.default(),
    origin, origin.type = c("model.type", "unconditional", "conditional"),
    method = c("trust", "nlm", "CG", "L-BFGS-B"), fscale, maxiter = 1000,
    nowarn = TRUE, newton = TRUE, optout = FALSE, ...)

Arguments

an nind by nnode matrix, the data for an aster model. The rows are independent and identically modeled random vectors. See details below for further requirements.

aster.formula constructs such an

root

an object of the same shape as x, the root data. For aster.default an nind by nnode matrix, For aster.formula an nind * nnode vector.

pred

an integer vector of length nnode determining the dependence graph of the aster model. pred[j] is the index of the predecessor of the node with index j unless the predecessor is a root node, in w

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.

modmat

an nind by nnode by ncoef three-dimensional array, the model matrix.

aster.formula constructs such a modmat from its formula, the data frame @data@, and the variables

concept

regression
exponential family
graphical model