mlogl {aster} R Documentation

## 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).

[Package aster version 1.1-2 Index]