Description Usage Arguments Details Value Author(s) References See Also Examples

This set of functions is a modification of the `pdMat`

class `pdIdent`

from library `nlme`

. The modification is to replace the log parameterization used in `pdMat`

with a `notLog2`

parameterization, since the latter avoids
indefiniteness in the likelihood and associated convergence problems: the
parameters also relate to variances rather than standard deviations, for
consistency with the `pdTens`

class. The functions are particularly useful for
working with Generalized Additive Mixed Models where variance parameters/smoothing parameters can
be very large or very small, so that overflow or underflow can be a problem.

These functions would not normally be called directly, although unlike the
`pdTens`

class it is easy to do so.

1 2 |

`value` |
Initialization values for parameters. Not normally used. |

`form` |
A one sided formula specifying the random effects structure. |

`nam` |
a names argument, not normally used with this class. |

`data` |
data frame in which to evaluate formula. |

The following functions are provided: `Dim.pdIndot`

, `coef.pdIdnot`

, `corMatrix.pdIdnot`

,
`logDet.pdIdnot`

, `pdConstruct.pdIdnot`

, `pdFactor.pdIdnot`

, `pdMatrix.pdIdnot`

,
`solve.pdIdnot`

, `summary.pdIdnot`

. (e.g. `mgcv:::coef.pdIdnot`

to access.)

Note that while the `pdFactor`

and `pdMatrix`

functions return the inverse of the scaled random
effect covariance matrix or its factor, the `pdConstruct`

function is initialised with estimates of the
scaled covariance matrix itself.

A class `pdIdnot`

object, or related quantities. See the `nlme`

documentation for further details.

Simon N. Wood [email protected]

Pinheiro J.C. and Bates, D.M. (2000) Mixed effects Models in S and S-PLUS. Springer

The `nlme`

source code.

http://www.maths.bris.ac.uk/~sw15190/

1 | ```
# see gamm
``` |

mgcv documentation built on Jan. 15, 2018, 1:04 a.m.

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