Description Usage Arguments CAUTION

This function is a constructor for the `pdInd`

class, representing a
positive-definite matrix with zero covariances except possibly in the first
row and column. If the matrix associated with `object`

is of dimension
$n$, it is represented by $n + (n-1)$ unrestricted parameters representing a
lower-triangular log-Cholesky decomposition. The first $n$ parameters are
the logs of the diagonal elements of the matrix and the last $n-1$
components are the $n-1$ remaining elements of the lower-triangular
decomposition corresponding the to the possibly non-zero covariances in the
first row.

1 2 |

`value` |
an option initialization value, which can be any of the following ... |

`form` |
an optional one-sided linear formula specifying the row/column
names for the matrix represented by |

`nam` |
and optional vector of character strings specifying the
row/column names for the matrix represented by |

`data` |
and optional data frame i which to evaluate the variables names
in |

`cov` |
optional position in lower triangle of covariances that are estimated and, thus, possibly non-zero. The default is that the covariances in the first column are estimated and possibly non-zero. |

`zero` |
optional way of specifying covariances constrained to be equal
to zero. Only the lower triangular portion of the |

cov and zero do not work. Until fixed, `pdInd`

only creates the
default covariance pattern in which the only non-zero covariances are those with the first element.

gmonette/spida documentation built on May 17, 2019, 7:25 a.m.

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