ggmfit | R Documentation |

Fit graphical Gaussian model by iterative proportional fitting.

```
ggmfit(
S,
n.obs,
glist,
start = NULL,
eps = 1e-12,
iter = 1000,
details = 0,
...
)
```

`S` |
Empirical covariance matrix |

`n.obs` |
Number of observations |

`glist` |
Generating class for model (a list) |

`start` |
Initial value for concentration matrix |

`eps` |
Convergence criterion |

`iter` |
Maximum number of iterations |

`details` |
Controlling the amount of output. |

`...` |
Optional arguments; currently not used |

`ggmfit`

is based on a C implementation. `ggmfitr`

is
implemented purely in R (and is provided mainly as a benchmark for the
C-version).

A list with

`lrt` |
Likelihood ratio statistic (-2logL) |

`df` |
Degrees of freedom |

`logL` |
log likelihood |

`K` |
Estimated concentration matrix (inverse covariance matrix) |

Søren Højsgaard, sorenh@math.aau.dk

`cmod`

, `loglin`

```
## Fitting "butterfly model" to mathmark data
## Notice that the output from the two fitting functions is not
## entirely identical.
data(math)
glist <- list(c("al", "st", "an"), c("me", "ve", "al"))
d <- cov.wt(math, method="ML")
ggmfit (d$cov, d$n.obs, glist)
```

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.