imodel-dmod | R Documentation |

Specification of log–linear (graphical) model. The
'd' in the name `dmod`

refers to that it is a (graphical)
model for 'd'iscrete variables

```
dmod(
formula,
data,
marginal = NULL,
interactions = NULL,
fit = TRUE,
details = 0,
...
)
```

`formula` |
Model specification in one of the following forms: 1) a right-hand sided formula, 2) as a list of generators, 3) an undirected graph (represented either as an igraph object or as an adjacency matrix). Notice that there are certain model specification shortcuts, see Section 'details' below. |

`data` |
Either a table or a dataframe. In the latter case, the dataframe will be coerced to a table. See 'details' below. |

`marginal` |
Should only a subset of the variables be used in connection with the model specification shortcuts |

`interactions` |
A number given the highest order interactions in the model, see Section 'details' below. |

`fit` |
Should the model be fitted. |

`details` |
Control the amount of output; for debugging purposes. |

`...` |
Additional arguments; currently no used. |

The independence model can be specified as `~.^1`

and
`~.^.`

specifies the saturated model. Setting
e.g. `interactions=3`

implies that there will be at most
three factor interactions in the model.

Data can be specified as a table of counts or as a dataframe. If
data is a dataframe then it will be converted to a table (using
`xtabs()`

). This means that if the dataframe contains numeric
values then the you can get a very sparse and high dimensional
table. When a dataframe contains numeric values it may be
worthwhile to discretize data using the `cut()`

function.

The `marginal`

argument can be used for specifying the
independence or saturated models for only a subset of the
variables. When `marginal`

is given the corresponding marginal
table of data is formed and used in the analysis (notice that this
is different from the behaviour of `loglin()`

which uses the
full table.

The `triangulate()`

method for discrete models (dModel
objects) will for a model look at the dependence graph for the
model.

An object of class `dModel`

.

Søren Højsgaard, sorenh@math.aau.dk

`cmod`

, `mmod`

```
## Graphical log-linear model
data(reinis)
dm1 <- dmod(~ .^., reinis)
dm2 <- backward(dm1, k=2)
dm3 <- backward(dm1, k=2, fixin=list(c("family", "phys", "systol")))
## At most 3-factor interactions
dm1<-dmod(~ .^., data=reinis, interactions=3)
```

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