Description Usage Arguments Details Value Author(s) See Also

`print.moc`

prints information contained in a fitted `moc`

object. The `attributes`

*parameters* of the functions
`gmu`

, `gshape`

, `gextra`

and `gmixture`

will be
used to label the output.

`coef.moc`

returns the coefficients (estimated parameters) of a
fitted `moc`

object.

`fitted.moc`

computes the expected values for each observation
of a `moc`

object using its `expected`

function.

`obsfit.moc`

computes and prints the mean posterior
probabilities and the posterior means of a user specified function of
the expected and observed values, separated with respect
to the specified variable.

1 2 3 4 5 6 7 8 9 10 11 12 13 |

`x, object` |
Objects of class |

`split` |
If split is TRUE, returns a list with elements corresponding to mu, shape, extra and mixture parameters. |

`digits` |
Number of digits to be printed. |

`expand` |
Expand density, gmu, gshape, gextra, gmixture function body in the print. |

`transpose` |
Transpose fitted.mean and observed.mean in the print. |

`along` |
Splitting variable. |

`FUN` |
User defined function to apply to observed and expected values. |

`...` |
Unused. |

`obsfit.moc`

will first compute the posterior probabilities
for all subjects in each mixture using `post.moc`

and
then the weighted posterior mean probabilities

*\Sum_i (wt[i] * post[i,k]) / \Sum_i wt[i]*

The weighted posterior means of a function *g()* of the data
(which are the empirical estimators of the conditional expectation given
mixture group) are computed as

*\Sum_i (wt[i] * post[i,k] * g(y[i])) /
\Sum_i (wt[i] * post[i,k])*

where both sums are taken over index of valid data *y[i]*.

All these methods return their results invisibly.

Bernard Boulerice <bernard.boulerice.bb@gmail.com>

`moc`

, `residuals.moc`

, `post.moc`

,
`plot.moc`

, `AIC.moc`

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