After fitting the distribution, it is often desirable to see whether the moments of the data matches with the fitted distribution. This function computes the theoretical and actual moments for the FMKL GLD maximum likelihood estimation and starship method.

1 | ```
fun.comp.moments.ml.2(theo.obj, data, name = "ML")
``` |

`theo.obj` |
Fitted distribution parameters, there should be two sets, both FMKL GLD. |

`data` |
Data set used |

`name` |
Naming the method used in fitting the distribution, by default this is "ML". |

`r.mat` |
A matrix showing the mean, variance, skewness and kurtosis of the fitted distribution in comparison to the data set. |

`eval.mat` |
Absolute difference in each of the four moments from the data under each of the distibutional fits. |

To compare all three fits under `fun.data.fit.ml`

see
`fun.comp.moments.ml`

function.

Steve Su

1 2 3 4 5 6 7 8 9 | ```
## Generate random normally distributed observations.
# junk<-rnorm(1000,3,2)
## Fit the dataset using fun.data.ml
# fit<-cbind(fun.RMFMKL.ml(junk),starship(junk)$lambda)
## Compare the resulting fits. It is usually the case the maximum likelihood
## provides better estimation of the moments than the starship method.
# fun.comp.moments.ml.2(fit,junk)
``` |

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