Performs a test of convergence based on the L2 norm of the change in the parameter estimates.

1 | ```
conv.test(theta1, theta2, epsilon)
``` |

`theta1` |
vector of parameter estimates at previous step. |

`theta2` |
vector of parameter estimates at current step. |

`epsilon` |
positive convergence tolerance. |

This is used as the convergence test in the
`addreg`

fitting functions, because the EM
algorithm may converge slowly such that the test based on
the deviance used in `glm.fit`

(see
`glm.control`

) may report convergence at a
point away from the actual optimum.

A logical; `TRUE`

if
```
sqrt(sum((theta1-theta2)**2))/sqrt(sum(theta1**2)) <
epsilon
```

, `FALSE`

otherwise.

Mark W. Donoghoe Mark.Donoghoe@mq.edu.au

1 2 3 4 5 |

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