par.avg | R Documentation |

Average a coefficient with standard errors based on provided weights. This function is intended chiefly for internal use.

```
par.avg(x, se, weight, df = NULL, level = 1 - alpha, alpha = 0.05,
revised.var = TRUE, adjusted = TRUE)
```

`x` |
vector of parameters. |

`se` |
vector of standard errors. |

`weight` |
vector of weights. |

`df` |
optional vector of degrees of freedom. |

`alpha, level` |
significance level for calculating confidence intervals. |

`revised.var` |
logical, should the revised formula for standard errors be used? See ‘Details’. |

`adjusted` |
logical, should the inflated standard errors be calculated? See ‘Details’. |

Unconditional standard errors are square root of the variance estimator,
calculated either according to the original equation in Burnham and Anderson
(2002, equation 4.7),
or a newer, revised formula from Burnham and Anderson (2004, equation 4)
(if `revised.var = TRUE`

, this is the default).
If `adjusted = TRUE`

(the default) and degrees of freedom are given, the
adjusted standard error estimator and confidence intervals with improved
coverage are returned (see Burnham and Anderson 2002, section 4.3.3).

`par.avg`

returns a vector with named elements:

`Coefficient` |
model coefficients |

`SE` |
unconditional standard error |

`Adjusted SE` |
adjusted standard error |

`Lower CI, Upper CI` |
unconditional confidence intervals. |

Kamil Bartoń

Burnham, K. P. and Anderson, D. R. 2002 *Model selection and multimodel
inference: a practical information-theoretic approach*. 2nd ed.

Burnham, K. P. and Anderson, D. R. 2004 Multimodel inference -
understanding AIC and BIC in model selection. *Sociological Methods & Research*
**33**, 261–304.

`model.avg`

for model averaging.

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