# par.avg: Parameter averaging In MuMIn: Multi-Model Inference

 par.avg R Documentation

## Parameter averaging

### Description

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

### Usage

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

### Arguments

 `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’.

### 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).

### Value

`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

### References

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(2): 261-304.

`model.avg` for model averaging.