par.avg: Parameter averaging

In MuMIn: Multi-Model Inference

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.

Author(s)

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, 261–304.

See Also

model.avg for model averaging.

MuMIn documentation built on June 22, 2024, 6:44 p.m.