MuMIn (version 1.9.5)

par.avg: Parameter averaging

Description

Average a single model coefficient based on provided weights. It is mostly intended 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.

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.

encoding

utf-8

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).

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.

See Also

model.avg for model averaging.