MuMIn (version 1.3.6)

par.avg: Parameter averaging

Description

Averages single model coefficient based on provided weights

Usage

par.avg(x, se, weight, df = NULL, alpha = 0.05, 
    revised.var = TRUE)

Arguments

x

vector of parameters

se

vector of standard errors

weight

vector of weights

df

(optional) vector of degrees of freedom

alpha

significance level for calculating confidence intervals

revised.var

logical, should the revised formula for standard errors be used? 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 formula in Burnham and Anderson (2002, p. 160, equation 4.7), or a newer, revised formula from Burnham and Anderson (2004, equation 4) (if revised.var = TRUE, this is the default). If degrees of freedom are given, the confidence intervals are based on adjusted standard error estimator (Burnham and Anderson 2002, page 164).

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.