MuMIn (version 1.5.0)

par.avg: Parameter averaging

Description

Averages single model coefficient based on provided weights

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 confidence intervals are based on adjusted standard error estimator (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.