Average a coefficient with standard errors based on provided weights. This function is intended chiefly for internal use.
par.avg(x, se, weight, df = NULL, level = 1 - alpha, alpha = 0.05,
revised.var = TRUE, adjusted = TRUE)vector of parameters.
vector of standard errors.
vector of weights.
optional vector of degrees of freedom.
significance level for calculating confidence intervals.
logical, should the revised formula for standard errors be used? See ‘Details’.
logical, should the inflated standard errors be calculated? See ‘Details’.
par.avg returns a vector with named elements:
model coefficients
unconditional standard error
adjusted standard error
unconditional confidence intervals.
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).
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.