Calculate Akaike weights for model averaging

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Akaike weights are calculated based on the relative -expected Kullback-Leibler information as specified -by Burnham and Anderson (2004).

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aw(object, ...)
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-# S3 method for mkinfit
-aw(object, ...)
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-# S3 method for mmkin
-aw(object, ...)
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-# S3 method for mixed.mmkin
-aw(object, ...)
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-# S3 method for multistart
-aw(object, ...)

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Arguments

object: An mmkin column object, containing two or more -mkinfit models that have been fitted to the same data, -or an mkinfit object. In the latter case, further mkinfit -objects fitted to the same data should be specified -as dots arguments.
...: Not used in the method for mmkin column objects, -further mkinfit objects in the method for mkinfit objects.

References

Burnham KP and Anderson DR (2004) Multimodel -Inference: Understanding AIC and BIC in Model Selection. -Sociological Methods & Research 33(2) 261-304

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Examples

# \dontrun{
-f_sfo <- mkinfit("SFO", FOCUS_2006_D, quiet = TRUE)
-f_dfop <- mkinfit("DFOP", FOCUS_2006_D, quiet = TRUE)
-aw_sfo_dfop <- aw(f_sfo, f_dfop)
-sum(aw_sfo_dfop)
-#> [1] 1
-aw_sfo_dfop # SFO gets more weight as it has less parameters and a similar fit
-#> [1] 0.5970258 0.4029742
-f <- mmkin(c("SFO", "FOMC", "DFOP"), list("FOCUS D" = FOCUS_2006_D), cores = 1, quiet = TRUE)
-aw(f)
-#> [1] 0.4808722 0.1945539 0.3245740
-sum(aw(f))
-#> [1] 1
-aw(f[c("SFO", "DFOP")])
-#> [1] 0.5970258 0.4029742
-# }
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