Calculate Akaike weights for model averaging

+ + +

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

+ +

aw(object, ...)
+
+# S3 method for mkinfit
+aw(object, ...)
+
+# S3 method for mmkin
+aw(object, ...)

+ +

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

+ +

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
# }
+

+ +

Calculate Akaike weights for model averaging

Arguments

References

Examples

Contents