Calculate the AIC for a column of an mmkin object

+ + +

Provides a convenient way to compare different kinetic models fitted to the +same dataset.

+ +

# S3 method for mmkin
+AIC(object, ..., k = 2)
+
+# S3 method for mmkin
+BIC(object, ...)

+ +

Arguments

+ + + + + + + + + + + + + + +

object	An object of class `mmkin`, containing only one +column.
...	For compatibility with the generic method
k	As in the generic method

+ +

Value

+ +

As in the generic method (a numeric value for single fits, or a +dataframe if there are several fits in the column).

+ +

Examples


+   # skip, as it takes > 10 s on winbuilder
+  f <- mmkin(c("SFO", "FOMC", "DFOP"),
+    list("FOCUS A" = FOCUS_2006_A,
+         "FOCUS C" = FOCUS_2006_C), cores = 1, quiet = TRUE)
#> Warning: Optimisation did not converge:
+#> false convergence (8)
  # We get a warning because the FOMC model does not converge for the
+  # FOCUS A dataset, as it is well described by SFO
+
+  AIC(f["SFO", "FOCUS A"]) # We get a single number for a single fit
#> [1] 55.28197
  AIC(f[["SFO", "FOCUS A"]]) # or when extracting an mkinfit object
#> [1] 55.28197

+  # For FOCUS A, the models fit almost equally well, so the higher the number
+  # of parameters, the higher (worse) the AIC
+  AIC(f[, "FOCUS A"])
#>      df      AIC
+#> SFO   3 55.28197
+#> FOMC  4 57.28202
+#> DFOP  5 59.28197
  AIC(f[, "FOCUS A"], k = 0) # If we do not penalize additional parameters, we get nearly the same
#>      df      AIC
+#> SFO   3 49.28197
+#> FOMC  4 49.28202
+#> DFOP  5 49.28197
  BIC(f[, "FOCUS A"])        # Comparing the BIC gives a very similar picture
#>      df      BIC
+#> SFO   3 55.52030
+#> FOMC  4 57.59979
+#> DFOP  5 59.67918

+  # For FOCUS C, the more complex models fit better
+  AIC(f[, "FOCUS C"])
#>      df      AIC
+#> SFO   3 59.29336
+#> FOMC  4 44.68652
+#> DFOP  5 29.02372
  BIC(f[, "FOCUS C"])
#>      df      BIC
+#> SFO   3 59.88504
+#> FOMC  4 45.47542
+#> DFOP  5 30.00984
  
+
+

+ +