Calculate the AIC for a column of an mmkin object

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

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

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Arguments

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

Value

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

Author

Johannes Ranke

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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)
-  # We get a warning because the FOMC model does not converge for the
-  # FOCUS A dataset, as it is well described by SFO
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-  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
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-  # 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 57.28222
-#> 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 49.28222
-#> DFOP  5 49.28197
-  BIC(f[, "FOCUS A"])        # Comparing the BIC gives a very similar picture
-#>      df      BIC
-#> SFO   3 55.52030
-#> FOMC  4 57.59999
-#> DFOP  5 59.67918
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-  # 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
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