Calculate the AIC for a column of an mmkin object

@@ -149,102 +88,103 @@ same dataset." /> 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

# 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).

Author

- +

Author

Johannes Ranke

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
-
-  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 57.28222
-#> 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

-  # 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
  
-
-

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
+
+  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 57.28222
+#> 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
+
+  # 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
+  
+
+