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-rw-r--r--R/AIC.mmkin.R8
1 files changed, 7 insertions, 1 deletions
diff --git a/R/AIC.mmkin.R b/R/AIC.mmkin.R
index 7d405c4a..f1a66998 100644
--- a/R/AIC.mmkin.R
+++ b/R/AIC.mmkin.R
@@ -17,15 +17,21 @@
#' f <- mmkin(c("SFO", "FOMC", "DFOP"),
#' list("FOCUS A" = FOCUS_2006_A,
#' "FOCUS C" = FOCUS_2006_C), cores = 1, quiet = TRUE)
-#' AIC(f[1, "FOCUS A"]) # We get a single number for a single fit
+#' # 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
+#' AIC(f[["SFO", "FOCUS A"]]) # or when extracting an mkinfit object
#'
#' # 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"])
#' AIC(f[, "FOCUS A"], k = 0) # If we do not penalize additional parameters, we get nearly the same
+#' BIC(f[, "FOCUS A"]) # Comparing the BIC gives a very similar picture
#'
#' # For FOCUS C, the more complex models fit better
#' AIC(f[, "FOCUS C"])
+#' BIC(f[, "FOCUS C"])
#' }
#'
#' @export

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