Remove zero observations, adapt logLik

Zero observations at time zero made fitting the two-component error
model fail. A concentration of exactly zero does not make sense anyways,
as we generally have a limit of detection

1 files changed, 7 insertions, 0 deletions

diff --git a/R/mkinfit.R b/R/mkinfit.R
index 73c2f485..6c12d027 100644
--- a/R/mkinfit.R
+++ b/R/mkinfit.R
@@ -64,6 +64,12 @@ mkinfit <- function(mkinmod, observed,
   observed <- subset(observed, name %in% obs_vars)

   observed <- subset(observed, !is.na(value))

 

+  # Also remove zero values to avoid instabilities (e.g. of the 'tc' error model)

+  if (any(observed$value == 0)) {

+    warning("Observations with value of zero were removed from the data")

+    observed <- subset(observed, value != 0)

+  }

+

   # Obtain data for decline from maximum mean value if requested

   if (from_max_mean) {

     # This is only used for simple decline models

@@ -405,6 +411,7 @@ mkinfit <- function(mkinmod, observed,
   } else {

     if(!quiet) cat("Optimisation successfully terminated.\n")

   }

+  fit$logLik <- - nlogLik.current

 

   # We need to return some more data for summary and plotting

   fit$solution_type <- solution_type