Add logLik method to enable AIC() on mkinfit models

Further relax two tests to pass build on Travis

2 files changed, 36 insertions, 1 deletions

diff --git a/R/logLik.mkinfit.R b/R/logLik.mkinfit.R
new file mode 100644
index 00000000..c30cc099
--- /dev/null
+++ b/R/logLik.mkinfit.R
@@ -0,0 +1,35 @@
+# Copyright (C) 2018 Johannes Ranke
+# Contact: jranke@uni-bremen.de
+
+# This file is part of the R package mkin
+
+# mkin is free software: you can redistribute it and/or modify it under the
+# terms of the GNU General Public License as published by the Free Software
+# Foundation, either version 3 of the License, or (at your option) any later
+# version.
+
+# This program is distributed in the hope that it will be useful, but WITHOUT
+# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
+# FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more
+# details.
+
+# You should have received a copy of the GNU General Public License along with
+# this program. If not, see <http://www.gnu.org/licenses/>
+logLik.mkinfit <- function(object, ...) {
+  y_ij <- object$data$observed
+  yhat_ij <- object$data$predicted
+  if (is.null(object$data$err)) {
+    err <- sd(object$data$residual)
+    n_var_comp <- 1 # Number of variance components estimated
+  } else {
+    err <- object$data$err
+    if (object$reweight.method == "obs") n_var_comp <- length(object$var_ms_unweighted)
+    else n_var_comp <- 2
+  }
+  prob_dens <- dnorm(y_ij, yhat_ij, err)
+  val <- log(prod(prob_dens))
+  class(val) <- "logLik"
+  attr(val, "df") <- length(coef(object)) + n_var_comp
+  return(val)
+}
+# vim: set ts=2 sw=2 expandtab:
diff --git a/R/mkinfit.R b/R/mkinfit.R
index 8c7549ad..b27f67b4 100644
--- a/R/mkinfit.R
+++ b/R/mkinfit.R
@@ -859,7 +859,7 @@ print.summary.mkinfit <- function(x, digits = max(3, getOption("digits") - 3), .
   invisible(x)

 }

 

-# Fit the mean absolute deviance against the observed values,

+# Fit the median absolute deviation against the observed values,

 # using the current error model for weighting

 .fit_error_model_mad_obs <- function(tmp_res, tc, iteration) {

   mad_agg <- aggregate(tmp_res$res.unweighted,