From d566ba840f4351a3aeebf21aae0649caebe0e009 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Fri, 23 Nov 2018 19:52:18 +0100 Subject: Add logLik method to enable AIC() on mkinfit models Further relax two tests to pass build on Travis --- R/logLik.mkinfit.R | 35 +++++++++++++++++++++++++++++++++++ R/mkinfit.R | 2 +- 2 files changed, 36 insertions(+), 1 deletion(-) create mode 100644 R/logLik.mkinfit.R (limited to 'R') 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 +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, -- cgit v1.2.1