- Small fixes that arose from api changes in package ess
The package has undergone a major make-over. A slight, but breakable, change in the api of fit_outlier. The documentation of fit_outlier has been updated and now includes more and better examples of how and when to use the function. The fit_graph function is no longer a part of molic. It now lives in its own package at ess and molic is now dependend on ess. It is therefore now required to run include(ess) to have access to fit_graph.
The readme file has also undergone a major change - the former example using cars data has been removed; it was never really a good example showing how to do outlier detection with molic.
- A new data set,
dermahas been included and a new vignette using this data has been added. - The
tgp_datdata has now been compressed to save disk space. - The
plot.gengraphfunction applied to an object (gengraph) returned from one of the graph fitting functions (fit_graph,fit_componentsetc.) now takes an input that let the user specify the color of the nodes.
subgraphfunction is now provided.sapply's are now converted tovapply's for safety and potentially more speed when fitting graphs.
pmfno longer plots the density of the deviances of aoutlier_modelobject. Useplotfor this instead; this is now consistent with the other related functions likefit_outlier. Insteadpmfis used to construct the probability mass function of a decomposable graphical model which can be used to obtain probabilities of observing specific cells/observations/configurations.
Development Model
From this release we adopt the branching model introduced by Vincent Driessen
This means, that there are now two branches: the master branch is always the current stable version, and the develop branch is the develop version.
New API
- Functions like
fit_outlierthat depends on an adjacency list no acceptgengraphobjects returned fromfit_graph- i.e. no need to useadj_lst()first.
New functions
-
generate_multiple_models- Given a class variable with
$1,2\ldots, l$ levels and a new observation$y$ , this function is a convenient wrapper aroundfit_graphandfit_outlierthat conducts all the hypothesis$H_k:$ $y$ has level$k$ for$k = 1,2,\ldots, l$ .
- Given a class variable with
-
plot.multiple_models- Given an object returned from
fit_multiple_modelsthis function is used to visualize all the hypothesis tests for a single observation simultaneously. It is aggplot2object
- Given an object returned from
-
plot.outlier- Given an object returned from
fit_outlierthis function is used to visualize the approximated density of the deviance under the null hypothesis. It is aggplot2object.
- Given an object returned from
-
components- Return a list with all components of a graph
fit_components
Misc
- All deviances are now non-negative as they should be! Before, a constant was neglected which could potentially confuse the users since a deviance is per definition non-negative.
- First release.