Bug fix: It was possible to get CICc values in the summary output that were not valid. Specifically, to calculate CICc there is a division by (n - 1 - q)
, where n
is the number of observations (species) and q
the number of parameters in the causal model. This could lead to infinite CICc when n == q + 1
, or a flipped of CICc when n < q + 1
. This would typically only occur when attempting to fit models with very species (e.g. < 10).
New behavior is to set CICc to NA
when n
is insufficient, and to give a warning.
Removed dependencies dplyr
and tidyr
, but added tibble
.
Fixed bug that would return the wrong model in some error messages.
Improved reporting of warnings, and a show_warnings()
function has been added.
Citation info now points to the PeerJ paper.
Citation info now points to the bioRxiv paper.
All modeling functions now completely rely on the phylolm
package, and no longer use ape
. This is a major change, that will possibly change the outcomes of some of your existing analyses (as can happen when chaning the modeling package). There are, however, several good reasons to make this change, which I think make it worth the trouble. Firstly, the package is much faster for large trees, and this effect is compounded in phylopath
because one may have to fit a few dozen models. Secondly, I think it is important to have confidence intervals around the regression coefficients, and those were not available for ape::binaryPGLMM
. Thirdly, phylolm
makes it easy to use a larger variety of models of evolution, including two versions of OU and early burst, which can be simply set using the model
parameter. Lastly, the phylolm()
and phyloglm()
functions give more uniform results, which makes it easier to code for situation where you may use both.
phylo_path
and all related methods now deal automatically with both continuous and binary data. All separate binary functions and methods have disappeared as they are no longer needed. Mixing of binary and continious data in the same models is now allowed.
The variable order in d-seperation statements now better follows the causal flow of the DAG.
Added plot()
method for phylopath.summary
objects, that shows the weights and p-values for the different models.
coef_plot()
gained error_bar
, order_by
, from
and to
arguments. The first allows the user to choose between confidence invervals and standard errors, the second to order the paths by several methods, and the last two can be used to select only certain paths.
Plotting methods of causal models now support a manual layout.
Plotting of fitted DAG’s now uses edge width instead of color to indicate, the standardized regression coefficient strength, but this can be reverted using the type
argument.
Added a define_model_set()
convenience function for building models, that avoids repeated calls to DAG()
and has an argument to supply paths that are shared between all your models. It is not needed to specify isolate variables. Old code using DAG()
continues to work as normal.
Added support for additional arguments passed to gls
from phylo_path
. This can be helpful, for example, for setting the fitting method to maximum likelihood (method = "ML"
).
####Bugfixes:
The package broke due to an update of purrr
, but has now been fixed (reported by Christoph Liedtke, @hcliedtke).
The package depends on a recent version of nlme
, but this wasn’t specified. All package versions of dependencies are now defined (reported by @ManuelaGonzalez).
Added support for completely binary models, that are fitted with ape::binaryPGLMM
. Use phylo_path_binary()
to compare models. average()
, best()
and choice()
are now S3 generics and will handle both continuous and binary versions. Usage is designed to be as close to the continuous version as possible. est_DAG_binary()
powers the binary S3 methods.
All plot functions that used DiagrammeR
now use ggraph
instead. This gives much more control over the positioning of the nodes, and allows to plot multiple models at once. Exporting plots also becomes much easier.
You can now plot a list of causal models with plot_model_set()
. This creates a faceted plot where all nodes are kept in the same location, which makes it easier to spot how models are different.
If there are any NA
values in data
for the variables in models
, these rows are now dropped from data
with a message. Use na.rm = FALSE
to revert to the old behavior.
When PGLS models fail, an informative error is now returned to the user.
phylo_path()
now checks for row.names that line up with the tree tip labels. If the tree contains surplus species, it gets pruned to size with a message. This includes cases where species are dropped due to missing values.
citation()
now correctly refers to the methods paper by Von Hardenberg & Gonzalez-Voyer first and the package second.
Fewer models are now fitted when using phylo_path()
, since any duplicated independence statements are now only fitted once. This leads to a significant reduction in running time in many cases, especially when many models are considered.
Implemented support for parallel processing in phylo_path()
using the parallel
argument.
phylo_path()
now shows a progress bar.
New function added (choice()
) that is a very simple wrapper around est_DAG()
. It adds to best()
and average()
by allowing for choosing any model as the final model, and encourages users to not always pick the lowest CICc model.
Prepared plotting functions for new release of DiagrammeR
, v0.9 now required.
IMPORTANT: Faulty model averaging has been fixed. This was often introduced due to differences in matrix ordering. Averaging results from versions before 0.2.1 should NOT be trusted.
Using ape::corBrownian()
no longer returns an error.
Averaging is less likely to fail due to errors in nlme::intervals()
.
phylo_path()
has become more streamlined with functionality moved to other functions. The phylopath
object now contains all necessary models and data, summary()
is used to obtain the results table, and best()
and average()
are used to extract and fit the best or average model. See the vignette for details.
Model averaging for arbitrary models is now possible with average_DAGs()
.
Model averaging now supports both conditional and full model averaging.
Both the old est_DAG()
and the new average_DAGs()
now return objects of a new class fitted_DAG
, that has it’s separate plot
method. The plot
method for objects of class DAG
has been simplified.
Model averaging now returns standard errors and confidence intervals based on the MuMIn
package (issue #1).
A new function plot_coefs
for plotting regression coefficients and their confidence intervals has been added.