This article is written for package authors and maintainers who want
to extend the
tidyfun/tf
ecosystem. End users can usually skip it.
tidyfun is intended to make user
interactions with functional data easier. The package builds on package
tf, which defines a data class
(tf) so that vectors of functional observations are
available, similar to vectors of class numeric or
character1
Such classes make it possible to store functional data alongside
other variables in a single dataframe; by extension, tools for data
manipulation from the tidyverse and
elsewhere can be used with datasets in which one or more variable is
functional.
Many basic analyses are available in
tidyfun – it’s possible to compute mean
functions and other summary statistics of tf vectors, to
expand observations using a spline basis or using functional principal
components analysis. With tools like group_by and
summarize, these can be powerful tools for data
exploration. Other analysis approaches are included in the
refunder package, and more will be added
to that package over time.
There is an active research community in FDA, and there is constant
development of new methods for analysis. Our hope is that the data
structures in tidyfun will be useful as
others as new approaches are implemented, and this page is intended to
provide some advice and guidance for those implementations.
tidyfunThere are start-up costs to using a new data class when writing code
for new methods, and there should be reasons to adopt such a class. We
see several benefits to using tidyfun:
ggplot and base RTogether, these make it possible to analyze functional data in a pipeline (import, exploratory analysis, visualization, and formal analysis) that is similar to those used for scalar variables.
These advantages are user-facing – they are intended to make things
easier when analyzing datasets that include functional observations.
Because new methods for functional data typically involve working with
“raw” observations (numeric vectors or matrices), implementing methods
for tidyfun will require some
consideration of user interfaces, input objects, and data
transformations. We believe the benefits are worth this effort.
tidyfun will be most effective in new
methods that are intended to be part of an analysis pipeline. As a
starting point, we suggest addressing the following questions:
tf
columns?tfd, tfb, or
both? Does the output class matter?plot or predict, which can be used to
summarize results?At best, users will have a seamless experience across data
exploration, modeling, and understanding results;
tidyfun is intended to encourage that.
tidyfun in new functionsWe anticipate that new methods for functional data will use raw
numeric values (in vectors or matrices) for estimation and / or
inference. Tools for converting tf vectors to matrices or
other formats are available, and useful in this context. In general, we
have used the following structure for functions that perform
analyses:
tf vectorstf vectors are converted to matrices (or numeric
vectors)tf vectors as appropriate and
returned to the userThis pipeline shifts the burden for data conversion from the user to the function author, and in doing so maintains seamlessness for the user.
As an example, the refunder::rfr_fpca function for
functional principal components analysis has two main inputs: a
dataframe containing one or more tf variables, and the name
of the variable to decompose using FPCA. Internally, the tf
vector is converted to a matrix for estimation. The function returns a
list of elements relevant for FPCA, and includes estimated functions as
a tfb vector. There is also a predict method,
so that FPCA expansions of new data using the estimated basis can be
easily obtained by users.
If you build on tidyfun, please let us
know by opening an issue or sending
suggestions.
This class and its subclasses and methods live in a
separate package without tidyverse
dependencies in order to keep maintenance simpler and to support users
who prefer not to depend on the
tidyverse.↩︎