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LDATS (development version)
Version numbers follow Semantic
Versioning.
LDATS 0.3.0
2023-09-16
Patching CRAN
issues with vignette building
- The paper comparison vignette is now pre-computed to avoid needing
to access internet resources during cran build (see
https://ropensci.org/blog/2019/12/08/precompute-vignettes/).
Converting from
travis ci to github actions
- Travis CI no longer works, so shifting to github actions for
builds
2020-03-18
Patching CRAN
issues with vignette building
- Dependencies are being managed different now
- For the paper comparison vignette, all of the code is pre-run and
saved in the LDATS-replications repository
- Allows removal of otherwise unused packages from this package’s
dependency list
2020-03-02
Patching a bug in tests for
r-devel
straingsAsFactors update- only involved patching one test
2019-12-22
General editing of
simulation functions
- Don’t need to make a sparse matrix to pass in now
- Tweaking the simulation functions to simplify X
Patching a bug in
sim_TS
- Using only as.matrix() fails if there is only 1 year in a segment
and there are multiple covariates. In that case, as.matrix(X[in1, ])
returns a matrix of n_covariates rows x 1 column, instead of a matrix of
1 row and n_covariates columns. This edit should fix that by forcing it
into a matrix of the correct number of rows.
2019-07-28
Edits for resubmission to
CRAN
- Added space between braces for auto-linking in the description
text.
2019-07-24
Edits for resubmission to
CRAN
- Output given by
print/cat has been
replaced with message messages.
- Added examples in documentation (and replacement of
duntrun{} with donttest{})
- Editing of description file for specs
- Reduction of test runtimes
Changed functions
2019-07-10
Edits for submission to CRAN
- Including appropriate files in the Rbuildignore
Minor patching of vignette
code
- Handling the downloads so they work robustly locally
2019-07-09
Vignette update
- Incorporates Hao’s feedback and edits on the paper comparison
vignette
- Updates the vignette to work with the contemporary version of the
package
- Allowed removal of the large model cache files
Zenodo json
- Inclusion of the json file for the Zenodo page
Tidying of the model doc
- The .pdf describing the model (the manuscript work in progress) is
now at the top level and named “LDATS_model.pdf”, to allow the full
model description to remain stable while the ms development happens
elsewhere.
2019-07-09
API updates
- At the
LDA_TS function level, the separate inputs for
data tables (document_term_table and
document_covariate_table) have been merged into a single
input data, which can be just the
document_term_table or a list including the
document_term_table and optionally also a
document_covariate_table. If covariates aren’t provided,
the function now constructs a covariate table assuming equi-spaced
observations. If using a list, the function assumes that one and only
one element of the list will have a name containing the letters “term”,
and at most one element containing the letters “covariate” (regular
expressions are used for matching). (addresses issue
119)
timename has been moved from within the
TS_controls_list to a main argument in all associated
functions.- The control lists have been made easier to interact with. Primarily,
the arguments that previously required
LDA_controls_list,
TS_controls_list, or LDA_TS_controls_list
inputs now take general list inputs (so LDA_TS
does not need to have a nested set of control functions). Each control
list is passed through a function (LDA_set_control,
TS_control, or LDA_TS_control) to set any
non-input values to their defaults. This also allows the removal of
those controls list class definitions. (addresses issue
130)
Fixed
and updated example code to improve user experience
Updated
calculation of the number of observations in LDA
- The number of observations for a VEM-fit LDA is now calculated as
the number of entries in the document-term matrix (following Hoffman et
al. and Buntine, see
?logLik.LDA_VEM for references.
- Associated, we now include an AICc function that is general and
works in this specific case as defined (addresses issue
129)
Fixed bug in
plotting across multiple outputs
- A few plotting functions use
devAskNewPage to help flip
through multiple outputs, but were only resetting it with
devAskNewPage(FALSE) at the end of a clean execution. The
code has been updated with on.exit(devAskNewPage(FALSE)),
which accounts for failed executions. (addresses issue
118)
Renamed functions
summarize_TS has been renamed package_TS
to align with the other package_ functions that package
model output.
Simulate functions
- Basic simulation functionality has been added for help with
generating data sets to analyze. (addresses issue
114)
sim_LDA_data simulates an LDA model’s
document-term-matrixsim_TS_data simulates an TS model’s document-topic
distribution matrixsim_LDA_TS_data simulates an LDA_TS model’s
document-term-matrixsoftmax and logsumexp are added as utility
functions
Improved pkgdown site
Editing of output from
TS
- Due to a misread of earlier code, the AIC value in the output from
TS was named “deviance”. The output has been updated to
return the AIC.
Replacement
of AIC method with logLik method for
TS_fit
2019-02-11
Code structure
- Creation of a standard
API and code pipeline for all components of the LDATS analysis.
- Substantial refactor of the underlying code from hardcoded to
generalized functions.
- Development of checking functions used to run the basic structural
checks on the function inputs.
- Inclusion of control options lists for the LDA stage, TS stage, and
overall to reduce the length of input lists.
Full inclusion of functions
- All functions used in the code base are now exported, documented,
and tested.
LDA model AIC calculation
AIC.LDA_VEM() now uses the number of parameters as
reported from logLik to calculate AIC.- Previous by-hand calculations of AIC included variational parameters
that are integrated out of the model in the total parameter count.
Regressor estimates
- Time series models allow for flexible covariate set for regression
via formula inputs to the top-level functions.
- The time series model code now also includes estimation of the
parameters defining the between-change point regressions (i.e.,
the regressors).
- Regressor estimates come as marginal posterior distributions, and
are calculated by unconditioning the estimates generated under known
change points.
Document weighting
document_weights() function is provided to allow for
appropriate weighting of documents by their sizes (number of words) so
that an average-length document is 1.- Document weighting is done automatically by default, which is easily
undone by using
weights = NULL.
ptMCMC functionality
- The ptMCMC code has been refactored into functions, many of which
are generalized to use in other contexts.
- The temperature schema is fully controllable via arguments to the TS
controls list
- Burn-in removal and thinning of final chains is controllable via the
TS controls list
Optional memoisation
- Memoisation of
multinom_TS() and
multinom_TS_chunk() now is optional via
memoise_fun() and is controlled through the TS controls
list.
Plotting functions
LDA_set(), LDA_TS(), and TS()
now all have default plotting options on their outputs.plot.TS() provides MCMC diagnostic plots and summary
plots.plot.LDA_TS() plots produce the combination of
plots.
Rodents data set
- Portal rodent data from Christensen et
al. (2018) are now provided in a pre-formatted and
ready-to-roll data object.
- Access the data using
data(rodents).
- Note, however, that the data in Christensen et al. 2018 are
scaled according to trapping effort. The data included in LDATS are not,
to allow for appropriate weighting. See comparison
vignette for further details.
The comparison
vignette provides a step-by-step comparison of the LDATS pipeline to
the analysis in Christensen et al. 2018.
The key differences are as follows:
* The `document_term_table` in Christensen *et al.* 2018 was adjusted to account for variable trapping effort. The data included in LDATS are not adjusted, so that sampling periods can be weighted appropriately.
* The LDA model selection criterion has changed (see LDA model AIC calculation, above), so that LDATS now identifies 6 topics compared to the 4 topics found in the paper.
* LDATS will by default weight sampling periods according to the number of terms (see Document weighting, above).
* Despite these changes, the updated LDATS pipeline gives qualitatively similar results to the analysis in Christensen *et al.* 2018.
2017-11-16
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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