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LDATS (development version)

Version numbers follow Semantic Versioning.

LDATS 0.2.7(https://github.com/weecology/ldats/releases/tag/v0.2.7)

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

LDATS 0.2.6(https://github.com/weecology/ldats/releases/tag/v0.2.6)

2020-03-02

Patching a bug in tests for r-devel

  • straingsAsFactors update
  • only involved patching one test

LDATS 0.2.5(https://github.com/weecology/ldats/releases/tag/v0.2.5)

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.

LDATS 0.2.4

2019-07-28

Edits for resubmission to CRAN

  • Added space between braces for auto-linking in the description text.

LDATS 0.2.3

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

  • messageq replaces qprint

LDATS 0.2.2

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

LDATS 0.2.1

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.

LDATS 0.2.0

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-matrix
  • sim_TS_data simulates an TS model's document-topic distribution matrix
  • sim_LDA_TS_data simulates an LDA_TS model's document-term-matrix
  • softmax 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

LDATS 0.1.0

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.

Comparison with Christensen et al. (2018)

  • 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.

LDATS 0.0.1

2017-11-16