inst/NEWS.md

In hmsc-r/HMSC: Hierarchical Model of Species Communities

Version 3.0-14 (development version in github)

New Features

• Failed updaters are only recorded during sampling. Failed updates can be detected in more cases (V, Z).

Bug Fixes

• Legend for plotVariancePartitioning had two entries for Random when there were no random levels.

• predict could fail when there were no random levels.

Version 3.0-13

New Features

• Posterior samples from several independent Hmsc objects can be combined as new chains with new method function c(). This provides an easy alternative for distributed computing. The user should take care that these independent models are defined similarly so that they really can be combined. The function tests for similarity of objects, but it only gives warnings and can allow combination of incompatible models at user will. The user should be careful not to start these models from the same random number seed as these just duplicate your data instead of adding independent new samples.

• sampleMcmc allows the use of fork clusters instead of socket clusters. Socket clusters are still the only alternative in Windows, but other platforms can profit from the use of fork clusters which may have lower memory use and are faster to set up, and also may be marginally faster. The choice can be made with new argument useSocket (defaults TRUE).

• Updaters in sampleMcmc can occasionally fail in extreme Hmsc models. This is no longer an error that stops analysis, but sampling tries to recover from failures. The numbers of failures for each updater is reported with the result. If there are only a low number of failures, the sampling is safe to use. If there are updater errors only in some chains, these chains can be removed, but other chains can be used. See issue #123.

• New experimental function pcomputePredictedValues with more aggressive parallelization than computePredictedValues. In old code chains within each partition could be run in parallel, but partitions were run serially. In the new function, all chains and partitions can be run in parallel. The plan is to replace the old function with this new alternative, but at the moment both functions are available for testing. See issue #142.

• Implemented longitude-latitude coordinates and user-supplied distance matrices for NNGP spatial models. Sanity checks for spatial model input were improved.

• Improved support for spatial models defined via distance matrices instead of spatial coordinates.

• constructGradient provides wider choice of coordinates for centroid of new_unit, including user-set and infinite (meaning no spatial dependence) coordinates.

• Detect cases when user tries to analyse posterior samples of non-sampled Hmsc object to avoid confusing error messages such as reported in issue #125.

Bug Fixes

• sampleMcmc with initPar = "fixed effects" failed if Y variates had missing values. The choice "fixed effects" was undocumented in the package, but was used in several scripts at large. See issue #101.

• The default number of neighbours in NNGP spatial models was not known in all posterior analysis tools giving very obscure error messages. Reported by Ben Weigel (Uni Helsinki).

• Covariate-dependent latent loadings did not have correct alignment.

• predict did not honour setting start and thin which could result in huge output data that exhausted memory. See issue #86.

• predict failed in NNGP spatial models. See issue #96.

• predict failed with one-dimensional spatial data. See comments in unrelated issue #61.

• Missing values are handled better in predict, but they are still not allowed in all cases.

• prepareGradient failed with geo-referenced spatial random levels.

• More robust handling of models that were fitted with model matrix X instead of model frame XData and model formula XFormula. Concerns functions biPlot, computeVariancePartitioning and constructGradient. Fixes issue #126.

• biPlot has improved handling of colour scaling of continuous variables.

Version 3.0-11

Installation

• New package dependencies: matrixStats and pracma.

New Features

• plotGradient gained argument to show the support of trend for continuous variables. Main title can be shown for factor variables (earlier it was shown only for continuous variables).

• Grids of knots for Gaussian Predictive Process (GPP) are centred for the coordinates in constructKnots. More knots were produced than requested.

Bug Fixes

• Prediction failed in spatial models with predictEtaMean = TRUE.

• Prediction failed in spatial NNGP models.

• constructGradient (and hence plotGradient) ignored specified order of factor levels. See github issue #63.

• Performance inefficiency issues were fixed in NNGP models and some updaters.

User Interface

• User interface is more robust and can handle several inputs that earlier caused errors (often with confusing and obscure error messages). Input data is checked more carefully to avoid misleading results because of wrongly interpreted data.

• User interface changes fix several github issues: #65, #66, #68, #70, #71, #78, #80, #81, #82.

• Spatial and phylogenetic data are inspected more carefully to avoid errors in sampling.

• Updaters are automatically disabled when needed instead of producing an error.

Version 3.0-9

Installation

• Hmsc is no longer dependent on packages mvtnorm and pdist.

• Hmsc is now dependent on the sp package.

• Vignettes can be re-built from their sources out of the box. Previously they needed editing by hand to reproduce the pdf version.

Spatial Data

• It is now possible to use Spatial data in random models. Handling of Spatial data is based on the sp package and follows its conventions. The locations of sampling units can be given as a decimal longitude-latitude matrix, and the Hmsc functions will use great circle distances in spatial models. Projected spatial coordinates will be handled as such and Euclidean distances will be used internally.

• User-specified spatial distances can be more widely used in spatial random models. However, some models are more flexible with spatial coordinates. Most importantly, Gaussian Predictive Process (GPP) needs spatial coordinate data.

New Features

• Species data Y is normally a numeric matrix, but now it is allowed to use numeric data frames, or in univariate models, a numeric vector.

• A tibble can be used for measured covariates for fixed effects XData in addition to a data frame (the wish of Github issue #37).

• The names of distributions can be abbreviated in Hmsc definition as long as the names are unique.

• computeWAIC is more robust against results of poorly fitting models, and it is now possible to evaluate WAIC separately for each species. See GitHub issue #44.

• constructGradient argument nonFocalVariables accepts now a single number 1 or 2 as a shortcut of default type for all non-focal variables instead of requesting a list of types of all variables.

• plotGradient gained new argument yshow which is a single number or vector of numeric values that must be included on the y-axis. In general, the y-axis is scaled to show the plotted values, but yshow = 0 will always show zero, even when this is not among plotted values, and yshow = c(0,1) will show both zero and one.

• plotVariancePartition defaults to plot the original terms instead of single contrast. For instance, only one component is shown for multilevel factors instead of showing each level separately. User can still specify how the components are displayed.

• plot functions plotBeta, plotGamma and plotVariancePartitioning allow setting or modifying the plot main title. plotGradient already allowed this.

• Random seed is now saved in sampleMcmc models. This allows replication of same random number sequences. However, there is no guarantee of replication across Hmsc release versions or computing platforms.

• HmscRandomLevel saves the function call. The call can be inspected with getCall() and the model can be modified with update().

Bug Fixes

• constructGradient could sometimes shuffle spatial locations leading into wrong predictions with spatial models.

• plotGradient(..., showData = TRUE) ignored data values in setting plot minimum. See GitHub issue #48. The data values were not always shown with measure = "S" in quantitative linear models.

Version 3.0-6

Installation and Versions

• R release 4.0 will drop the convention to automatically change character variables to factors, and this causes errors in internal working of several Hmsc functions. This version of Hmsc is released principally to accomodate these changes in R. Hmsc will also work in previous versions of R.

• Hmsc 3.0-5 was never released to CRAN. It is a snapshot that corresponds to the on-line publication of Tikhonov et al. (2020) Joint species distribution modelling with the R-package Hmsc. Methods in Ecology and Evolution 11, 442--447. (https://doi.org/10.1111/2041-210X.13345).

Models

• Shape and rate parameters (aSigma, bSigma) for the prior Gamma distribution for the variance parameter (sigma) changed. The change will influence models with "normal" and "lognormal poisson" distributions. In particular, "lognormal poisson" will more easily tend toward zero sigma if there is no overdispersion to "poisson". However, in such cases it may be wiser to refit models with pure "poisson" distribution. You can changes these parameters with setPriors function.

• Cross-validation works also when the test data set has some spatial units that were unseen in the training data.

Bug Fixes

• When calling sampleMcmc with fromPrior = TRUE, the residual variance parameter sigma used Gamma rather than inverse of Gamma distribution. The same error was present when sampling the initial values for the MCMC algorithm. However, the actual MCMC algorithm (and thus the posterior distribution) was correct.

• Predictions with spatial NNGP models failed if there was only one unit. Github issue #40.

• Reduced-Rank Regression also works for single-species models, and more robust scaling is used for species-specific covariate matrices.

• Spatial models with Gaussian Predictive Process now also works when the number of spatial locations is less than the number of sampling units.

• Predictions with spatial NNGP and GPP models gave bad estimates.

Documentation

• New vignette (#5) on Hmsc performance.

Version 3.0-4

Installation

• Hmsc no longer depends on phytools package, and external ImageMagick software is no longer needed. See discussion in github issue #34 and pull request #36.

Bug Fixes

• Several functions failed in the development version of R (to be released as R version 4). The failures were caused by changes in R internals.

• Fixed bug with delta for alignPosterior which influences sampleMcmc. See github issue #27.

• plotBeta failed with argument plotTree = FALSE together with SpeciesOrder = "Tree".

• Spatial models with Nearest Neighbour Gaussian Process (NNGP) failed when the number spatial locations was not equal to the number of sampling units. This could happen, for instance, if there are multiple observations on the same spatial location. The problem still persists in spatial models with Gaussian Predictive Process (GPP).

New Features

• Hmsc models can be modified using update(<Hmsc model>, <new arguments>). This was achieved by adding a call component per wish in github issue #34.

• evaluateModelFit can handle probit models where binary data were given as TRUE/FALSE. Earlier only numeric data (0/1) were accepted. See github issue #30.

• biPlot uses equal aspect ratio in ordination biplots.

Documentation

• Added section on priors in vignette on high-dimensional multivariate models.

Version 3.0-2

• This is the first CRAN release. For previous development, see https://github.com/hmsc-r/HMSC/.

hmsc-r/HMSC documentation built on March 5, 2025, 10:52 p.m.