V, Z).Legend for plotVariancePartitioning had two entries for Random
when there were no random levels.
predict could fail when there were no random levels.
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.
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 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.
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.
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 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.
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.
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.
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().
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.
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).
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.
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.
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).
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.