svcTPGOcc models that required updating v0.7.3 on CRAN to pass valgrind checks, as well as a memory leak in the calculation of the nearest neighbors, and a small problem with the DESCRIPTION file for including on CRAN.n.batch, batch.length, n.samples, n.burn, n.thin, n.chains) are specified in a way that leads to a non-integer value. In such situations, models would previously run and return without an error, but sometimes the last posterior sample in any given chain could have widely inaccurate values, or values that prevented subsequent functions from working. Thanks to Wendy Leuenberger and Colin Swider for bringing this to my attention.data list supplied to model fitting functions called grid.index. This is useful for data sets where there is some sort of nested structuring among the data collection protocol, such that you may wish to specify the spatial random effects at a lower resolution than each individual location. Further, it can be particularly useful for SVC models where you only want to specify nonstationarity at a lower spatial resolution (e.g., across a set of grid cells). This is currently implemented for the following functions: spPGOcc, sfMsPGOcc, stMsPGOcc, stPGOcc, svcPGOcc, svcTMsPGOcc, svcTPGOcc. See the documentation for a given model function for how to specify this. I am hoping to eventually write up a small example that shows how to do this, but for now documentation is fairly limited to just the manual pages for each function. Feel free to contact me if you want to use this functionality and have any questions.updateMCMC() function. This function is in active development, but it will ultimately allow for all spOccupancy and spAbundance model objects to be updated with additional MCMC samples, instead of having to completely rerun an MCMC analysis if adequate burn-in/convergence was not reached. It currently works for the function sfJSDM() in spOccupancy and msAbund() in spAbundance.svcTMsPGOcc and sfJSDM. This is done by setting the tags independent.betas and independent.alphas to TRUE. This will fix the values of the community-level mean and variance parameters to the initial values specified in inits. This is equivalent to setting an independent Gaussian prior for each of the species-specific regression coefficients, which may potentially be useful in certian situations where the assumption of normality in the distribution of the species-level effects is not well met. This functionality will eventually be incorporated for all multi-species models.intMsPGOcc() that caused the model to crash upon initialization of the MCMC algorithm when data were supplied in a way such that for a given data set, the maximum number of times a specific site was sampled was less than the total number of “replicate periods” (i.e., the third dimension of the data list). This may happen when the “replicates” are structured as specific time periods (i.e., weeks, years) instead of a specific “replicate”. This was previously fixed in all other model fitting functions.k.fold.only = TRUE. Thanks to Zack Steel for pointing this out.plot() function to generate simple traceplots using spOccupancy model objects. Details can be found in the help page (e.g., for spPGOcc models, type ?plot.spPGOcc in the console).spOccupancy. Thanks to Sara Stoudt for writing this!lfJSDM() without residual species correlations by setting n.factors = 0. This is a model analogous to msPGOcc(), but without the detection component.shared.spatial argument to sfJSDM(). If set to TRUE, this argument estimates a common spatial process for all species instead of using the default spatial factor modeling approach.predict.svcTMsPGOcc() when same variable was used for a fixed and random effect (e.g., if including a linear year trend and also an unstructured random intercept for year). Thanks to Liam Kendall for pointing this out.spOccupancy v0.7.0 contains a variety of substantial updates, most notably functionality for fitting non-spatial and spatial multi-species multi-season occupancy models, as well as multi-species spatially-varying coefficient models. There are also a variety of smaller bug fixes/additional error handling that will help eliminate some common hard-to-interpret errors that users encountered.
tMsPGOcc() fits non-spatial, multi-season, multi-species occupancy models, and the function stMsPGOcc() fits spatial, multi-season occupancy models. The spatially-explicit function also inherently accounts for species correlations with a spatial factor modeling approach (e.g., they are joint species distribution models with imperfect detection and species correlations). See Doser et al. 2023 for statistical details on the spatial factor modeling approach. A vignette will be posted that details fitting these models in depth in the coming months, but the syntax is essentially a combination of multi-species models (e.g. msPGOcc(), sfMsPGOcc()) and multi-season single-species models (i.e., tPGOcc() and stPGOcc()), so the recommendations provided in the vignettes for those models is applicable for these models as well.svcMsPGOcc()) and multi-season (svcTMsPGOcc()) models. These approaches use a spatial factor modeling approach for each of the SVCs to make the models relatively computationally efficient. The functions inherently account for species correlations. The vignette on spatially-varying coefficients provides an example for svcMsPGOcc(), with an example for svcTMsPGOcc() coming soon.simTMsOcc() simulates multi-season, multi-species occupancy models.getSVCSamples() to now work with multi-species spatially-varying coefficient models.data$coords object were unique, as this is a requirement for spOccupancy spatially-explicit models. In previous versions, this resulted in an error of c++ error: dpotrf failed, or something along those lines, which was a common source of confusion.n.burn argument was greater than 0 and/or n.thin was greater than 1. Thanks to Alex Bacjz for bringing this to my attention.by.sp argument to waicOcc() to allow users to calculate WAIC separately for individual species in all multi-species model types in spOccupancy.intMsPGOcc(). This fits a single-season version of the “intgrated community occupancy model” from Doser et al. 2022. The function intMsPGOcc() should be considered experimental and is still under development. We have done adequate testing of the function and users can be confident the resulting estimates are correct. Rather, we consider this “experimental” because it lacks all the functionality currently supported for other spOccupancy model types. In particular, intMsPGOcc model objects do not currently work with ppcOcc() (posterior predictive checks), fitted() (generated fitted values), or k-fold cross-validation, and there may be specific data set situations that cause the function to break. Please contact us if you use the function and have any feedback or run into any problems. We are in active development of the associated spatial versions of the function (both without spatial factors and with spatial factors), as well as the above mentioned limitations. intMsPGOcc() does not currently support random effects in the detection models, which we are actively working on. See vignette("integratedMultispecies") for more details.postHocLM(). The function postHocLM() fits a basic linear (mixed) model to a response variable that is assumed to come from a previous model fit, and thus each value in the data response variable has a full set of posterior MCMC samples associated with it. While this function can be used for a variety of situations (including objects that don’t come from spOccupancy), postHocLM() may be particularly useful for use with multi-species occupancy models to explore associations between species-specific covariate effect estimates from a multi-species occupancy model with species-level covariates, while fully accounting for uncertainty in the estimates. A vignette displaying how to do this will be posted in the coming months, but see the documentation for the function for basic instructions on how to use the function.sfMsPGOcc() to allow a half-t prior on the community-level variance parameters. See documentation for more information on how to specify this. All multi-species occupancy model fitting functions will eventually be updated to allow for this prior, which can be a less informative prior when sample sizes (i.e., the number of species in this case) is low.intPGOcc() and spIntPGOcc() to remove an error that may occur if a data set only has site level detection covariates.getSVCSamples() to eliminate errors that prevented the function from working under certain circumstances depending on which covariates in the design matrix were modelled as spatially-varying coefficients.tPGOcc() and stPGOcc() to eliminate an error that occurred when trying to run these models with single-visit data sets.mis.spec.type and scale.param arguments to the simTOcc() function to simulate multi-season detection-nondetection data under varying forms of model mis-specification. See simTOcc() documentation for detials. Thanks to Sara Stoudt for her help with this.ppcOcc() from working when there were only site-level random effects on detection. This also sometimes caused problems with cross-validation functionality as well. Thanks to Jose Luis Mena for bringing this to my attention.spOccupancy v0.5.2 contains an important bug fix in the cross-validation functionality for single-season occupancy models with unbalanced sampling across replicates in the data set. Specifically, the reported cross-validation deviance metrics may be inaccurate when one or more sites had a detection history where a missing value came before a non-missing value. For example, if one or more sites had a detection history of c(NA, 1, 0, 0, 1), this would lead to the problem occurring, but this would not occur if all missing values were at the end of the detection history (e.g., c(1, 0, 0, 1, NA)). The affected functions include the following: PGOcc(), spPGOcc(), msPGOcc(), spMsPGOcc(), lfMsPGOcc(), sfMsPGOcc(), intPGOcc(), spIntPGOcc(). We strongly encourage users who have performed cross-validation with these models and unbalanced sampling across replicates in the manner described to rerun their analyses using v0.5.2. We apologize for any troubles this has caused.
spOccupancy v0.5.0 contains numerous substantial updates that provide new functionality, improved run times for models with unstructured random effects, an important bug fix for cross-validation with unstructured random effects under certain scenarios, and some other minor bug fixes. The changes include:
svcPGOcc() fits a single-season spatially-varying coefficient model, and svcTPGOcc() fits a multi-season spatially-varying coefficient model. We also include the functions svcPGBinom() and svcTPGBinom() for fitting spatially-varying coefficient generalized linear models when ignoring imperfect detection. We also include the helper function getSVCSamples() to more easily extract the SVC samples from the resulting model objects if they are desired.C++ code to reduce run times for models that include unstructured random intercepts.k.fold.only argument to all model-fitting functions, which allows users to only perform k-fold cross-validation instead of having to run the model first with the entire data set.X.0 was supplied as a data frame and not a matrix. See GitHub issue 13.tPGOcc() and stPGOcc().fitted() functions for all spOccupancy model objects. See this Github issue for more details.ignore.RE = TRUE.waicOcc() for integrated models (intPGOcc() and spIntPGOcc()) that sometimes resulted in incorrect estimates of WAIC for data sets other than the first data set. We strongly encourage users who have used waicOcc() with an integrated model to rerun their analyses using v0.3.2.predict() on a spatially-explicit model with non-spatial random effects in the occurrence portion of the model to rerun their analyses using v0.3.2.intPGOcc() and spIntPGOcc() that were accidentally introduced in v0.3.0. See this Github issue for more details.spOccupancy Version 0.3.0 contains numerous substantial updates that provide new functionality, improved computational performance for model fitting and subsequent model checking/comparison, and minor bug fixes. The changes include:
lfMsPGOcc() and sfMsPGOcc(). We also included the functions lfJSDM() and sfJSDM() which are more typical joint species distribution models that fail to explicitly account for imperfect detection.predict() functions for single-species and multi-species models now include the argument type, which allows for prediction of detection probability (type = 'detection') at a set of covariate values as well as predictions of occurrence (type = 'occupancy').like.samples in the resulting model object, which contains model likelihood values needed for calculation of WAIC. This leads to much shorter run times for waicOcc() compared to previous versions.fitted.*() functions now return both the fitted values and the estimated detection probability samples from a fitted spOccupancy model.ignore.RE to all predict() functions. If non-spatial random intercepts are included when fitting the model, setting ignore.RE = TRUE will yield predictions that ignore the values of the random effects. If ignore.RE = FALSE, the model will predict new values using the random intercepts for both sampled and non-sampled levels of the effects.spOccupancy model fitting functions that occurred when random effects were included in the occurrence and/or detection component of the model.simOcc() and simMsOcc() that prevented simulating data with multiple random intercepts on detection.NNGP = FALSE and not specifying initial values for the spatial range parameter phi.predict() functions for spMsPGOcc and spPGOcc objects that resulted in potentially inaccurate predictions when n.omp.threads > 1.n.chains argument to all model-fitting functions for running multiple chains in sequence.summary displays for each model-fitting function.predict functions to return occurrence probabilities at sampled sites instead of NAs.spOccupancy.