Relax range parameter sanity() check from 1x to 1.5x
the greatest distance in the data.
Add Pearson residuals for several families.
residuals(fit, type = "pearson") Useful for checking for
overdispersion with N > 1 binomial or Poisson families, among other
uses. See the overdisp_fun() function at:
https://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#testing-for-overdispersioncomputing-overdispersion-factor
Fix bug when using residuals() or
simulate() with binomial families specified via
cbind() or weights = N. The binomial sample
size wasn’t being passed through typically resulting in
Inf/-Inf.
Add mixture families: gamma_mix(),
lognormal_mix() and associated delta/hurdle families:
delta_gamma_mix(), delta_lognormal_mix().
These families feature a mixture of two distributions with different
means but shared variance parameters.
Add delta_beta() family.
Tweak sanity() checking of standard error
size.
Export previously experimental plot_anisotropy()
function. The old function is now
plot_anisotropy2().
Allow passing offset data through predict.sdmTMB()
via offset argument.
Switch effects = 'ran_vals' for random intercept
values from tidy.sdmTMB() to match the broom.mixed
package.
Make tidy.sdmTMB() return a tibble if the tibble
package is installed. Note this could affect old code since
drop = FALSE is the default for tibbles but
drop = TRUE is the default for data frames (i.e., tibbles
always return a data frame when subsetted).
Fix longstanding issue with predicting on newdata with mgcv’s
t2(). Previously this was disabled because of issues. It
now works as expected.
Add knots argument in sdmTMB(), which
is passed to mgcv. A common use would be to specify end points in a
cyclical spline (e.g.,
s(x, bs = 'cc', k = 4), knots = list(x = c(1, 3, 5, 7)))
when the data don’t extend fully to the boundaries that should match
up.
Preparing for release on CRAN.
Add time-varying AR1 option (originally was always a random
walk). See time_varying_type argument in
?sdmTMB.
Allow prediction on newdata with missing time
elements. #130
Add check for offset() (which does not work
in sdmTMB, use the offset argument instead). #131
Add check for random slopes (sdmTMB currently only does random intercepts, although slopes can vary spatially). #131
ADREPORT several parameters in natural space. https://github.com/pbs-assess/sdmTMB/discussions/113
Improve robustness of model print() to more esoteric
mgcv smoothers.
Let sims_var work with multiple spatially varying
slopes (zeta_s); return output in named list by
coefficients. #107
Add threshold_coefs to
sdmTMB_simulate().
Don’t make a fake mesh for non-spatial model (faster).
Add vignettes on visreg, ggeffects, and delta families (thanks J. Indivero!) #83 #87 #89 Forecasting and presence-only vignettes to be merged in soon.
Add support for emmeans package. See ?emmeans.sdmTMB
for examples.
Add support for effects package. The
ggeffects::ggeffect() function can be used to make fast
marginal effects plots. ggeffects::ggpredict() works with a
custom fork of ggeffects. A pull request will be made shortly.
#101
Add vcov(), fixef(),
df.residual(), formula(),
terms(), and model.frame() methods.
Add support for "cloglog" link. Code adapted from
glmmTMB for robust likelihood implementation.
For delta models, by default share the anisotropy parameters as
in VAST. Separate anisotropy (old behavior) can be estimated with
control = sdmTMBcontrol(map = list(ln_H_input = factor(c(1, 2, 3, 4))))
Add experimental do_index,
predict_args, and index_args in
sdmTMB(). These can be used to perform prediction and index
calculation at the same time as fitting. For very large datasets or
meshes this can save time compared to fitting, predicting, and index
calculation in 3 separate steps since the TMB AD object doesn’t have to
be rebuilt. This will somewhat slow down the initial fitting.
Remove max_gradient and bad_eig from
get_index() output.
Use unique locations on prediction for huge speedups on large
newdata gridded data.
Fix bug where in rare cases get_index() would return
gibberish small values.
Add bayesian argument, which when TRUE
adds Jacobian adjustments for non-linear transformed parameters. This
should be TRUE if the model will be passed to tmbstan, but
FALSE otherwise. #95
Add experimental and not-yet-exported
sdmTMB:::plot_anisotropy2().
Add many anisotropy, delta model, and index calculation unit tests.
Enable random walk random field TMB simulation in
sdmTMB_simulate().
Add check for irregular time with AR1 or random walk processes.
Fix bugs introduced by delta model code (offsets with
extra_time and threshold model prediction).
Fix bug in sanity() message with small random field
SDs.
Add support for ‘delta’ (or ‘hurdle’) models. See examples and
documentation in ?sdmTMB. This has resulted in a
substantial restructuring of the internal model code. By default both
model components (e.g., binomial & Gamma) share the same formula,
spatial, and spatiotemporal structure, but these can be separated by
supplying argument values in lists where the first element corresponds
to the first model and the second element corresponds to the second
model (with some limitations as described in ?sdmTMB
documentation ‘Details’).
Add support for multiple spatially varying coefficients (used to be limited to a single variable).
Add compatibility with the ‘visreg’ package for visualizing
conditional effects of parameters. See ?visreg_delta for
examples.
Add MCMC residual type to residuals.sdmTMB(). These
are a ‘better’ residuals but slower to calculate. See documentation
‘Details’ in ?residuals.sdmTMB.
Make offset an argument in sdmTMB().
Using the reserved word offset in the formula is now
deprecated.
Add sanity() function to perform some basic sanity
checks on model fits.
Make an sdmTMB() model object compatible with
update() method.
Remove several deprecated arguments.
Overhaul examples in ?sdmTMB.
Use faster “low-rank sparse hessian bias-correction” TMB bias correction.
Add parallel processing support. See parallel
argument in sdmTMBcontrol. By default, grabs value of
sdmTMB.cores option. E.g.
options(sdmTMB.cores = 4). Only currently enabled on
Mac/Linux. Using too many cores can be much slower than 1 core.
Use ‘cli’ package
cli_abort()/cli_warn()/cli_inform()
over
stop()/warning()/message().
Add many unit tests.
poly(..., raw = FALSE) on
newdata. #77Add experimental sdmTMB_stacking() for ensemble
model stacking weights.
Add fake mesh if random fields are all off. #59
Make predict(..., newdata = NULL) also use
last.par.best instead of last.par to match
newdata = df.
Fix bug in MVN fixed-effect prior indexing
sims and n_sims arguments have been
deprecated and standardized to nsim to match the
simulate() S3 method.
Bias correction on get_index() and
get_cog() is now selective and is just applied to the
necessary derived parameters.
INLA projection matrix ‘A’ is now shared across spatial and spatiotemporal fields.
Add add_utm_columns() to ease adding UTM
columns.
Add dharma_residuals().
Fix bug in simulate.sdmTMB() and
residuals.sdmTMB() for binomial family.
Smoothers now appear in print() output. The format
should roughly match brms. The main-effect component (e.g.,
sdepth for s(depth)) represents the linear
component and the random effect (e.g., sds(depth))
component in the output corresponds to the standard deviation of the
penalized weights.
Add censored_poisson(link = 'log') family;
implemented by @joenomiddlename
fields in sdmTMB() is now deprecated
and replaced by spatiotemporal.
include_spatial in sdmTMB() is now
deprecated and replaced by spatial.
spatial_only in sdmTMB() is now
deprecated and replaced by spatiotemporal. E.g.
spatial_only = TRUE is now
spatiotemporal = 'off' or leaving
time = NULL.
spde in sdmTMB() is now deprecated and
replaced by mesh.
sdmTMB_simulate() is new and will likely replace
sdmTMB_sim() eventually. sdmTMB_simulate() is
set up to take a formula and a data frame and is easier to use if you
want different spatial observations (and covariates) for each time
slice. It can also take a fitted model and modify parts of it to
simulate. Finally, this function uses TMB for simulation and so is much
faster and more flexible in what it can simulate (e.g., anisotropy) than
the previous version.
spatial_trend is now spatial_varying
and accepts a one-sided formula with a single predictor of any
coefficient that should varying in space as a random field. Note that
you may want to include a fixed effect for the same variable to improve
interpretability. If the (scaled) time column is used, it will represent
a local-time-trend model as before.
The Tweedie power (p) parameter is now in print()
and tidy() output.
thetaf is now tweedie_p in
sdmTMB_sim().
se_fit = TRUE for
breakpoint models.s() are now penalized smoothers:
they determine the degree of wiggliness (as in mgcv) and it is no longer
necessary to choose an appropriate k value a priori. Models
fit with previous versions of sdmTMB with s(x, k = ...)
will not match models specified the same way in version >= 0.0.19
since the basis functions are now penalized. All the various
mgcv::s() options should be supported but t2()
(and ti() and te()) are not supported.Add ELPD (expected log predictive density) to
sdmTMB_cv() https://arxiv.org/abs/1507.04544
Fix bug evaluating ... when sdmTMB_cv()
was called within a function. #54
Add random walk option: fields = "RW".
Depreciate ar1_fields argument. See new
fields argument in `sdmTMB().
Many packages moved from ‘Imports’ to ‘Suggests’
Lower default nlminb() eval.max and
iter.max to 1000 and 2000.
Added profile option in
sdmTMBcontrol(). This can dramatically improve model
fitting speed with many fixed effects. Note the result is likely to be
slightly different with TRUE
vs. FALSE.
Added simulation from the MVN precision matrix to
predict.sdmTMB(). See the sims
argument.
Added gather_sims() and spread_sims()
to extract parameter simulations from the joint precision matrix in a
format that matches the tidybayes package.
Added get_index_sims() for a population index
calculated from the MVN simulation draws.
Added extract_mcmc() to extract MCMC samples if the
model is passed to tmbstan.
Added the ability to predict from a model fitted with tmbstan.
See the tmbstan_model argument in
predict.sdmTMB().
Allowed for separate random field Matern range parameters for
spatial and spatiotemporal fields. E.g.
sdmTMB(shared_range = FALSE)
Bounded the AR1 rho parameter between -0.999 and 0.999 to improve convergence; was -1 to 1. Please post an issue if this creates problems for your model.
Added map, start, lower,
and upper options to control model fitting. See
sdmTMBcontrol().
Added priors for all parameters. See ?sdmTMB::priors
and the priors argument in sdmTMB(). PC priors
are available for the random fields. See ?pc_matern and the
details there.
Moved many less-common arguments from sdmTMB() to
sdmTMBcontrol().
Fix bug in sdmTMB_cv() where fitting and testing
data splits were reversed. I.e., the small chunk was fit; the big chunk
was tested.
Added experimental penalized complexity (PC) prior as used in
INLA. See arguments matern_prior_O and
matern_prior_E.
Added back normalize argument to
sdmTMB() and default to FALSE. Setting to
TRUE can dramatically speed up some model fits (~4 times
for some test models).
Add re_form_iid to
predict.sdmTMB().
Add map_rf option to sdmTMB(). This
lets you map (fix at their starting values of zero) all random fields to
produce a classic GLM/GLMM.
... + (1 | g)
#34epsilon_predictor argument in sdmTMB()
to allow a model of the spatiotemporal variance through time.penalties argument to allow for
regularization.Fixed parameter initialization for inverse links #35
Switched Gamma ‘phi’ parameter to representing shape instead of CV to match glm(), glmmTMB(), etc.
get_generic() function can be used to grab things like
standardized average values of the response across a grid. What used to
be log_total in the raw TMB output is now
link_total but most users you shouldn’t notice any
difference.Overhauled the simulation function. The function is now called
sdmTMB_sim() and uses INLA functions instead of
RandomFields functions for simulating the random fields.
The simulation function can now accommodate all families and links and takes an INLA mesh as input.
Added a tidy() method (from broom and broom.mixed)
to return a data frame of parameter estimates. The function can extract
the fixed effects or the random effect parameters (variances, AR1
correlation, spatial range).
Added an argument extra_time to
sdmTMB(). This introduces additional time slices that you
can then predict on if you want to interpolate or forecast. Internally,
it uses Eric Ward’s ‘weights hack’. This is also useful if you have data
unevenly spaced in time and you want the gaps evenly spaced for a random
walk or AR1 process (add any missing years to
extra_time).
make_spde() is now replaced with
make_mesh() and make_spde() has been soft
deprecated. make_mesh() carries through the x and y column
names to the predict function and is more in line with the tidyverse
style of taking a data frame first.
make_mesh() can accept cutoff as an
argument (as in INLA), which is likely a better default way to specify
the mesh since it scales across regions better and is line with the
literature on INLA.
make_mesh() can use a binary search algorithm to
find a cutoff that best matches a desired number of knots (thanks to
Kelli Johnson for the idea).
Barrier meshes are now possible. See
add_barrier_mesh() for an example.
There is a pkgdown website now that gets auto generated with GitHub actions.
There is the start of a model description vignette. It is very much a work in progress.
Fixed bug with predictions on new data in models with break points
Overhauled cross validation function. The function now:
Added threshold parameters to the print method
Added forecasting example with the weights hack
Fixed bug in linear break point models
Fixed GAM predictions with all 0s in new data.
Add linear and logistic threshold models. #17
Added parsing of mgcv formulas for splines. #16
Added ability to predict with standard errors at the population level. This helps with making marginal-effect plots. #15
Added optimization options to aid convergence. Also added
run_extra_optimization() to run these on already fit
models. Default is for no extra optimization.
Added binomial likelihood to cross validation. Git hash
ee3f3ba.
Started keeping track of news in NEWS.md.