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"bage_mod" objects"multi" option for optimizer
argument to fit(). With "multi", the
fit() function first tries nlminb() and if
that fails switches to optim() with method
"BFGS"."bage_mod" objects to show
the time spent by TMB::sdreport rather than the time spent
by drawing from the multivariate normal (which, since
bage started using sparseMVN, is very
short).kor_births.report_sim() excludes comparisons of
"hyper" parameters (eg standard deviations) if the
simulation model and estimation model use different priors with
different classes for that term. For instance if the simulation model
uses a RW() prior for age and the estimation model uses a
RW2() prior for age, then report_sim() will
not report on the standard deviation parameter for age.report_sim() stating
that the interface is still under development.zero_sum argument to con (short
for “constraint”). con = "none" corresponds to
zero_sum = FALSE, and con = "by" corresponds
to zero_sum = TRUE. Additional options will be added in
future.sd argument to RW(),
RW2(), SVD_RW() and SVD_RW2().
The initial value of the random walks are drawn from a
N(0, sd^2) prior. By default sd equals
1, but it can be set to
AR() and Lin_AR()
priors so that the coefficients no longer need to be consistent with
stationarity. The Stan user guide recommends against building in
stationarity:
https://mc-stan.org/docs/stan-users-guide/time-series.html#autoregressive.section
Also, testing for stationarity often causes numerical problems.AR() and
Lin_AR() priors.AR() and
Lin_AR() priors, so that partical autocorrelation function
(PACF), rather than the AR coefficients themselves, are restricted to
(-1, 1). Restricting the PACF to (-1,1) ensures stationarity.optimizer argument to fit(), giving
choice between three ways of optimizingquiet argument to
fit() so that when it is TRUE, trace output
from the optimizer is shown.start_oldpar argument to fit(), to
allow calculations to be restarted on a model that has already been
fitted."bage_mod" object.computations part of models so
that it works with models fitted using the “inner-outer” method.
Extended the print() method for "bage_mod" so
that it shows extra output for models fitted using the
"inner-outer" method."bage_mod" objects. (Thank you to Andrew Taylor for
suggesting this.)s = 0 in Lin() priorszero_sum argument to priors with an
along dimension. When zero_sum is
TRUE, values for each combination of a by
variable and the along variable are constrained to sum to
zero. This can allow better identification of higher-level terms in
complicated models. It can also slow computations, and has virtually no
effect on estimates of the lowest-level rates, probabilities, and
means.RW2_Infant() prior for modelling age-patterns of
mortality rates.s_seas parameter in RW_Seas() and
RW2_Seas() now defaults to 0, rather than 1, so that
seasonal effects are by default fixed over time rather than varying.
Using varying seasonal effects can greatly increase computation
times.computations(), which can be used to
extract this information from fitted model objects.quiet argument to fit(). When
quiet is TRUE (the default), warnings
generated by nlminb() are suppressed. (These warnings are
virtually always about NAs early in the optimization process and are
nothing to worry about.)HFD, a scaled SVD object holding data from the
Human Fertiltiy Databasedeaths –> isl_deathsexpenditure –> nld_expendituredivorces –> nzl_divorcesinjuries –> nzl_injuriesus_acc_deaths –> usa_deathskor_births, births in South
Koreareport_sim() now works on fitted models. Thank you to
Ollie Pike for pointing out that it previously did not.age variable in
divorces.rr3().
Call poputils function rr3() instead.newdata argument to forecast().Lin() and
Lin_AR() priors.method and vars_inner to
fit(). When method is "standard"
(the default) fit() uses the existing calculation methods.
When method is "inner-outer",
fit() uses a new, somewhat experimental calculation method
that involves fitting an inner model using a subset of variables, and
then an outer model using the remaining variables. With big datasets,
"inner-outer" can be faster, and use less memory, but give
very similar results.fit() now internally aggregates input data before
fitting, so that cells with the same combinations of predictor variables
are combined. This increases speed and reduces memory usage.print.bage_modssvd() no longer exported. Will export once
package bssvd matures.augment() so it runs fasterdivorces datasetset_datamod_outcome_rr3(), which deals with the case where
the outcome variable has been randomly rounded to base 3.augment() now creates a new version of the outcome
variable if (i) the outcome variable has NAs, or (ii) a
data model is being applied to the outcome variable. The name of the new
variable is created by added a . to the start of the name
of the outcome variable.standardization
argument: "terms", "anova", and
"none". With "terms", all effects, plus
assoicated SVD coefficients, and trend, cyclical, and seasonal terms,
are centered independently. With "anova", the type of
standardization descibed in Section 15.6 of Gelman et al (2014) Bayesian
Data Analysis, is applied to the effects.SVDS(), SVDS_AR(),
SVDS_AR1(), SVDS_RW(), and
SVDS_RW2() priors. Added indep argument to
corresponding SVD priors. SVD priors now
choose between ‘total’, ‘independent’ and ‘joint’ models based on (1)
the value of indep argument, (2) the value of
var_sexgender and the name of the term.HMD now contains 5 components, rather than
10.Lin() and
LinAR() priorsSVD_AR(), SVDS_AR(),
SVD_AR1(), SVDS_AR1(), SVD_RW(),
SVDS_RW(), SVD_RW2(),
SVDS_RW2()draws_linpred, added draws_effectfree,
draws_spline, and draws_svd. Modified/added
downstream functions.compose_time()report_sim()components().augment() method for bage_mod objects now
calculated value for .fitted in cases where the outcome or
exposure/size is NA, rather than setting the value of
.fitted to NA.components() is
called. augment() uses the linear predictor (which does not
need standardization.)disp are stored, rather than the
full standardized components.ssvd_comp().forecast.bage_mod() Forecasting. Interface not yet
finalised.generate.bage_ssvd() Generate random age-sex profiles
from SVD.draw_vals_effect_mod() was
malfunctioning on models that contained SVD priors.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.