The bsitar now supports three different types of
splines: 'rcs', 'nsp', and 'nsk'.
While 'rcs' constructs the spline design matrix using the
truncated power basis, both 'nsp' and 'nsk'
implement a B-spline-based natural cubic spline basis. The truncated
power basis method, often referred to as Harrell’s method, is
implemented in the rcspline.eval() function of the
Hmisc package. The B-spline-based implementations of
'nsp' and 'nsk' are the same as those
described in the splines2 package. Previously, only
'rcs' was available. Now, the default method is
'nsp'.
The bsitar package allows for fitting the SITAR model
with a four-parameter formulation (a + b + c + d), where a
fourth parameter, d, is added. The parameter
d, the age slope, allows the adult part of the growth curve
to vary in slope. The age slope d represents the regression
coefficient of y on x. Like the
sitar package, the bsitar offers two
parameterizations: one in which x is adjusted for the
random effects b and c i.e.,
(x-b)*exp(c), and the other in which the unadjusted
x is used. This is controlled by the argument
d_adjusted provided in the bsitar::bsitar()
function. When d_adjusted = TRUE, the first version is
applied (x adjusted for random effects b and
c), which means that individual developmental age, rather
than chronological age, is used in the slope regression. This makes
parameter d more sensitive to the timing of puberty in
individuals. When d_adjusted = FALSE (the default), the
unadjusted x is used. The default choice,
d_adjusted = FALSE, is primarily to match the behavior of
the sitar package, which sets
d.adjusted = FALSE.
Added support for computing and comparing growth curves using the
marginaleffects package as the back-end (see
marginal_draws(), marginal_comparison(), and
growthparameters_comparison()). This enables the use of the
computational flexibility offered by the marginaleffects
package to estimate various quantities of interest, such as adjusted
growth curves (distance and velocity), and growth parameters like age at
peak growth velocity. All three functions support parallel computation
via the future and doFuture packages.
The optimize_model() function now allows users to
specify custom functions in optimize_x and
optimize_y when optimizing the Bayesian SITAR model. For
example, it is now possible to use
optimize_x = list(function(x) log(x + 3/4)). Thanks to Tim
Cole for suggesting this feature. This update greatly enhances the
flexibility of optimize_model() and enables users to search
for a range of optimal x and y
transformations.
An experimental feature has been added to use
$pathfinder() based initial values for the MCMC sampling
$sample() (via the argument
pathfinder_init = TRUE, default is FALSE). The arguments
for $pathfinder() can be specified as a named list using
pathfinder_args. Note that this feature is only available
when backend = 'cmdstanr'.
Added a new vignette comparing growth curves and growth parameters
obtained from the frequentist (sitar package) and Bayesian
(bsitar package) versions of the SITAR model applied to the
height data.
The prior distribution for each parameter has been changed from
student_t() to normal(). The prior
distribution for all parameters, including regression coefficients as
well as the standard deviation (sd) for the group-level random effects
and the distributional parameter (sigma), has been changed to
normal(). Previously, the distribution for regression
coefficients and the sd for the group-level random effects was
student_t(), while the distribution for the sd of the
distributional parameter (sigma) was exponential(). Note
that the same location and scale parameters used earlier for
student_t() are now used for the normal()
distribution. For example, prior specified earlier as
student_t(3, 0, 15) (3 degree of freedom, location 0 and
scale) has been revised as normal(0, 15).
The default setting for initial values is now random
except for the population average parameters: size
(a_init_beta), timing (b_init_beta), intensity
(c_init_beta), and spline coefficients
(s_init_beta). For size and spline coefficient parameters,
the initial values are derived from the linear regression fit and
specified as a_init_beta = lm and
s_init_beta = lm. The initial values for both timing and
intensity parameters are set to ‘0’, i.e., b_init_beta = 0
and c_init_beta = 0.
Improved documentation
The sigma_cov_init_beta = random argument was setting
incorrect initial values for the covariates included in the
sigma formula. The initial values for the Intercept
(sigma_init_beta) were incorrectly applied to the
covariates as well.
Users no longer need to set the environment to
globalenv(), i.e., envir = globalenv(), for
post-processing functions to work properly. The environment is now
automatically set to match the environment of the exposed functions. It
is important to note that manually setting the environment (via the
envir argument) may result in errors. The
envir argument is now primarily for internal use, which is
needed during tests.
Minor corrections and changes have been made to improve the efficiency of the R code.
add_model_criterion() function to compute fit
criteria such as “loo”, “waic”, “kfold”, “loo_subsample”, “bayes_R2”
(Bayesian R-squared), “loo_R2” (LOO-adjusted R-squared), and “marglik”
(log marginal likelihood). The computed fit criteria are added to the
model object for later use including comparison of models. The
add_model_criterion() is a wrapper around the
add_criterion() function available from the
brms packagebsitar::bsitar()```received options ```file```,file_refit, and ``file_compress
to save and retreive fitted objects. See brms::brm help
file for details.