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The random initial values for the residual correlation parameter of multivariate model resulted in empty list warning.
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.