Select the best fitting SITAR model that involves choosing the
optimum degrees of freedom (df) for the natural cubic-spline curve
and the appropriate transformations of the predictor x and response
y variables.
model |
An object of class bgmfit.
|
newdata |
An optional data frame to be used in estimation. If
NULL (default), the newdata is retrieved from the
model.
|
optimize_df |
A list of integers specifying the degree of freedom
(df) values to be optimized. If NULL (default), the df
is taken from the original model. For optimization over different
df, say for example df 4 and df 5, the corresponding
code is optimize_df = list(4,5). For univariate_by and
multivariate models, optimize_df can be a single integer
(e.g., optimize_df = 4) or a list (e.g., optimize_df =
list(4,5)), or a a list of lists. As an example, consider optimization
over df 4 and df 5 for the first sub model, and df 5
and df 6 for the second sub model, the corresponding code is
optimize_df = list(list(4,5), list(5,6)).
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optimize_x |
A vector specifying the transformations for the predictor
variable (i.e., x). The options available are NULL,
'log', 'sqrt', or their combinations. Note that user need not
to enclose these options in a single or double quotes as they are take care
of internally. The default setting is to explore all possible combination
i.e., optimize_x = list(NULL, log, sqrt). Similar to the
optimize_df, user can specify different optimize_x for
univariate_by and multivariate sub models.
|
optimize_y |
A vector specifying the transformations of the the response
variable (i.e., y). The approach and options available for
optimize_y are same as described above for the optimize_x.
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transform_prior_class |
A character vector (default NULL)
specifying the transformations of location-scale based priors such as
normal() when response variable (i.e., y) is 'log' or
'sqrt' transformed. The prior type that could be transformed are
'beta', 'sd', 'rsd', 'sigma' and 'dpar'.
Currently it is available only for 'log' transformed y. Each
prior type (i.e., 'beta', 'sd', 'rsd', 'sigma', 'dpar') specified
via transform_prior_class is log transformed as follows:
log_location = log(location / sqrt(scale^2 / location^2 + 1)),
log_scale = sqrt(log(scale^2 / location^2 + 1)),
where location and scale are the original parameters supplied by the user
and the log_location and log_scale are the equivalent parameters on the log
scale. For more details, see a_prior_beta argument in bsitar()
function. Note that transform_prior_class is used as an experiment
and therefore results may not be what user intended. Thus we recommend to
explicitly set the desired prior and not to use
transform_prior_class.
|
transform_beta_coef |
A character vector (default NULL)
specifying the transformations of location-scale based priors for specific
regression coefficient(s) when response variable (i.e., y) is
'log' or 'sqrt' transformed. The coefficient that could be
transformed are 'a', 'b', 'c', 'd' and
's'. The default is transform_beta_coef = c('b',' b', 'd')
which implies that parameters 'a', 'a' and 'a' will be
transformed whereas parameter 'a' will be left unchanged because
default prior for parameter 'a' is based on outcome y itself
(e.g., a_prior_beta = normal(ymean, ysd)) which has be transformed.
However, we strongly suggest that user explicitly set the desired prior and
not to rely on transform_beta_coef because it is included on
experimental basis. See transform_prior_class for details.
|
transform_sd_coef |
A character vector (default NULL) specifying
the transformations of location-scale based priors for specific group level
coefficient(s) when response variable (i.e., y) is 'log' or
'sqrt' transformed. The coefficient that could be transformed are
'a', 'b', 'c', 'd' and 's'. The default
is transform_beta_coef = c('b',' b', 'd'). See
transform_prior_class and transform_beta_coef for details.
|
exclude_default_funs |
A logical to indicate whether transformations for
(x and y) variables used in the original model fit should be
excluded. If TRUE (default), the transformations specified for the
x and y variables in the original model fit are excluded from
the optimize_x and optimize_y. From example, if original
model is fit with xvar = log and yvar = NULL, then
optimize_x is translated into optimize_x = list(NULL, sqrt),
and similarly optimize_y is reset as optimize_y = list(log,
sqrt).
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add_fit_criteria |
An optional argument (default NULL) to
indicate whether to add fit criteria to the returned model fit. Options
available are 'loo' and 'waic'. Please see
brms::add_criterion() for details.
|
add_bayes_R |
An optional argument (default NULL) to indicate
whether to add Bayesian R square to the returned model fit. To estimate and
add bayes_R2 to the model fit, the argument add_bayes_R is
set as add_bayes_R = 'bayes_R2'.
|
byresp |
A logical (default FALSE) to indicate if response wise
fit criteria to be calculated. This argument is evaluated only for the
multivariate model in which user can select whether to get joint
calculation of point wise log likelihood (byresp = FALSE) or
response specific (byresp = TRUE). For, univariate_by model,
the only option available is to calculate separate point wise log
likelihood for each sub-model, i.e., byresp = TRUE.
|
digits |
An integer (default 2) to set the decimal argument for
the base::round() function.
|
cores |
The number of cores to used in parallel processing (default
1). The argument cores is passed to the
brms::add_criterion().
|
verbose |
An optional argument (logical, default FALSE) to
indicate whether to print information collected during setting up the
object(s).
|
expose_function |
An optional logical argument to indicate whether to
expose Stan functions (default FALSE). Note that if user has already
exposed Stan functions during model fit by setting expose_function =
TRUE in the bsitar(), then those exposed functions are saved and can be
used during post processing of the posterior draws and therefore
expose_function is by default set as FALSE in all post
processing functions except optimize_model(). For optimize_model(), the
default setting is expose_function = NULL. The reason is that each
optimized model has different Stan function and therefore it need to be re
exposed and saved. The expose_function = NULL implies that the
setting for expose_function is taken from the original model
fit. Note that expose_function must be set to TRUE when
adding fit criteria and/or bayes_R2 during model
optimization.
|
usesavedfuns |
A logical (default NULL) to indicate whether to
use the already exposed and saved Stan functions. Depending on
whether the user have exposed Stan functions within the bsitar() call via
expose_functions argument in the bsitar(), the usesavedfuns
is automatically set to TRUE (if expose_functions = TRUE) or
FALSE (if expose_functions = FALSE). Therefore, manual
setting of usesavedfuns as TRUE/FALSE is rarely
needed. This is for internal purposes only and mainly used during the
testing of the functions and therefore should not be used by users as it
might lead to unreliable estimates.
|
clearenvfuns |
A logical to indicate whether to clear the exposed
function from the environment (TRUE) or not (FALSE). If
NULL (default), then clearenvfuns is set as TRUE when
usesavedfuns is TRUE, and FALSE if usesavedfuns
is FALSE.
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envir |
Environment used for function evaluation. The default is
NULL which will set parent.frame() as default environment.
Note that since most of post processing functions are based on brms,
the functions needed for evaluation should be in the .GlobalEnv.
Therefore, it is strongly recommended to set envir = globalenv()
(or envir = .GlobalEnv). This is particularly true for the
derivatives such as velocity curve.
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... |
Other arguments passed to update_model.
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