hBayesDM Model Base Function
The base function from which all hBayesDM model functions are created.
Contributor: Jethro Lee
hBayesDM_model( task_name, model_name, model_type = "", data_columns, parameters, regressors = NULL, postpreds = "y_pred", stanmodel_arg = NULL, preprocess_func )
task_name |
Character value for name of task. E.g. |
model_name |
Character value for name of model. E.g. |
model_type |
Character value for modeling type: |
data_columns |
Character vector of necessary column names for the data. E.g.
|
parameters |
List of parameters, with information about their lower bound, plausible value,
upper bound. E.g. |
regressors |
List of regressors, with information about their extracted dimensions. E.g.
|
postpreds |
Character vector of name(s) for the trial-level posterior predictive
simulations. Default is |
stanmodel_arg |
Leave as |
preprocess_func |
Function to preprocess the raw data before it gets passed to Stan. Takes
(at least) two arguments: a data.table object |
task_name: Typically same task models share the same data column requirements.
model_name: Typically different models are distinguished by their different list of parameters.
model_type is one of the following three:
""Modeling of multiple subjects. (Default hierarchical Bayesian analysis.)
"single"Modeling of a single subject.
"multipleB"Modeling of multiple subjects, where multiple blocks exist within each subject.
data_columns must be the entirety of necessary data columns used at some point in the R
or Stan code. I.e. "subjID" must always be included. In the case of 'multipleB' type
models, "block" should also be included as well.
parameters is a list object, whose keys are the parameters of this model. Each parameter key must be assigned a numeric vector holding 3 elements: the parameter's lower bound, plausible value, and upper bound.
regressors is a list object, whose keys are the model-based regressors of this model.
Each regressor key must be assigned a numeric value indicating the number of dimensions its
data will be extracted as. If model-based regressors are not available for this model, this
argument should just be NULL.
postpreds defaults to "y_pred", but any other character vector holding
appropriate names is possible (c.f. Two-Step Task models). If posterior predictions are not yet
available for this model, this argument should just be NULL.
stanmodel_arg can be used by developers, during the developmental stage of creating a
new model function. If this argument is passed a character value, the Stan file with the
corresponding name will be used for model fitting. If this argument is passed a
stanmodel object, that stanmodel object will be used for model fitting. When
creation of the model function is complete, this argument should just be left as NULL.
preprocess_func is the part of the code that is specific to the model, and is thus
written in the specific model R file.
Arguments for this function are:
raw_dataA data.table that holds the raw user data, which was read by using
fread.
general_infoA list that holds the general informations about the raw data, i.e.
subjs, n_subj, t_subjs, t_max, b_subjs, b_max.
...Optional additional argument(s) that specific model functions may want to
include. Examples of such additional arguments currently being used in hBayesDM models are:
RTbound (choiceRT_ddm models), payscale (igt models), and trans_prob (ts
models).
Return value for this function should be:
data_listA list with appropriately named keys (as required by the model Stan file), holding the fully preprocessed user data.
NOTE: Syntax for data.table slightly differs from that of data.frame. If you want to use
raw_data as a data.frame when writing the preprocess_func, simply begin with the
line: raw_data <- as.data.frame(raw_data).
NOTE: Because of allowing case & underscore insensitive column names in user data,
raw_data columns must now be referenced by their lowercase non-underscored versions,
e.g. "subjid", within the code of the preprocess function.
A specific hBayesDM model function.
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