Description Usage Arguments Value See Also
Train the phenotyping model on the training dataset, and evaluate its performance via random splits of the training dataset.
1 2 3 4 5 | phecap_train_phenotyping_model(
data, surrogates, feature_selected,
method = "lasso_bic",
train_percent = 0.7, num_splits = 200L,
start_seed = 78900L, verbose = 0L)
|
data |
an object of class |
surrogates |
a list of objects of class |
feature_selected |
a character vector of the features that should be included in the model,
probably returned by |
method |
Either a character vector or a list of two components. If a character vector is used, possible entries are given below. When at least two methods are specified, the predicted probability is the simple average of the predicted probabilities from each method.
If a list is used, it should contain two named components as follows.
|
train_percent |
The percentage (between 0 and 1) of labels that are used for model training during random splits |
num_splits |
The number of random splits. |
start_seed |
in the i-th split, the seed is set to start_seed + i. |
verbose |
print progress every verbose splits if verbose is positive, or remain quiet if verbose is zero |
An object of class PhecapModel
, with components
coefficients |
the fitted object |
method |
the method used for model training |
feature_selected |
the feature selected by SAFE |
train_roc |
ROC on training dataset |
train_auc |
AUC on training dataset |
split_roc |
average ROC on random splits of training dataset |
split_auc |
average AUC on random splits of training dataset |
fit_function |
the function used for fitting |
predict_function |
the function used for prediction |
See PheCAP-package
for code examples.
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