deliveryPred function

Wrapper Function for Accelerometry data Preprocessing, Feature Extraction, and Delivery Prediction

The function is a wrapper function that performs preprocessing, feature extraction, and delivery day prediction of an accelerometry dataset. The prediction model can be selected from one of three models, a Random Forest, a logistic regression, and a convolutional neural network (default: Random Forest).

deliveryPred(df, model = c("RF", "NN", "GLM"))

Arguments

• df: A dataframe. The source accelerometry dataset, in dataframe format.
• model: A character. Indicates which prediction model to use. RF is a Random Forest. GLM is a logistic regression, and NN is a convolutional neural network.

Returns

A dataframe is returned with a predicted probability of each day being a delivery activity day.

Details

Function works for data consisting of one or multiple unique trials.

Note

The input dataframe should have the following columns: TimeStamp , axis1 , axis2 , axis3 , vm , where vm is the vector magnitude of axes 1, 2, and 3. Dataframe should also be formatted to 60 second epoch.

The function uses the default preprocessing criteria used in the development of the predictive models.

Examples

data(deliveryData)

predictions <- deliveryPred(df = deliveryData, model = "GLM")

See Also

deliveryPreprocess, deliveryFeatures, deliveryPrediction

Author(s)

Ryan Moore ryan.moore@vumc.org , Cole Beck cole.beck@vumc.org , and Leena Choi leena.choi@Vanderbilt.Edu