| deliveryPred {PhysicalActivity} | R Documentation |
Wrapper Function for Accelerometry data Preprocessing, Feature Extraction, and Delivery Prediction
Description
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).
Usage
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. |
Details
Function works for data consisting of one or multiple unique trials.
Value
A dataframe is returned with a predicted probability of each day being a delivery activity day.
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.
Author(s)
Ryan Moore ryan.moore@vumc.org, Cole Beck cole.beck@vumc.org, and Leena Choi leena.choi@Vanderbilt.Edu
See Also
deliveryPreprocess, deliveryFeatures, deliveryPrediction
Examples
data(deliveryData)
predictions <- deliveryPred(df = deliveryData, model = "GLM")