deliveryPrediction: Predict Delivery Days in Accelerometry Data

Description Usage Arguments Details Value Note Author(s) See Also Examples

View source: R/deliveryPrediction.R

Description

The function predicts the probability of each day in an accelerometry dataset being caused from delivery activity instead of human activity. 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

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deliveryPrediction(df, feats, model = c("RF", "GLM", "NN"), ...)

Arguments

df

A dataframe. The source accelerometry dataset, in dataframe format.

feats

A dataframe. Features output from the deliveryFeatures function.

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.

...

not used at this time

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.

Author(s)

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

See Also

deliveryFeatures, deliveryPred

Examples

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data(deliveryData)

deliveryDataProcessed <- deliveryPreprocess(df = deliveryData)
deliveryDataFeats <- deliveryFeatures(df = deliveryDataProcessed)
deliveryPrediction(deliveryDataProcessed, deliveryDataFeats)

PhysicalActivity documentation built on Jan. 23, 2021, 1:06 a.m.