hibbing-helpers

apply_two_regression_hibbing18

Applies the specified two-regression algorithm from
<a href="https://pubmed.ncbi.nlm.nih.gov/29271847/">Hibbing et al. (2018,
Med Sci Sports Exerc)</a> to data from the primary accelerometer and IMU
(if applicable)

internal

Facilitates development and application of two-regression
algorithms for research-grade wearable devices. It provides an easy
way for users to access previously-developed algorithms, and also to
develop their own. Initial motivation came from Hibbing PR, LaMunion SR,
Kaplan AS, & Crouter SE (2018) <doi:10.1249/MSS.0000000000001532>.
However, other algorithms are now supported. Please see the associated
references in the package documentation for full details of the algorithms
that are supported.

Paul Hibbins

TwoRegression

Develop and Apply Two-Regression Algorithms

hibbing-helpers function

<dl><dt>which_algorithm</dt>
<dd>a dataframe specifying which algorithm to use, based
on <code>Wear_Location</code> and <code>Algorithm</code> columns</dd>
<dt>AG</dt>
<dd>data frame of ActiGraph data</dd>
<dt>verbose</dt>
<dd>logical. Print updates to console?</dd>
<dt>...</dt>
<dd>arguments passed to <code>predict.TwoRegression</code></dd></dl>

Arguments

Applies the specified two-regression algorithm from
<a href='https://pubmed.ncbi.nlm.nih.gov/29271847/'>Hibbing et al. (2018,
Med Sci Sports Exerc)</a> to data from the primary accelerometer and IMU
(if applicable)

Apply a Hibbing 2018 two-regression algorithm — hibbing-helpers

<dl>

<dt>which_algorithm</dt>
<dd>a dataframe specifying which algorithm to use, based
on <code>Wear_Location</code> and <code>Algorithm</code> columns</dd>


<dt>AG</dt>
<dd>data frame of ActiGraph data</dd>


<dt>verbose</dt>
<dd>logical. Print updates to console?</dd>


<dt>...</dt>
<dd>arguments passed to <code>predict.TwoRegression</code></dd>

</dl>

hibbing-helpers: Apply a Hibbing 2018 two-regression algorithm

Description

Usage

Value

Arguments