block_acf

This function allows calculation of an autocorrelation function (ACF) for a dataset with multiple independent units (for example, data from several individuals, data from multiple dives by an individual animal, etc.). The groups (individual, dive, etc.) should be coded in a categorical variable. The function calculates correlation coefficients over all levels of the categorical variable, but respecting divisions between levels (for example, individual animals are kept separate).

High-resolution movement-sensor tags typically include accelerometers
to measure body posture and sudden movements or changes in speed,
magnetometers to measure direction of travel, and pressure sensors
to measure dive depth in aquatic or marine animals. The sensors in these tags usually sample many times per second. Some tags include sensors for speed, turning rate (gyroscopes), and sound. This package provides software tools to facilitate calibration, processing, and analysis of such data. Tools are provided for: data import/export;
calibration (from raw data to calibrated data in scientific units);
visualization (for example, multi-panel time-series plots);
data processing (such as event detection, calculation of derived metrics like jerk and
dynamic acceleration, dive detection, and dive parameter calculation); and statistical analysis (for example, track reconstruction, a rotation test, and Mahalanobis distance analysis).

Stacy DeRuiter

tagtools

Work with Data from High-Resolution Biologging Tags

Mark Johnson

David Sweeney

Ye Joo McNamara-Oh

Samuel Fynewever

(Oghenkevwe) Racheal Tejevbo

Tiago Marques

Yuqian Wang

(Oghenesuvwe) Su Ogedegbe

block_acf function

<dl><dt>resids</dt>
<dd>The variable for which the ACF is to be computed (often a vector of residuals from a fitted model)</dd>
<dt>blocks</dt>
<dd>A categorical variable indicating the groupings (must be the same length as resids. ACF will be computed only for data points within the same block.)</dd>
<dt>max_lag</dt>
<dd>ACF will be computed at 0-max_lag lags, ignoring all observations that span blocks. Defaults to the minimum number of observations in any block. The function will allow you to specify a max_lag longer than the shortest block if you so choose.</dd>
<dt>make_plot</dt>
<dd>Logical. Should a plot be produced? Defaults to TRUE.</dd>
<dt>...</dt>
<dd>Additional arguments to be passed to plot.acf</dd></dl>

Arguments

Compute autocorrelation function — block_acf

<dl>

<dt>resids</dt>
<dd>The variable for which the ACF is to be computed (often a vector of residuals from a fitted model)</dd>


<dt>blocks</dt>
<dd>A categorical variable indicating the groupings (must be the same length as resids. ACF will be computed only for data points within the same block.)</dd>


<dt>max_lag</dt>
<dd>ACF will be computed at 0-max_lag lags, ignoring all observations that span blocks. Defaults to the minimum number of observations in any block. The function will allow you to specify a max_lag longer than the shortest block if you so choose.</dd>


<dt>make_plot</dt>
<dd>Logical. Should a plot be produced? Defaults to TRUE.</dd>


<dt>...</dt>
<dd>Additional arguments to be passed to plot.acf</dd>

</dl>

Compute autocorrelation function

block_acf: Compute autocorrelation function

Description

Usage

Value

Arguments

Examples