opt_mbp

Takes dataframes created by `renorm_SSR()` and `conv_thresholds()` and
determines the corresponding "optimum" maximum blanking period (MBP) for each
convergence threshold.

Analyze telemetry datasets generalized to allow
any technology. The filtering steps check for false positives caused by
reflected transmissions from surfaces and false pings from other noise
generating equipment. The filters are based on JSATS filtering algorithms
found in package 'filteRjsats' <https://CRAN.R-project.org/package=filteRjsats>
but have been generalized to allow the user to define many of the filtering
variables. Additionally, this package contains scripts used to help identify
an optimal maximum blanking period as defined in Capello et al (2015)
<doi:10.1371/journal.pone.0134002>. The functions were written according to
their manuscript description, but have not been reviewed by the authors for
accuracy. It is included here as is, without warranty.

Taylor Spaulding

telemetR

Filter and Analyze Generalised Telemetry Data from Organisms

opt_mbp function

<dl><dt>rSSR_df</dt>
<dd>a dataframe created by created by renorm_SSRduration compare
showing the renormalized sum of squares of the residuals between one
potential blanking period and the next.</dd>
<dt>thresh_values</dt>
<dd>a dataframe created by conv_thresholds corresponding to
the chosen convergence thresholds.</dd></dl>

Arguments

Idenitfy the Optimum MBP based on Convergence Threshold — opt_mbp

<dl>

<dt>rSSR_df</dt>
<dd>a dataframe created by created by renorm_SSRduration compare
showing the renormalized sum of squares of the residuals between one
potential blanking period and the next.</dd>


<dt>thresh_values</dt>
<dd>a dataframe created by conv_thresholds corresponding to
the chosen convergence thresholds.</dd>

</dl>

opt_mbp: Idenitfy the Optimum MBP based on Convergence Threshold

Description

Usage

Value

Arguments

Examples