tangled | R Documentation |
The tangled package takes output from the model described in Schick et al. 2013, PLoS-ONE. In the package we analyse the data, prepare it for plotting and make plot objects to be used in the entanglement paper. This paper (Knowlton, Schick, et al. in prep) describes the effect of entanglements on the health and survival of right whales. There are 5 main components to the package: 1) extraction summary; 2) changes in health over time; 3) health during entanglements; 4) sub-lethal impacts of impaired health; and 5) effects on survival. I'll document those briefly in the sections below, but they are spelled out in greater detail in each of the vignettes that accompany this package.
The goal of this section is to show for the readers how we extract health
information with respect to the start/end times and duration of the
entanglements. The important function is winDiagram
This part of the package describes how we plot changes in health
over time as a function of entanglement severity. There are 4 prep functions:
prepSlopeHealthData
, prepSlopeHealthDataBarplot
,
prepSlopeHealthDataMedian
, and prepSlopeHealthDataAnnotation
;
there is 1 plot function: plotSlopeHealth
.
While the Changes in Health section describes the change across the entanglement
period - the goal of this analysis is to examine health during the
period. There are 2 main functions: prepBoxplotHealthData
and
plotBoxplotHealth
.
This section documents the amount of time animals are spending in impaired
health status during their entanglements. The idea is to see if animals
are spending more time in worse health - as defined by the reproductive
threshold enumerated in Rolland et al. (2016) Marine Ecology Progress
Series 542:265-82. We compare health below this threshold status as
a function of increasing entanglement severity and complexity. There are 4
main functions: prepThreshDataUnImp
, prepThreshDataRepro
,
prepHealthThresholdPlotData
, and plotHealthThreshold
.
This section examines survival of entangled right whales as a function
of injury category. As with the other sections, it combines data prep \&
analysis with data plotting. Unlike the others, we also add a section
comparing the statistics of the different curves and models. There are 6
main functions: calcpresdSurvdat
, calckdpaSurvdat
both of which
prepare the survival data for each of the three types: known dead, presumed
dead, and presumed alive. The next 2 functions: calcKMCurves
and
calcKMCurvesSevGen
prepare Kaplan-Meier survivorship data. Finally,
plotSurv
and potSurvGenderSeverity
make the plots of this
survivorship data.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.