Within the Run SCR module, we allow the user to run SCR analyses via a Bayesian and Maximum Likelihood approach. For the Maximum Likelihood approach, we use the secr package.
There are great ways to visualize the SCR data by using the summary() and plot() functions to show a list of summary stats and a map of detections and stations, respectively. These are great tools to assist the user in determining if there are any issues in their analysis.
These summary() and plot() functions summarize and plot the output of the secr::read.capthist() function, specifically.
Within the Run SCR module, we allow the user to run SCR analyses via a Bayesian and Maximum Likelihood approach. For the Maximum Likelihood approach, we use the secr package.
There are great ways to visualize the SCR data by using the summary() and plot() functions to show a list of summary stats and a map of detections and stations, respectively. These are great tools to assist the user in determining if there are any issues in their analysis.
These summary() and plot() functions summarize and plot the output of the
secr::read.capthist()function, specifically.plot() example: plot(KafueCentral2021.lion.MLFR.capthist, border=2000, title=FALSE, tracks=TRUE, varycol=TRUE)
We should somehow communicate this to the users, either on the page or within another tab on the page. To be discussed when the time comes.
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