Repository and research compendium in support of the manuscript "Spring haul-out behavior of seals in the Bering and Chukchi seas." Maintained by Josh London (@jmlondon / josh.london@noaa.gov)
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Improve weather covariate marginal effect like plots #22
The initial attempts to plot the marginal/conditional effect of individual weather covariates on haul-out probability resulted in plots that were aesthetically pleasing and 'beautiful' but, ultimately, distracting and difficult for the reader to interpret.
here's example of the initial plot for spotted seals
the following collection of reviewer comments from Jason Baker sums up the rationale for improvement
The swooshy vapor trails, I think, require more explanation. You mention transparent vertical lines, and I can see some of those, but I can also see horizontal bands in some places. How to interpret the alternating consecutive high and low CI’s on the right 2/3 of the precip graph, which mostly don’t include the fitted line!?
It comes down to what you want to convey. If it’s that we need to account for temperature, then what is wrong with good old marginal plots? You could choose peak haul out, high solar noon, mean precipitation, mean wind, etc. and show how temperature affects haul out probability.
In chatting with @dsjohnson I'm convinced that traditional marginal or conditional effects plots aren't the best option for communicating the relationship. These plots pull from the same data set as the haul-out probability surface plots and, simply, reflect the range of predicted haul-out probabilities across the range of weather covariate values. So, I also don't have to develop all the additional code to produce more typical marginal/conditional effects.
That said, I think Jason's concerns are important to recognize. I'm hoping that I can find a solution by limiting the range of days and hours in the plot and provide some control for underlying temporal collinearity and interactions that lead to the 'swooshy vapor trails'
The initial attempts to plot the marginal/conditional effect of individual weather covariates on haul-out probability resulted in plots that were aesthetically pleasing and 'beautiful' but, ultimately, distracting and difficult for the reader to interpret.
here's example of the initial plot for spotted seals
the following collection of reviewer comments from Jason Baker sums up the rationale for improvement
In chatting with @dsjohnson I'm convinced that traditional marginal or conditional effects plots aren't the best option for communicating the relationship. These plots pull from the same data set as the haul-out probability surface plots and, simply, reflect the range of predicted haul-out probabilities across the range of weather covariate values. So, I also don't have to develop all the additional code to produce more typical marginal/conditional effects.
That said, I think Jason's concerns are important to recognize. I'm hoping that I can find a solution by limiting the range of days and hours in the plot and provide some control for underlying temporal collinearity and interactions that lead to the 'swooshy vapor trails'