inst/app/help/5_b_variograms.md

In ctmm-initiative/ctmm-webapp: Web App For ctmm

• Variograms of individuals in current subset are plotted here.
• Empirical variograms are added with a layer of automatic Guesstimate parameters.
• You can further fine-tune the parameters by selecting individual in the drop down list.
  • The drop down list support search filter and direct editing.
  • There will be a pop up window for fine-tune parameters.
  • Center current values will update sliders range to twice of current value, so it's easier to deal with very small or large values.
  • Apply butto will accept adjusted parameters and update the variogram.
• Alternative information criteria can be selected before moving to next step of model fitting, which can be useful for certain case like small samples.
• Moving to Modeled tab will try various models with current guesstimate parameters as initial condition.

Reference

• C. H. Fleming, J. M. Calabrese, T. Mueller, K.A. Olson, P. Leimgruber, W. F. Fagan. From fine-scale foraging to home ranges: A semi-variance approach to identifying movement modes across spatiotemporal scales. The American Naturalist, 183:5, E154-E167 (2014).

• J. M. Calabrese., C. H. Fleming, E. Gurarie. ctmm: an r package for analyzing animal relocation data as a continuous‐time stochastic process. Methods in Ecology and Evolution 7:9, 1124-1132 (2016).

ctmm-initiative/ctmm-webapp documentation built on June 25, 2024, 8:29 a.m.