In annam21/spaceNtime: Run STE, TTE, and ISE Models

Workflow for running a simulation-based power analysis for STE and TTE

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(spaceNtime)

STE power analysis

The precision in STE is driven by a combination of number of cameras, number of occasions, and animal density.

To simulate STE under different scenarios, use the function ste_pwr_sim().

rslt <- ste_pwr_sim(
  N = c(1, 2),
  study_area = c(1e6),
  ncam = c(30, 50),
  nocc = c(1000, 2000),
  cam_area = 100,
  niter = 5
)
rslt

To visualize the results, you can plot the iterations with

plot_pwr_sim(rslt)

You can also visualize the coefficient of variation with

plot_pwr_cv(rslt)

TTE power analysis

The workflow for the TTE power analysis is identical to the STE workflow. First, simulate scenarios using the function tte_pwr_sim(), then visualize the results using the same plot functions as you used for STE.

The only major difference in using tte_pwr_sim() is that the user will have to define the number of sampling periods in each occasion. Refer to the Ecosphere paper and Using spaceNtime vignette for the definition of sampling periods. The length of sampling periods is calculated based on camera area and animal movement. Remember that in real life, there will be a tradeoff between the number of sampling periods per occasion and total number of occasions.

res_tte <- tte_pwr_sim(
  N = c(1, 2),
  study_area = c(1e6),
  ncam = c(30, 50),
  nocc = c(300, 500),
  nper = 100,
  cam_area = 100,
  niter = 5
)
res_tte
plot_pwr_sim(res_tte)
plot_pwr_cv(res_tte)