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Introduction to acre

Source: vignettes/acre.qmd

Just placeholder-ish for now.

Installation

If you haven’t already, make sure to install the acre using devtools.

library(devtools)
install_github("b-steve/acre")

Reading in data

The first step is to combine all of these data sources together into an R object using the read.acre() function. It is your job to create this object. There are five arguments you’ll need to use:

  1. captures: the data frame with the detection data. traps: the object with the listening post locations.

  2. control.mask: you need to specify the maximum distance at which you can possibly detect a gibbon. In this case, the maximum feasible distance is 3000m. Use control.mask = list(buffer = 3000) for this argument.

  3. loc.cov: a data frame with columns x and y, specifying locations at which spatial covariates have been measured, and then a further column for each spatial covariates themselves. For this step you should provide the data frame containing the measured covariate values, rather than the interpolated values. The function will complete the interpolation for you.

  4. dist.cov: a data frame containing locations of objects of interest, from which you want to construct a spatial covariate for the distance to the nearest object. This needs to be a list, where each component name relates to the type of object, and the component itself is a data frame with columns named x and y specifying the locations of these objects. In this case, we just have to obtain the distance to the nearest village for each point in PPWS, so you can use dist.cov = list(village = villages.df).

Visualizing data

Multiple exploratory plotting tools available.

See…

  1. plot(data.acre, type = "survey")

  2. plot(data.acre, type = "capt")

  3. plot(data.acre, type = "covariates")

Fitting models

Maybe fit a basic model here? I think that in this introduction we should definitely use an IHD example, but maybe one that is as simple as possible?

Just because being able to plot the detection surface, and seeing how it varies is super neat, rather than just one uniform-coloured detection surface blob.

  1. fit <- fit.acre(data.acre)

  2. summary(fit)

  3. plot(data.acre, type = "detfn")

  4. plot(data.acre, type = "Dsurf")

More…(not sure what to title this section)

Here explain all the additional functionality provided, and then suggest seeing the tutorials for further guidance?