# Prepare Data for UHC Plots for a Fitted Model Creates data used to make used-habitat calibration plots ```r prep_uhc(object, test_dat, n_samp = 1000, n_dens = 512, verbose = TRUE) ## S3 method for class 'glm' prep_uhc(object, test_dat, n_samp = 1000, n_dens = 512, verbose = TRUE) ## S3 method for class 'fit_logit' prep_uhc(object, test_dat, n_samp = 1000, n_dens = 512, verbose = TRUE) ## S3 method for class 'fit_clogit' prep_uhc(object, test_dat, n_samp = 1000, n_dens = 512, verbose = TRUE) ``` ## Arguments - `object`: `[glm, fit_logit, fit_clogit]` A fitted RSF or (i)SSF model. Should be fit to **training** dataset separate from the testing data. - `test_dat`: `[data.frame]` A `data.frame` with **testing** data from which to sample test points. Should be separate from the data used to train the model passed to `object`. - `n_samp`: `[numeric = 1000]` A `vector` of length 1 giving the number of samples to use to characterize the used habitat distribution under the model. - `n_dens`: `[numeric = 512]` A `numeric` vector of length 1 giving the number of equally spaced points at which density (used, available, and sampled) is estimated. Passed to `stats::density.default()`, which indicates that `n` should usually be specified as a power of 2. - `verbose`: `[logical]` Should messages be displayed (`TRUE`) or not (`FALSE`)? ## Returns Returns a `list` of class `uhc_data` with elements: * `orig`: List of `data.frame`s, one per variable (see `vars`). Each `data.frame` contains the density plot data (`x` and `y`) for the original used (`dist == "U"`) and available (`dist == "A"`) data. * `samp`: List of `data.frame`s, one per variable (see `vars`). Each `data.frame` contains the density plot data (`x` and `y`) for each iteration of bootstrap resampling (`iter`). * `vars`: Character vector with names of the habitat variables for which to create UHC plots. * `type`: Named character vector with the type for each of `vars` (either `"numeric"` or `"factor"`). * `resp`: Character vector of length 1 with the name of the response variable. ## Details This function performs the heavy lifting of creating UHC plots. It creates the data used later by the `plot()` method, which actually draws the UHC plots. This function (1) creates density plots of the used and available locations from the **test** data, and (2) resamples the (a) fitted coefficients and (b) test data (weighted by the exponential habitat selection function) to create the distribution of used habitat under the model. Note that `test_dat` should contain at least all of the variables that appear in the model `object`. Any further habitat variables in `test_dat` will also have UHC plots generated, treating these variables as possible candidate variables that are simply not included in this particular model. ## Examples ```r # Load packages library(amt) library(dplyr) library(terra) library(sf) # HSF ---------------------------------------------- # Load data data(uhc_hsf_locs) data(uhc_hab) hab <- rast(uhc_hab, type = "xyz", crs = "epsg:32612") # Convert "cover" layer to factor levels(hab[[4]]) <- data.frame(id = 1:3, cover = c("grass", "forest", "wetland")) # Split into train (80%) and test (20%) set.seed(1) uhc_hsf_locs$train <- rbinom(n = nrow(uhc_hsf_locs), size = 1, prob = 0.8) train <- uhc_hsf_locs[uhc_hsf_locs$train == 1, ] test <- uhc_hsf_locs[uhc_hsf_locs$train == 0, ] # Available locations avail_train <- random_points(st_as_sf(st_as_sfc(st_bbox(hab))), n = nrow(train) * 10) avail_test <- random_points(st_as_sf(st_as_sfc(st_bbox(hab))), n = nrow(test) * 10) # Combine with used train_dat <- train |> make_track(x, y, crs = 32612) |> mutate(case_ = TRUE) |> bind_rows(avail_train) |> # Attach covariates extract_covariates(hab) |> # Assign large weights to available mutate(weight = case_when( case_ ~ 1, !case_ ~ 5000 )) test_dat <- test |> make_track(x, y, crs = 32612) |> mutate(case_ = TRUE) |> bind_rows(avail_test) |> # Attach covariates extract_covariates(hab) |> # Assign large weights to available mutate(weight = case_when( case_ ~ 1, !case_ ~ 5000 )) # Fit (correct) HSF hsf1 <- glm(case_ ~ forage + temp + I(temp^2) + pred + cover, data = train_dat, family = binomial(), weights = weight) # Drop weights from 'test_dat' test_dat$weight <- NULL # Prep UHC plots uhc_dat <- prep_uhc(object = hsf1, test_dat = test_dat, n_samp = 500, verbose = TRUE) # Plot all variables plot(uhc_dat) # Plot only first variable plot(uhc_dat[1]) # Plot only "cover" variable plot(uhc_dat["cover"]) # Coerce to data.frame df <- as.data.frame(uhc_dat) # Simplify sampled lines to confidence envelopes conf <- conf_envelope(df) # Default plot for the envelopes version plot(conf) ``` ## References Fieberg, J.R., Forester, J.D., Street, G.M., Johnson, D.H., ArchMiller, A.A., and Matthiopoulos, J. 2018. Used-habitat calibration plots: A new procedure for validating species distribution, resource selection, and step-selection models. **Ecography** 41:737–752. ## See Also See Fieberg **et al.** 2018 for details about UHC plots. Default plotting method available: `plot.uhc_data()` Coercion to `data.frame`: `as.data.frame.uhc_data()` Subsetting method: `Extract.uhc_data` ## Author(s) Brian J. Smith

prep_uhc function