# Calculate log-RSS for a fitted model Calculate log-RSS(x1, x2) for a fitted RSF or (i)SSF ```r log_rss(object, ...) ## S3 method for class 'glm' log_rss(object, x1, x2, ci = NA, ci_level = 0.95, n_boot = 1000, ...) ## S3 method for class 'fit_clogit' log_rss(object, x1, x2, ci = NA, ci_level = 0.95, n_boot = 1000, ...) ``` ## Arguments - `object`: `[fit_logit, fit_clogit]` A fitted RSF or (i)SSF model. - `...`: Further arguments, none implemented. - `x1`: `[data.frame]` A `data.frame` representing the habitat values at location x_1. Must contain all fitted covariates as expected by `predict()`. - `x2`: `[data.frame]` A 1-row `data.frame` representing the single hypothetical location of x_2. Must contain all fitted covariates as expected by `predict()`. - `ci`: `[character]` Method for estimating confidence intervals around log-RSS. `NA` skips calculating CIs. Character string `"se"` uses standard error method and `"boot"` uses empirical bootstrap method. - `ci_level`: `[numeric]` Level for confidence interval. Defaults to 0.95 for a 95% confidence interval. - `n_boot`: `[integer]` Number of bootstrap samples to estimate confidence intervals. Ignored if `ci != "boot"`. ## Returns Returns a `list` of class `log_rss` with four entries: * `df`: A `data.frame` with the covariates and the `log_rss` * `x1`: A `data.frame` with covariate values for `x1`. * `x2`: A `data.frame` with covariate values for `x2`. * `formula`: The formula used to fit the model. ## Details This function assumes that the user would like to compare relative selection strengths from at least one proposed location (`x1`) to exactly one reference location (`x2`). The objects `object$model`, `x1`, and `x2` will be passed to `predict()`. Therefore, the columns of `x1` and `x2` must match the terms in the model formula exactly. ## Examples ```r # RSF ------------------------------------------------------- # Fit an RSF, then calculate log-RSS to visualize results. # Load packages library(ggplot2) # Load data data("amt_fisher") amt_fisher_covar <- get_amt_fisher_covars() # Prepare data for RSF rsf_data <- amt_fisher |> filter(name == "Lupe") |> make_track(x_, y_, t_) |> random_points() |> extract_covariates(amt_fisher_covar$elevation) |> extract_covariates(amt_fisher_covar$popden) |> extract_covariates(amt_fisher_covar$landuse) |> mutate(lu = factor(landuse)) # Fit RSF m1 <- rsf_data |> fit_rsf(case_ ~ lu + elevation + popden) # Calculate log-RSS # data.frame of x1s x1 <- data.frame(lu = factor(50, levels = levels(rsf_data$lu)), elevation = seq(90, 120, length.out = 100), popden = mean(rsf_data$popden)) # data.frame of x2 (note factor levels should be same as model data) x2 <- data.frame(lu = factor(50, levels = levels(rsf_data$lu)), elevation = mean(rsf_data$elevation), popden = mean(rsf_data$popden)) # Calculate (use se method for confidence interval) logRSS <- log_rss(object = m1, x1 = x1, x2 = x2, ci = "se") # Plot ggplot(logRSS$df, aes(x = elevation_x1, y = log_rss)) + geom_hline(yintercept = 0, linetype = "dashed", color = "gray") + geom_ribbon(aes(ymin = lwr, ymax = upr), fill = "gray80") + geom_line() + xlab(expression("Elevation " * (x[1]))) + ylab("log-RSS") + ggtitle(expression("log-RSS" * (x[1] * ", " * x[2]))) + theme_bw() # SSF ------------------------------------------------------- # Fit an SSF, then calculate log-RSS to visualize results. # Load data data(deer) sh_forest <- get_sh_forest() # Prepare data for SSF ssf_data <- deer |> steps_by_burst() |> random_steps(n = 15) |> extract_covariates(sh_forest) |> mutate(forest = factor(forest, levels = 1:0, labels = c("forest", "non-forest")), cos_ta = cos(ta_), log_sl = log(sl_)) # Fit an SSF (note model = TRUE necessary for predict() to work) m2 <- ssf_data |> fit_clogit(case_ ~ forest + strata(step_id_), model = TRUE) # Calculate log-RSS # data.frame of x1s x1 <- data.frame(forest = factor(c("forest", "non-forest"))) # data.frame of x2 x2 <- data.frame(forest = factor("forest", levels = levels(ssf_data$forest))) # Calculate logRSS <- log_rss(object = m2, x1 = x1, x2 = x2, ci = "se") # Plot ggplot(logRSS$df, aes(x = forest_x1, y = log_rss)) + geom_hline(yintercept = 0, linetype = "dashed", color = "gray") + geom_errorbar(aes(ymin = lwr, ymax = upr), width = 0.25) + geom_point(size = 3) + xlab(expression("Forest Cover " * (x[1]))) + ylab("log-RSS") + ggtitle(expression("log-RSS" * (x[1] * ", " * x[2]))) + theme_bw() ``` ## References * Avgar, T., Lele, S.R., Keim, J.L., and Boyce, M.S.. (2017). Relative Selection Strength: Quantifying effect size in habitat- and step-selection inference. **Ecology and Evolution**, 7, 5322–5330. * Fieberg, J., Signer, J., Smith, B., & Avgar, T. (2021). A "How to" guide for interpreting parameters in habitat-selection analyses. **Journal of Animal Ecology**, 90(5), 1027-1043. ## See Also See Avgar **et al.** 2017 for details about relative selection strength. Default plotting method available: `plot.log_rss()` ## Author(s) Brian J. Smith

log_rss function