amt (version 0.1.2)

log_rss: Calculate log-RSS for a fitted model

Description

Calculate log-RSS(x1, x2) for a fitted RSF or (i)SSF

Usage

log_rss(object, ...)
# S3 method for fit_logit
log_rss(object, x1, x2, ...)
# S3 method for fit_clogit
log_rss(object, x1, x2, ...)

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().

Value

Returns a list of class log_rss.

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.

References

Avgar, T., Lele, S.R., Keim, J.L., & Boyce, M.S.. (2017). Relative Selection Strength: Quantifying effect size in habitat- and step-selection inference. Ecology and Evolution, 7, 5322<U+2013>5330.

See Also

See Avgar et al. 2017 for details about relative selection strength.

Default plotting method available: \link{plot.log_rss}()

Examples

# NOT RUN {
# Fit an RSF, then calculate log-RSS to visualize results.

# Load packages
library(ggplot2)

# Load data
data("amt_fisher")
data("amt_fisher_lu")

# Prepare data for RSF
rsf_data <- amt_fisher %>%
  filter(burst_ == 1) %>%
  make_track(x_, y_, t_) %>%
  random_points() %>%
  extract_covariates(amt_fisher_lu) %>%
  mutate(lu = factor(landuse_study_area))

# Fit RSF
m1 <- rsf_data %>%
  fit_rsf(case_ ~ lu)

# Calculate log-RSS
# data.frame of x1s
x1 <- data.frame(lu = sort(unique(rsf_data$lu)))
# data.frame of x2 (note factor levels should be same as model data)
x2 <- data.frame(lu = factor(21, levels = levels(rsf_data$lu)))
# Calculate
logRSS <- log_rss(object = m1, x1 = x1, x2 = x2)

# Plot
ggplot(logRSS$df, aes(x = lu_x1, y = log_rss)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "gray") +
  geom_point() +
  xlab(expression("Land Use Category " * (x[1]))) +
  ylab("log-RSS") +
  ggtitle(expression("log-RSS" * (x[1] * ", " * x[2]))) +
  theme_bw()

# Fit an SSF, then calculate log-RSS to visualize results.

 #Prepare data for SSF
ssf_data <- deer %>%
  steps_by_burst() %>%
  random_steps(n = 15) %>%
  extract_covariates(sh_forest) %>%
  mutate(forest = factor(sh.forest, levels = 1:2,
                    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)

# Plot
ggplot(logRSS$df, aes(x = forest_x1, y = log_rss)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "gray") +
  geom_point(size = 3) +
  xlab(expression("Forest Cover " * (x[1]))) +
  ylab("log-RSS") +
  ggtitle(expression("log-RSS" * (x[1] * ", " * x[2]))) +
  theme_bw()

# }