amt (version 0.1.4)

check_factors: Check factor levels

Description

Check factor levels before log-RSS calculation

Usage

check_factors(model, x1, x2)

Arguments

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().

object

[glm, clogit] The model object from a fitted RSF/(i)SSF. I.e., it will be object$model when called within log_rss().

Details

This function is meant for internal use by log_rss() and is not meant to be called by the user.

Examples

# NOT RUN {
# }
# NOT RUN {
#Load data
data("amt_fisher")

# 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 without factor
m1 <- rsf_data %>%
  fit_rsf(case_ ~ elevation + popden)

# data.frame of x1s
x1 <- data.frame(elevation = seq(90, 120, length.out = 100),
                 popden = mean(rsf_data$popden))
# data.frame of x2 (note factor levels should be same as model data)
x2 <- data.frame(elevation = mean(rsf_data$elevation),
                 popden = mean(rsf_data$popden))

# Function should return NULL (no factors to check)
check_factors(m1$model, x1, x2)

# Fit RSF with factor
m2 <- rsf_data %>%
  fit_rsf(case_ ~ lu + elevation + popden)

# 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))

# Function should return NULL (no discrepancies)
check_factors(m2, x1, x2)

# Now misspecify factor for x1
x1 <- data.frame(lu = factor(50),
                 elevation = seq(90, 120, length.out = 100),
                 popden = mean(rsf_data$popden))

# Function should return informative errors regarding data vs x1 and x1 vs x2
check_factors(m2$model, x1, x2)

# Also misspecify factor for x2
x2 <- data.frame(lu = factor(50),
                 elevation = mean(rsf_data$elevation),
                 popden = mean(rsf_data$popden))

# Function should return informative errors regarding data vs x1 and data vs x2
check_factors(m2$model, x1, x2)
# }
# NOT RUN {
# }