check_factors(model, x1, x2)

## 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)

## End(Not run)