Get the anomalies from a model

Usage

get_anomaly(analysis, ...)

# S4 method for class 'n2kInla'
get_anomaly(
  analysis,
  n = 20,
  expected_ratio = 5,
  expected_absent = 5,
  random_threshold = 1.05,
  verbose = TRUE,
  ...
)

# S4 method for class 'n2kModel'
get_anomaly(analysis, verbose = TRUE, ...)

Arguments

analysis: The model to add
...: Extra options. See details.
n: the number of anomalies per category.
expected_ratio: Observations that have observed / fitted > expected_ratio or fitted / observed > expected_ratio are potential anomalies. Defaults to 5, which implies that observed values that are 5 times higher or lower than the fitted values are potential anomalies.
expected_absent: Zero observations where fitted > expected_absent are potential anomalies.
random_threshold: The minimal relative effect size of a random effect. Random effect with a smaller effect size will never be an anomaly. Defaults to 1.05 (5%).
verbose: Print extra information on the screen